Fondazione Bruno Kessler - Technologies of Vision
contains material from
Template Matching Techniques in Computer Vision: Theory and Practice
Roberto Brunelli © 2009 John Wiley & Sons, Ltd
[1] KA Abdikalikov, VK Zadiraka, OS Kondratenko, and SS Mel’nikova. Fast algorithms for estimating the correlation functions of large signals. Cybernetics and Systems Analysis, 27:414–419, 1991.
[2] Y Abe and Y Iiguni. Interpolation capability of the periodic radial basis function
network. IEE Proceedings - Vision, Image and Signal Processing, 153:785–794, 2006.
http://dx.doi.org/10.1049/ip-vis:20050259.
[3] EEAA Abusham, D Ngo, and A Teoh. Comparing the performance of Principal Component Analysis and RBF network for face recognition using locally linear embedding. Int. J. of Computer Science and Network Security, 6(4):25–29, 2006.
[4] B Achermann and H Bunke. Classifying range images of human faces with the
Hausdorff distance. In Proc. of the 15th IAPR International Conference on Pattern
Recognition (ICPR’00), volume 2, pages 813–817, 2000.
http://dx.doi.org/10.1109/ICPR.2000.906199.
[5] N Aggarwal and WC Karl. Line detection in images through regularized Hough
transform. IEEE Trans. on Image processing, 15:582–591, 2006.
http://dx.doi.org/10.1109/TIP.2005.863021.
[6] AS Aguado, E Montiel, and MS Nixon. Invariant characterisation of the Hough
transform for pose estimation of arbitrary shapes. Pattern Recognition, 35:1083–1097,
2002.
http://dx.doi.org/10.1016/S0031-3203(01)00099-1.
[7] T Ahonen, A Hadid, and M Pietikainen. Face description with local binary patterns:
Application to face recognition. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 28:2037–2041, 2006.
http://dx.doi.org/10.1109/TPAMI.2006.244.
[8] H Ai, L Liang, and G Xu. Face detection based on template matching and support
vector machines. In Proc. of the International Conference on Image Processing (ICIP’01),
volume 1, pages 1006–1009, 2001.
http://dx.doi.org/10.1109/ICIP.2001.959218.
[9] M Akra, L Bazzi, and S Mitter. Sampling of images for efficient model-based vision.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 21:4–11, 1999.
http://dx.doi.org/10.1109/34.745729.
[10] JL Alba, A Pujol, A Lopez, and JJ Villanueva. Improving shape-based face
recognition by means of a supervised discriminant Hausdorff distance. In Proc. of the
International Conference on Image Processing (ICIP’03), volume 3, pages 901–904, 2003.
http://dx.doi.org/10.1109/ICIP.2003.1247391.
[11] VA Anisimov and ND Gorsky. Fast hierarchical matching of an arbitrarily oriented
template. Pattern Recognition Letters, 14:95–101, 1993.
http://dx.doi.org/10.1016/0167-8655(93)90082-O.
[12] G Antonini, V Popovici, and JP Thiran. Independent component analysis and support vector machine for face feature extraction. In Proc. of the 4th International Conference on Audio-and Video-Based Biometric Person Authentication, volume 2688 of Lecture Notes in Computer Science, pages 111–118. Springer, 2003.
[13] G Antonini, M Sorci, M Bierlaire, and JP Thiran. Discrete choice models for static
facial expression recognition. In Proc. of the 8th International Conference on Advanced
Concepts for Intelligent Vision Systems (ACIVS’06), volume 4179 of Lecture Notes in
Computer Science, pages 710–721. Springer, 2006.
http://dx.doi.org/10.1007/11864349_65.
[14] A Apodaca and L Gritz. Advanced RenderMan: Creating CGI for Motion Pictures. Morgan Kaufmann, 1999.
[15] V Argyriou and T Vlachos. Using gradient correlation for sub-pixel motion estimation
of video sequences. In Proc. of the IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP’04), volume 3, pages 329–332, 2004.
http://dx.doi.org/10.1109/ICASSP.2004.1326548.
[16] E Arias-Castro, DL Donoho, and X Huo. Near-optimal detection of geometric objects
by fast multiscale methods. IEEE Trans. on Information Theory, 51:2402–2425, 2005.
http://dx.doi.org/10.1109/TIT.2005.850056.
[17] JAR Artolazabal, J Illingworth, and AS Aguado. Light: Local invariant generalized
Hough transform. In Proc. of the 18th IAPR International Conference on Pattern
Recognition (ICPR’06), pages 304–307, 2006.
http://dx.doi.org/10.1109/ICPR.2006.763.
[18] MJ Atallah. Faster image template matching in the sum of the absolute value of
differences measure. IEEE Trans. on Image processing, 10:659–663, 2001.
http://dx.doi.org/10.1109/83.913600.
[19] R Bajcsy and S Kovačič. Multiresolution elastic matching. Computer Vision,
Graphics and Image Processing, 46:1–21, 1989.
http://dx.doi.org/10.1016/S0734-189X(89)80014-3.
[20] S Baker and SK Nayar. Algorithms for pattern rejection. In Proc. of the 13th IAPR
International Conference on Pattern Recognition (ICPR’96), volume 2, pages 869–874,
1996.
http://dx.doi.org/10.1109/ICPR.1996.547200.
[21] S Baker and SK Nayar. A theory of single-viewpoint catadioptric image formation.
Int. J. of Computer Vision, pages 175–196, 1999.
http://dx.doi.org/10.1023/A:1008128724364.
[22] S Baker and SK Nayar. Single viewpoint catadioptric cameras. In Panoramic Vision: Sensors, Theory and Applications, Monographs in Computer Science, pages 39–71. Springer, 2001.
[23] S Baker, SK Nayar, and H Murase. Parametric feature detection. Int. J. of Computer
Vision, 27:27–50, 1998.
http://dx.doi.org/10.1023/A:1007901712605.
[24] BJ Balas and P Sinha. Dissociated dipoles: Image representation via non-local comparisons. Technical Report CBCL-229, MIT Artificial Intelligence Laboratory, 2003.
[25] BJ Balas and P Sinha. Region-based representations for face recognition. ACM
Transactions on Applied Perception, 3:354–375, 2006.
http://dx.doi.org/http://doi.acm.org/10.1145/1190036.1190038.
[26] DH Ballard. Generalizing the Hough transform to detect arbitrary shapes. Pattern
Recognition, 13:111–122, 1981.
http://dx.doi.org/10.1016/0031-3203(81)90009-1.
[27] RV Baltz. Photons and Photon Statistics: from Incandescent Light to Lasers. In B di Bartolo and O Forte, editors, Frontiers of Optical Spectroscopy: Investigating Extreme Physical Conditions with Advanced Optical Techniques, 2005.
[28] GJF Banon and SD Faria. Morphological approach for template matching. In Proc.
of the 10th Brazilian Symposium on Computer Graphics and Image Processing, pages
171–178, 1997.
http://dx.doi.org/10.1109/SIGRA.1997.625169.
[29] M Barreno, AA Cardenas, and JD Tygar. Optimal ROC curve for a combination of classifiers. In Proc. of Advances in Neural Information Processing Systems, volume 20, pages 57–64, 2007.
[30] R Barrera, A Guzmán, A Ginich, and T Radhakrishnan. Design of a high-level language (L) for image processing. In Duff MJB and Levialdi S, editors, Languages and Architectures for Image Processing, pages 25–40. Academic Press, 1981.
[31] J Barron, D Fleet, and S Beauchemin. Performance of optical flow techniques. Int.
J. of Computer Vision, 12:43–77, 1994.
http://dx.doi.org/10.1007/BF01420984.
[32] MS Bartlett, G Littlewort, M Frank, C Lainscsek, IR Fasel, and JR Movellan.
Recognizing facial expression: Machine learning and application to spontaneous behavior.
In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’05), volume 2, pages 568–573, 2005.
http://dx.doi.org/10.1109/CVPR.2005.297.
[33] MS Bartlett, G Littlewort, M Frank, C Lainscsek, IR Fasel, and JR Movellan. Fully
automatic facial action recognition in spontaneous behavior. In Proc. of the 7th
International Conference on Automatic Face and Gesture Recognition (FG’06), pages
223–230, 2006.
http://dx.doi.org/10.1109/FGR.2006.55.
[34] MS Bartlett, G Littlewort, C Lainscsek, IR Fasel, and JR Movellan. Machine
learning methods for fully automatic recognition of facial expressions and facial actions. In
Proc. of the IEEE International Conference on Systems, Man, and Cybernetics, volume 1,
pages 592–597, 2004.
http://dx.doi.org/10.1109/ICSMC.2004.1398364.
[35] MS Bartlett, JR Movellan, and TJ Sejnowski. Face recognition by independent
component analysis. IEEE Trans. on Neural Networks, 13:1450–1464, 2002.
http://dx.doi.org/10.1109/TNN.2002.804287.
[36] MS Bartlett and TJ Sejnowski. Viewpoint invariant face recognition using independent component analysis and attractor networks. In Proc. of Advances in Neural Information Processing Systems, volume 9, pages 817–823, 1997.
[37] E Baudrier, G Millon, F Nicolier, and S Ruan. A new similarity measure using
Hausdorff distance map. In Proc. of the International Conference on Image Processing
(ICIP’04), volume 1, pages 669–672, 2004.
http://dx.doi.org/10.1109/ICIP.2004.1418843.
[38] E Baudrier, G Millon, F Nicolier, and S Ruan. A fast binary-image comparison
method with local-dissimilarity quantification. In Proc. of the 18th IAPR International
Conference on Pattern Recognition (ICPR’06), volume 3, pages 216–219, 2006.
http://dx.doi.org/10.1109/ICPR.2006.63.
[39] J Ben-Arie and KR Rao. A novel approach for template matching by non-orthogonal
image expansion. IEEE Trans. on Circuits and Systems for Video Technology, 3:71–84,
1993.
http://dx.doi.org/10.1109/76.180691.
[40] J Ben-Arie and KR Rao. On the recognition of occluded shapes and generic faces
using multiple-template expansion matching. In Proc. of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR’93), pages 214–219, 1993.
http://dx.doi.org/10.1109/CVPR.1993.340987.
[41] J Ben-Arie and KR Rao. Optimal template matching by nonorthogonal image
expansion using restoration. Machine Vision and Applications, 7:69–81, 1994.
http://dx.doi.org/10.1007/BF01215803.
[42] G Ben-Artzi, H Hel-Or, and Y Hel-Or. The gray-code filter kernels. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 29:382–393, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.62.
[43] M Ben-Ezra, A Zomet, and SK Nayar. Jitter camera: high resolution video from a
low resolution detector. In Proc. of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR’04), volume 2, pages 135–142, 2004.
http://dx.doi.org/10.1109/CVPR.2004.1315155.
[44] M Ben-Ezra, A Zomet, and SK Nayar. Video super-resolution using controlled
subpixel detector shifts. IEEE Trans. on Pattern Analysis and Machine Intelligence,
27:977–987, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.129.
[45] DE Benn, MS Nixon, and JN Carter. Robust eye centre extraction using the Hough transform. In Proc. of the 1st International Conference on Audio-and Video-Based Biometric Person Authentication, pages 3–9, 1997.
[46] KP Bennett and EJ Bredensteiner. Duality and geometry in SVM classifiers. In Proc. of the 17th International Conference on Machine Learning (ICML’00), pages 57–64, 2000.
[47] AC Berg, TL Berg, and J Malik. Shape matching and object recognition using low
distortion correspondences. In Proc. of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR’05), volume 1, pages 26–33, 2005.
http://dx.doi.org/10.1109/CVPR.2005.320.
[48] AC Berg and J Malik. Geometric blur for template matching. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’01), volume 1, pages
607–614, 2001.
http://dx.doi.org/10.1109/CVPR.2001.990529.
[49] M Berger and G Danuser. Deformable multi template matching with application
to portal images. In Proc. of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR’97), pages 374–379, 1997.
http://dx.doi.org/10.1109/CVPR.1997.609352.
[50] M Bertalmio, G Sapiro, and G Randall. Morphing active contours. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 22:733–737, 2000.
http://dx.doi.org/10.1109/34.865191.
[51] F Bertamini, R Brunelli, O Lanz, A Roat, A Santuari, F Tobia, and Q Xu.
Olympus: an ambient intelligence architecture on the verge of reality. In ICIAP, pages
139–144, 2003.
http://dx.doi.org/10.1109/ICIAP.2003.1234040.
[52] F. Bertamini, Roberto Brunelli, O. Lanz, A. Roat, A. Santuari, F. Tobia, and Qing
Xu. Olympus: an ambient intelligence architecture on the verge of reality. In ICIAP,
pages 139–144, 2003.
http://dx.doi.org/10.1109/ICIAP.2003.1234040.
[53] M Betke and NC Makris. Fast object recognition in noisy images using simulated
annealing. In Proc. of the 5th International Conference on Computer Vision and Pattern
Recognition (ICCV’95), pages 523–530, 1995.
http://dx.doi.org/10.1109/ICCV.1995.466895.
[54] D Beymer and T Poggio. Image representation for visual learning. Science, 272:1905–1909, 1996.
[55] D Beymer, A Shashua, and T Poggio. Example based image analysis and synthesis. Technical Report AIM-1431; CBCL-080, MIT, 1993.
[56] DN Bhat. An evolutionary measure for image matching. In Proc. of the 14th IAPR
International Conference on Pattern Recognition (ICPR’98), volume 1, pages 850–852,
1998.
http://dx.doi.org/10.1109/ICPR.1998.711283.
[57] DN Bhat and SK Nayar. Ordinal measures for image correspondence. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 20:415–423, 1998.
http://dx.doi.org/10.1109/34.677275.
[58] P Bhattacharya, A Rosenfeld, and I Weiss. Geometric and algebraic properties of
point-to-line mappings. Pattern Recognition, 36:483–503, 2003.
http://dx.doi.org/10.1016/S0031-3203(02)00079-1.
[59] W Bialek. Physical limits to sensation and perception. Annual Review of Biophysics
and Biophysical Chemistry, 16:455–478, 1987.
http://dx.doi.org/10.1146/annurev.bb.16.060187.002323.
[60] SM Bileschi and B Heisele. Advances in component based face detection. In Proc.
of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
(AMFG’03), 2003.
http://dx.doi.org/10.1109/AMFG.2003.1240837.
[61] G Birkhoff. The role of algebra in computing. In SIAM-AMS Proc. of Computers in Algebra and Number Theory, volume 4, pages 1–48, 1971.
[62] CM Bishop. Bayesian PCA. In Proc. of Advances in Neural Information Processing Systems, volume 11, pages 382–388, 1999.
[63] CM Bishop. Pattern Recognition and Machine Learning. Springer, 2007.
[64] DM Blackburn. Evaluating technology properly: Three easy steps to success. Corrections Today, 63:56–60, 2001.
[65] V Blanz and T Vetter. Face recognition based on fitting a 3D morphable model.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 25:1063–1074, 2003.
http://dx.doi.org/10.1109/TPAMI.2003.1227983.
[66] R Bogush, S Maltsev, S Ablameyko, S Uchida, and S Kamata. An efficient
correlation computation method for binary images based on matrix factorisation. In
Proc. of the 6th International Conference on Document Analysis and Recognition, pages
312–316, 2001.
http://dx.doi.org/10.1109/ICDAR.2001.953805.
[67] G Bonmassar and EL Schwartz. Space-variant Fourier analysis: the exponential chirp
transform. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19:1080–1089,
1997.
http://dx.doi.org/10.1109/34.625108.
[68] G Bonmassar and EL Schwartz. Improved cross-correlation for template matching
on the Laplacian pyramid. Pattern Recognition Letters, 19:765–770, 1998.
http://dx.doi.org/10.1016/S0167-8655(98)00056-7.
[69] N Bonnet. An unsupervised generalized Hough transform for natural shapes. Pattern
Recognition, 35:1193–1196, 2002.
http://dx.doi.org/10.1016/S0031-3203(01)00219-9.
[70] G Borgefors. Hierarchical chamfer matching: A parametric edge matching algorithm.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 10:849–865, 1988.
http://dx.doi.org/10.1109/34.9107.
[71] T Bossomaier and A Loeff. Parallel computation of the Hausdorff distance between
images. Parallel Computing, 19:1129–1140, 1993.
http://dx.doi.org/10.1016/0167-8191(93)90022-D.
[72] T Boult. Robust distance measures for face-recognition supporting revocable
biometric tokens. In Proc. of the 7th International Conference on Automatic Face and
Gesture Recognition (FG’06), pages 560–566, 2006.
http://dx.doi.org/10.1109/FGR.2006.94.
[73] TM Breuel. Fast recognition using adaptive subdivisions of transformation space. In
Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’92),
pages 445–451, 1992.
http://dx.doi.org/10.1109/CVPR.1992.223152.
[74] TM Breuel. Finding lines under bounded error. Pattern Recognition, 29:167–178, 1996.
[75] M Bro-Nielsen and C Gramkow. Fast fluid registration of medical images. In Proc. of the 4th International Conference on Visualization in Biomedical Computing, volume 1131 of Lecture Notes in Computer Science, pages 267–276. Springer, 1996.
[76] AM Bronstein, MM Bronstein, and R Kimmel. Three-dimensional face recognition.
Int. J. of Computer Vision, 64:5–30, 2005.
http://dx.doi.org/10.1007/s11263-005-1085-y.
[77] R. Brunelli. Training neural nets through stochastic minimization. Neural Networks,
7(9):1405–1412, 1994.
http://dx.doi.org/10.1016/0893-6080(94)90088-4.
[78] R Brunelli. Estimation of pose and illuminant direction for face processing. Image
and Vision Computing, 15:741–748, 1997.
http://dx.doi.org/10.1016/S0262-8856(97)00024-3.
[79] R Brunelli and D Falavigna. Person identification using multiple cues. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 17:955–966, 1995.
http://dx.doi.org/10.1109/34.464560.
[80] R Brunelli and S Messelodi. Robust estimation of correlation with applications to
computer vision. Pattern Recognition, 28:833–841, 1995.
http://dx.doi.org/10.1016/0031-3203(94)00170-Q.
[81] R. Brunelli and O. Mich. SpotIt! an Interactive Identikit System. Graphical Models
and Image Processing, 58:399–404, 1996.
http://dx.doi.org/10.1006/gmip.1996.0033.
[82] R Brunelli and O Mich. Image retrieval by examples. IEEE Trans. on Multimedia,
2:164–171, 2000.
http://dx.doi.org/10.1109/6046.865481.
[83] R Brunelli and O Mich. Histograms analysis for image retrieval. Pattern Recognition,
34:1625–1637, 2001.
http://dx.doi.org/10.1016/S0031-3203(00)00054-6.
[84] R Brunelli and CM Modena. ANIMAL: AN IMage ALgebra. High Frequency, LVIII:255–259, 1989.
[85] R Brunelli and T Poggio. Caricatural effects in automated face perception. Biological
Cybernetics, 69(3):235–241, 1993.
http://dx.doi.org/10.1007/BF00198964.
[86] R Brunelli and T Poggio. Face recognition: Features versus templates. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 15:1042–1052, 1993.
http://dx.doi.org/10.1109/34.254061.
[87] R Brunelli and T Poggio. Template matching: Matched spatial filters and beyond.
Pattern Recognition, 30:751–768, 1997.
http://dx.doi.org/10.1016/S0031-3203(96)00104-5.
[88] R. Brunelli and G.P. Tecchiolli. Stochastic minimization with adaptive memory.
Journal of Computational and Applied Mathematics, 57:329–343, 1995.
http://dx.doi.org/10.1016/0377-0427(93)E0203-X.
[89] JB Buckheit and DL Donoho. Wavelab and reproducible research. Technical report, Department of Statistics, Stanford University, 1995.
[90] CJC Burges. A tutorial on support vector machines for pattern recognition. Data
Mining and Knowledge Discovery, 2:121–167, 1998.
http://dx.doi.org/10.1023/A:1009715923555.
[91] TM Caelli and ZQ Liu. On the minimum number of templates required for shift,
rotation and size invariant pattern recognition. Pattern Recognition, 21:205–216, 1988.
http://dx.doi.org/10.1016/0031-3203(88)90055-6.
[92] F Cao and P Bouthemy. A general criterion for image similarity. Technical Report RR-5620, INRIA, 2005.
[93] R Cappelli, D Maio, and D Maltoni. Multispace KL for pattern representation and
classification. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23:977–996,
2001.
http://dx.doi.org/10.1109/34.955111.
[94] DE Cardoze and LJ Schulman. Pattern matching for spatial point sets. In Proc. of
the 39th Annual Symposium on Foundations of Computer Science, pages 156–165, 1998.
http://dx.doi.org/10.1109/SFCS.1998.743439.
[95] VJ Carey. Literate statistical programming: Concepts and tools. Chance, 14:46–50, 2001.
[96] D Casasent. Unified synthetic discriminant function computational formulation. Applied Optics, 23:1620–1627, 1984.
[97] D Casasent and W-T Chang. Correlation synthetic discriminant functions. Applied Optics, 25:2343–2350, 1986.
[98] D Casasent and G Ravichandran. Advanced distortion-invariant minimum average correlation energy (MACE) filters. Applied Optics, 31:1109–1116, 1992.
[99] D Casasent, G Ravichandran, and S Bollapragada. Gaussian-minimum average correlation energy filters. Applied Optics, 30:5176–5181, 1991.
[100] V Caselles, R Kimmel, and G Sapiro. Geodesic active contours. Int. J. of Computer
Vision, 22:61–79, 1997.
http://dx.doi.org/10.1109/ICCV.1995.466871.
[101] JM Chambers. Programming with Data. Springer, 1998.
[102] S Chambon and A Crouzil. Dense matching using correlation: new measures that are robust near occlusions. In Proc. of the British Machine Vision Conference (BMVC’03), volume 1, pages 143–152, 2003.
[103] S Chambon and A Crouzil. Color stereo matching using correlation measures. In Proc. of Complex Systems Intelligence and Modern Technological Applications (CSIMTA 2004), pages 520–525, 2004.
[104] S Chambon and A Crouzil. Towards correlation-based matching algorithms that are
robust near occlusions. In Proc. of the 17th IAPR International Conference on Pattern
Recognition (ICPR’04), volume 3, pages 20–23, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334459.
[105] TF Chan and LA Vese. Active contours without edges. IEEE Trans. on Image
processing, 10:266–277, 2001.
http://dx.doi.org/10.1109/83.902291.
[106] C-C Chang and C-J Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
[107] C-Y Chang, AA Maciejewski, and V Balakrishnan. Fast eigenspace decomposition of
correlated images. IEEE Trans. on Image processing, 9:1937–1949, 2000.
http://dx.doi.org/10.1109/83.877214.
[108] G Charpiat, O Faugeras, and R Keriven. Image statistics based on diffeomorphic
matching. In Proc. of the 10th International Conference on Computer Vision and Pattern
Recognition (ICCV’05), volume 1, pages 852–857, 2005.
http://dx.doi.org/10.1109/ICCV.2005.118.
[109] G Charpiat, O Faugeras, R Keriven, and P Maurel. Distance-based shape statistics.
In Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP’06), volume 5, pages 925–928, 2006.
http://dx.doi.org/10.1109/ICASSP.2006.1661428.
[110] G Charpiat, OD Faugeras, and R Keriven. Shape metrics, warping and statistics. In
Proc. of the International Conference on Image Processing (ICIP’03), volume 2, pages
627–630, 2003.
http://dx.doi.org/10.1109/ICIP.2003.1246758.
[111] FA Cheikh, B Cramariuc, M Partio, P Reijonen, and M Gabbouj. Evaluation of
shape correspondence using ordinal measures. In Proc. of SPIE, volume 4676, pages 22–30,
2001.
http://dx.doi.org/10.1117/12.451097.
[112] FA Cheikh, B Cramariuc, M Partio, P Reijonen, and M Gabbouj. Ordinal-measure
based shape correspondence. EURASIP Journal on Applied Signal Processing,
2002:362–371, 2002.
http://dx.doi.org/10.1155/S111086570200077X.
[113] H Chen. Principal component analysis with missing data and outliers. Technical report, Rutgers University, 2002.
[114] J-H Chen. M-estimator based robust kernels for support vector machines. In Proc.
of the 17th IAPR International Conference on Pattern Recognition (ICPR’04), volume 1,
pages 168–171, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334039.
[115] J-H Chen, C-S Chen, and Y-S Chen. Fast algorithm for robust template matching
with M-estimators. IEEE Trans. on Signal Processing, 51:230–243, 2003.
http://dx.doi.org/10.1109/TSP.2002.806551.
[116] C Chennubhotla and A Jepson. Sparse PCA. extracting multi-scale structure from
data. In Proc. of the 8th International Conference on Computer Vision and Pattern
Recognition (ICCV’01), volume 1, pages 641–647, 2001.
http://dx.doi.org/10.1109/ICCV.2001.937579.
[117] KH Cheung, A Kong You, J, Q Li, D Zhang, and P Bhattacharya. A new approach
to appearance-based face recognition. In Proc. of the IEEE International Conference on
Systems, Man, and Cybernetics, volume 2, pages 1686–1691, 2005.
http://dx.doi.org/10.1109/ICSMC.2005.1571391.
[118] KW Cheung, DY Yeung, and RT Chin. A Bayesian framework for deformable
pattern recognition withapplication to handwritten character recognition. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 20:1382–1388, 1998.
http://dx.doi.org/10.1109/34.735813.
[119] KW Cheung, DY Yeung, and RT Chin. Bidirectional deformable matching with
application to handwritten character extraction. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 24:1133–1139, 2002.
http://dx.doi.org/10.1109/TPAMI.2002.1024135.
[120] MS Choi and WY Kim. A novel two stage template matching method for rotation
and illumination invariance. Pattern Recognition, 35:119–129, 2002.
http://dx.doi.org/10.1016/S0031-3203(01)00025-5.
[121] WP Choi, KM Lam, and WC Siu. Robust Hausdorff distance for shape matching. In
Visual Communications and Image Processing 2002, volume 4671 of Proceedings of SPIE,
pages 793–804, 2002.
http://dx.doi.org/10.1117/12.453123.
[122] GE Christensen. Consistent linear-elastic transformations for image matching. In Proceedings of the 16th International Conference on Information Processing in Medical Imaging (IPMI’99), volume 1613 of Lecture Notes in Computer Science, pages 224–237, 1999.
[123] GE Christensen and HJ Johnson. Consistent image registration. IEEE Trans. on
Medical Imaging, 20:568–582, 2001.
http://dx.doi.org/10.1109/42.932742.
[124] GE Christensen, SC Joshi, and MI Miller. Volumetric transformation of brain
anatomy. IEEE Trans. on Medical Imaging, 16:864–877, 1997.
http://dx.doi.org/10.1109/42.650882.
[125] GE Christensen, RD Rabbitt, and MI Miller. Deformable templates using large
deformation kinematics. IEEE Trans. on Image processing, 5:1435–1447, 1996.
http://dx.doi.org/10.1109/83.536892.
[126] GE Christensen, P Yin, MW Vannier, KSC Chao, JF Dempsey, and JF Williamson.
Large-deformation image registration using fluid landmarks. In Proc. of the 4th IEEE
Southwest Symposium on Image Analysis and Interpretation (SSIAI 2000), pages 269–273,
2000.
http://dx.doi.org/10.1109/IAI.2000.839614.
[127] J Cohn and T Kanade. Use of automated facial image analysis for measurement of emotion expression. In JA Coan and JB Allen, editors, The handbook of emotion elicitation and assessment. Oxford University Press, 2006.
[128] TF Cootes, A Hill, CJ Taylor, and J Haslam. The use of active shape models for locating structures in medical images. Image and Vision Computing, 12:355–366, 1994.
[129] TF Cootes, CJ Taylor, DH Cooper, and J Graham. Active shape models: Their
training and application. Computer Vision and Image Understanding, 61:38–59, 1995.
http://dx.doi.org/10.1006/cviu.1995.1004.
[130] S Copt and MP Victoria-Feser. Fast algorithms for computing high breakdown covariance matrices with missing data. In Theory and Applications of Recent Robust Methods, pages 71–82. Birkhauser, 2004.
[131] J Coughlan, A Yuille, C English, and D Snow. Efficient deformable template
detection and localization without user initialization. Computer Vision and Image
Understanding, 78:303–319, 2000.
http://dx.doi.org/10.1006/cviu.2000.0842.
[132] JR Cowell and A Ayesh. Extracting subtle facial expression for emotional analysis. In
Proc. of the IEEE International Conference on Systems, Man, and Cybernetics, volume 1,
pages 677–681, 2004.
http://dx.doi.org/10.1109/ICSMC.2004.1398378.
[133] DL Cowles, JR Van Dolson, LR Hainey, and DM Dick. The use of different eye
regions in the mantis shrimp hemisquilla californiensis stephenson, 1967 (crustacea:
Stomatopoda) for detecting objects. J. of Experimental Marine Biology and Ecology,
2:528–534, 2006.
http://dx.doi.org/10.1016/j.jembe.2005.09.016.
[134] MJ Crawley. Statistics: An Introduction using R. Wiley, 2005.
[135] TW Cronin and J Marshall. Parallel processing and image analysis in the eyes of mantis shrimps. Biological Bulletin, 200:177–183, 2001.
[136] C Croux and C Dehon. Robustness versus efficiency for nonparametric correlation measures. ECARES Working Papers 2008_002, Université Libre de Bruxelles, Ecares, 2008.
[137] A Crouzil, L Massip-Pailhes, and S Castan. A new correlation criterion based on
gradient fields similarity. In Proc. of the 13th IAPR International Conference on Pattern
Recognition (ICPR’96), volume 1, pages 632–636, 1996.
http://dx.doi.org/10.1109/ICPR.1996.546101.
[138] R Cucchiara and F Filicori. The vector-gradient Hough transform for identifying
straight-translation generated shapes. In Proc. of the 13th IAPR International Conference
on Pattern Recognition (ICPR’96), volume 2, pages 502–510, 1996.
http://dx.doi.org/10.1109/ICPR.1996.546876.
[139] F Cucker and S Smale. Best choices for regularization parameters in learning theory: On the bias-variance problem. Foundations of Computational Mathematics, 2:413–428, 2002.
[140] B Cyganek. Computational framework for family of order statistic filters for tensor
valued data. In Proc. of the International Conference on Image Analysis and Recognition,
volume 1, pages 156–162, 2006.
http://dx.doi.org/10.1007/11867586.
[141] B Cyganek. Matching of the multi-channel images with improved nonparametric
transformations and weighted binary distance measures. In Proc. of the 11th International
Workshop on Combinatorial Image Analysis (IWCIA 2006), volume 4040 of Lecture Notes
in Computer Science, pages 74–88. Springer, 2006.
http://dx.doi.org/10.1007/11774938_7.
[142] B Cyganek and J Borgosz. Fuzzy nonparametric measures for image matching.
In Proc. of the 7th International Conference Artificial Intelligence and Soft Computing
(ICAISC 2004), volume 3070 of Lecture Notes in Computer Science, pages 712–717.
Springer, 2004.
http://dx.doi.org/10.1007/b98109.
[143] A Daffertshofer and H Haken. A new approach to recognition of deformed patterns.
Pattern Recognition, 27:1697–1705, 1994.
http://dx.doi.org/10.1016/0031-3203(94)90087-6.
[144] T Darrell. A radial cumulative similarity transform for robust image correspondence.
In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’98), pages 656–662, 1998.
http://dx.doi.org/10.1109/CVPR.1998.698674.
[145] T Darrell and M Covell. Correspondence with cumulative similiarity transforms.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 23:222–227, 2001.
http://dx.doi.org/10.1109/34.908973.
[146] ER Davies. Effect of foreground and background occlusion on feature matching for
target location. Electronic Letters, 35:887–889, 1999.
http://dx.doi.org/10.1049/el:19990648.
[147] PL Davies and U Gather. Breakdown and groups (with rejoinder). Annals of
Statistics, 33:977–1035, 2005.
http://dx.doi.org/10.1214/009053604000001138.
[148] J Davis and M Goadrich. The relationship between precision-recall and ROC curves.
In Proc. of the International Conference on Machine Learning (ICML’06), pages 233–240,
2006.
http://dx.doi.org/10.1145/1143844.1143874.
[149] F De la Torre and MJ Black. Dynamic coupled component analysis. In Proc. of the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR’01), volume 2,
pages 643–650, 2001.
http://dx.doi.org/10.1109/CVPR.2001.991024.
[150] F De la Torre and MJ Black. Robust principal component analysis for computer
vision. In Proc. of the 8th International Conference on Computer Vision and Pattern
Recognition (ICCV’01), volume 1, pages 362–369, 2001.
http://dx.doi.org/10.1109/ICCV.2001.937541.
[151] F De la Torre and MJ Black. A framework for robust subspace learning. Int. J. of
Computer Vision, 54:117–142, 2003.
http://dx.doi.org/10.1023/A:1023709501986.
[152] F De la Torre and MJ Black. Robust parameterized component analysis: theory and
applications to 2D facial appearance models. Computer Vision and Image Understanding,
91:53–71, 2003.
http://dx.doi.org/10.1016/S1077-3142(03)00076-6.
[153] J de Leeuw. Reproducible research. The bottom line. Statistics Series 301, Department of Statistics, UCLA, 1996.
[154] C De Stefano, F Tortorella, and M Vento. An entropy-based method for extracting
robust binary templates. Machine Vision and Applications, 8:173–178, 1995.
http://dx.doi.org/10.1007/BF01215812.
[155] SR Deans. Hough transform from the Radon transform. IEEE Trans. on Pattern Analysis and Machine Intelligence, 3:185–188, 1981.
[156] M Deering. A photon accurate model of the human eye. ACM Transactions on
Graphics, 24:649–658, 2005.
http://dx.doi.org/10.1145/1073204.1073243.
[157] A Del Bimbo and P Pala. Visual image retrieval by elastic matching of user sketches.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 19:121–132, 1997.
http://dx.doi.org/10.1109/34.574790.
[158] K Delac, M Grgic, and S Grgic. Independent comparative study of PCA, ICA, and
LDA on the FERET data set. International Journal of Imaging Systems and Technology,
15:252–260, 2005.
http://dx.doi.org/10.1002/ima.20059.
[159] K Delac, M Grgic, and S Grgic. Independent Comparative Study of PCA, ICA, and
LDA on the FERET Data Set. Int J Imaging Syst Technol, 15:252–260, 2006.
http://dx.doi.org/10.1002/ima.20059.
[160] S Derrode and F Ghorbel. Robust and efficient fourier-mellin transform
approximations for gray-level image reconstruction and complete invariant description.
Computer Vision and Image Understanding, 83:57–78, 2001.
http://dx.doi.org/10.1006/cviu.2001.0922.
[161] S Derrode and F Ghorbel. Robust and efficient Fourier-Mellin transform
approximations for gray-level image reconstruction and complete invariant description.
Computer Vision and Image Understanding, 83:57–78, 2001.
http://dx.doi.org/10.1006/cviu.2001.0922.
[162] L Di Stefano and S Mattoccia. Fast template matching using bounded partial
correlation. Machine Vision and Applications, 13:213–221, 2003.
http://dx.doi.org/10.1007/s00138-002-0070-5.
[163] L Di Stefano and S Mattoccia. A sufficient condition based on the Cauchy-Schwarz
inequality for efficient template matching. In Proc. of the International Conference on
Image Processing (ICIP’03), volume 1, pages 269–272, 2003.
http://dx.doi.org/10.1109/ICIP.2003.1246950.
[164] L Di Stefano, S Mattoccia, and M Mola. An efficient algorithm for exhaustive
template matching based on normalized cross correlation. In Proc. of the 12th
International Conference on Image Analysis and Processing, pages 322–327, 2003.
http://dx.doi.org/10.1109/ICIAP.2003.1234070.
[165] L Di Stefano, S Mattoccia, and F Tombari. Speeding-up NCC-based template
matching using parallel multimedia instructions. In Proc. of the 7th International
Workshop on Computer Architecture for Machine Perception, pages 193–197, 2005.
http://dx.doi.org/10.1109/CAMP.2005.49.
[166] L Di Stefano, S Mattoccia, and F Tombari. ZNCC-based template matching using
bounded partial correlation. Pattern Recognition Letters, 26:2129–2134, 2005.
http://dx.doi.org/10.1016/j.patrec.2005.03.022.
[167] L Ding, AA Goshtasby, and M Satter. Volume image registration by template
matching. Image and Vision Computing, 19:821–832, 2001.
http://dx.doi.org/10.1016/S0262-8856(00)00101-3.
[168] M Dobes, J Martinek, D Skoupil, Z Dobesova, and J Pospisil. Human eye
localization using the modified Hough transform. Optik - International Journal for Light
and Electron Optics, 117:468–473, 2006.
http://dx.doi.org/10.1016/j.ijleo.2005.11.008.
[169] G Donato, MS Bartlett, JC Hager, P Ekman, and TJ Sejnowski. Classifying facial
actions. IEEE Trans. on Pattern Analysis and Machine Intelligence, 21:974–989, 1999.
http://dx.doi.org/10.1109/34.799905.
[170] C Donner and HW Jensen. A spectral BSSRDF for shading human skin. In Proc. of the Eurographics Symposium on Rendering, pages 409–417, 2006.
[171] NDH Dowson and R Bowden. A unifying framework for mutual information methods
for use in non-linear optimisation. In Proc. of the 9th European Conference on Computer
Vision (ECCV’06), volume 1, pages 365–378, 2006.
http://dx.doi.org/10.1007/11744023.
[172] NDH Dowson, R Bowden, and T Kadir. Image template matching using mutual
information and NP-windows. In Proc. of the 18th IAPR International Conference on
Pattern Recognition (ICPR’06), volume 2, pages 1186–1191, 2006.
http://dx.doi.org/10.1109/ICPR.2006.691.
[173] MP Dubuisson and AK Jain. A modified Hausdorff distance for object matching.
In Proc. of the 12th IAPR International Conference on Pattern Recognition (ICPR’94),
volume 1, pages 566–568, 1994.
http://dx.doi.org/10.1109/ICPR.1994.576361.
[174] CE Duchon. Lanczos filtering in one and two dimensions. Journal of Applied
Meteorology, 18:1016–1022, 1979.
http://dx.doi.org/10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.
[175] RO Duda and PE Hart. Use of the Hough transform to detect lines and curves in
pictures. Communications of the ACM, 15:11–15, 1972.
http://dx.doi.org/http://doi.acm.org/10.1145/361237.361242.
[176] RO Duda, PE Hart, and DG Stork. Pattern Classification. Wiley, 2nd edition, 2000.
[177] RM Dufour, EL Miller, and NP Galatsanos. Impulse restoration template matching
under geometric uncertainties. In Proc. of the International Conference on Image
Processing (ICIP’00), volume 2, pages 950–953, 2000.
http://dx.doi.org/10.1109/ICIP.2000.899874.
[178] RM Dufour, EL Miller, and NP Galatsanos. Template matching based object
recognition with unknown geometric parameters. IEEE Trans. on Image processing,
11:1385–1396, 2002.
http://dx.doi.org/10.1109/TIP.2002.806245.
[179] JH Dukesherer and CE Smith. A hybrid Hough-Hausdorff method for recognizing
bicycles in natural scenes. In Proc. of the IEEE International Conference on Systems,
Man, and Cybernetics, volume 4, pages 2493–2498, 2001.
http://dx.doi.org/10.1109/ICSMC.2001.972932.
[180] O Ecabert and JP Thiran. Adaptive Hough transform for the detection of natural
shapes under weak affine transformations. Pattern Recognition Letters, 25:1411–1419,
2004.
http://dx.doi.org/10.1016/j.patrec.2004.05.009.
[181] JL Edwards and H Murase. Coarse-to-fine adaptive masks for appearance matching
of occluded scenes. Machine Vision and Applications, 10:232–242, 1998.
http://dx.doi.org/10.1007/s001380050075.
[182] B Efron and R Tibshirani. Improvements on cross-validation: The .632+ bootstrap method. J. of the American Statistical Association, 92:548–560, 1997.
[183] HK Ekenel and B Sankur. Feature selection in the independent component subspace
for face recognition. Pattern Recognition Letters, 25:1377–1388, 2004.
http://dx.doi.org/10.1016/j.patrec.2004.05.013.
[184] MP Eklund, AA Farag, and MT El-Melegy. Robust correspondence methods for
stereo vision. Int. J. of Pattern Recognition and Artificial Intelligence, 17:1059–1079,
2003.
http://dx.doi.org/10.1142/S0218001403002861.
[185] P Ekman and WV Friesen. Facial Action Coding System. Consulting Psychologists Press Inc., Palo Alto, Calif., 1978.
[186] HM El-Bakry and Q Zhao. Fast pattern detection using normalized neural networks
and cross-correlation in the frequency domain. EURASIP Journal on Applied Signal
Processing, 2005:2054–2060, 2005.
http://dx.doi.org/10.1155/ASP.2005.2054.
[187] A El Gamal and H Eltoukhy. CMOS image sensors. IEEE Circuits and Devices Magazine, 21:6–20, 2005.
[188] M Elad, Y Hel-Or, and R Keshet. Pattern detection using a maximal rejection classifier. In Proc. of the 4th International Workshop on Visual Form, volume 2059 of Lecture Notes in Computer Science, pages 514–524. Springer, 2001.
[189] M Elad, Y Hel-Or, and R Keshet. Rejection based classifier for face detection.
Pattern Recognition Letters, 23:1459–1471, 2002.
http://dx.doi.org/10.1016/S0167-8655(02)00106-X.
[190] JH Elder. Are edges incomplete? Int. J. of Computer Vision, 34:97–122, 1999.
http://dx.doi.org/10.1023/A:1008183703117.
[191] JH Elder and SW Zucker. Local scale control for edge detection and blur estimation.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 20:699–716, 1998.
http://dx.doi.org/10.1109/34.689301.
[192] NJ Emery. The eyes have it: the neuroethology, function and evolution of social gaze.
Neuroscience and Biobehavioral Reviews, 24:581–604, 2000.
http://dx.doi.org/10.1016/S0149-7634(00)00025-7.
[193] R Epstein and A Yuille. Training a general purpose deformable template. In Proc. of
the International Conference on Image Processing (ICIP’94), volume 1, pages 203–207,
1994.
http://dx.doi.org/10.1109/ICIP.1994.413304.
[194] IA Essa and A Pentland. Coding, analysis, interpretation, and recognition of facial
expressions. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19:757–763,
1997.
http://dx.doi.org/10.1109/34.598232.
[195] L Essannouni, E Ibn-Elhaj, and D Aboutajdine. Fast cross-spectral image
registration using new robust correlation. J. of Real-Time Image Processing, 1:123–129,
2006.
http://dx.doi.org/10.1007/s11554-006-0016-7.
[196] AN Evans. On the use of ordinal measures for cloud tracking. Int. J. of Remote
Sensing, 21:1939–1944, 2000.
http://dx.doi.org/10.1080/014311600209850.
[197] BS Everitt and T Hothorn. A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC, 2006.
[198] RM Everson and L Sirovich. The Karhunen-Loeve transform for incomplete data. J. of the Optical Society of America A, 12:1657–1664, 1995.
[199] T Evgeniou, M Pontil, and T Poggio. Regularization networks and support vector
machines. Advances in Computational Mathematics, 13:1–50, 2000.
http://dx.doi.org/10.1023/A:1018946025316.
[200] H Faraji and WJ MacLean. CCD noise removal in digital images. IEEE Trans. on
Image processing, 15:2676–26865, 2006a.
http://dx.doi.org/10.1109/TIP.2006.877363.
[201] T Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 27:861–874,
2006.
http://dx.doi.org/10.1016/j.patrec.2005.10.010.
[202] PF Felzenszwalb. Learning models for object recognition. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’01), volume 1, pages
1056–1062, 2001.
http://dx.doi.org/10.1109/CVPR.2001.990647.
[203] PF Felzenszwalb. Representation and detection of deformable shapes. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 27:208–220, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.35.
[204] AF Fercher, CK Hitzenberger,
M Sticker, E Moreno-Barriuso R Leitgeb, W Drexler, and K Sattmann. A thermal light
source technique for optical coherence tomography. Optics Communications, 185:57–64,
2000.
http://dx.doi.org/10.1016/S0030-4018(00)00986-X.
[205] R Fergus, P Perona, and A Zisserman. A sparse object category model for efficient
learning and exhaustive recognition. In Proc. of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR’05), volume 1, pages 380–387, 2005.
http://dx.doi.org/10.1109/CVPR.2005.47.
[206] RN Fernald. Evolution of eyes. Current Opinion in Neurobiology, 10:444–450, 2000.
http://dx.doi.org/10.1146/annurev.ne.15.030192.000245.
[207] X Fernandez. Template matching of binary targets in grey-scale images: A
nonparametric approach. Pattern Recognition, 30:1175–1182, 1997.
http://dx.doi.org/10.1016/S0031-3203(96)00136-7.
[208] C Ferri, P Flach, J Hernandez-Orallo, and A Senad. Modifying ROC curves to incoporate predicted probabilities. In Proc. of the ICML 2005 Workshop on ROC Analysis in Machine Learning, 2005.
[209] S Fidler and A Leonardis. Robust LDA classification by subsampling. In Proc. of the
Conference on Computer Vision and Pattern Recognition Workshop, page 97, 2003.
http://dx.doi.org/10.1109/CVPRW.2003.10089.
[210] S Fidler, D Skocaj, and A Leonardis. Combining reconstructive and discriminative
subspace methods for robust classification and regression by subsampling. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 28:337–350, 2006.
http://dx.doi.org/10.1109/TPAMI.2006.46.
[211] JW Fisher. Nonlinear Extensions to the Minimum Average Correlation Energy Filter. PhD thesis, University of Florida, 1997.
[212] RB Fisher and P Oliver. Multi-variate cross-correlation and image matching. In Proc. of the British Machine Vision Conference (BMVC’95), pages 623–632, 1995.
[213] R Fisker and JM Carstensen. On parameter estimation in deformable models. In
Proc. of the 14th IAPR International Conference on Pattern Recognition (ICPR’98),
volume 1, pages 762–766, 1998.
http://dx.doi.org/10.1109/ICPR.1998.711258.
[214] AJ Fitch, A Kadyrov, WJ Christmas, and J Kittler. Orientation correlation. In Proc. of the British Machine Vision Conference (BMVC’02), pages 133–142, 2002.
[215] AJ Fitch, A Kadyrov, WJ Christmas, and J Kittler. Fast robust correlation. IEEE
Trans. on Image processing, 14:1063–1073, 2005.
http://dx.doi.org/10.1109/TIP.2005.849767.
[216] MJ Flynn. Very high-speed computing systems. Proceedings of the IEEE, 54:1901–1909, 1966.
[217] JD Foley, A van Dam, SK Feiner, and JF Hughes. Computer Graphics: Principles and Practice in C. Addison-Wesly Professional, 1995.
[218] DA Forsyth and J Ponce. Computer Vision: A Modern Approach. Prentice Hall, 2002.
[219] A Foulonneau, P Charbonnier, and F Heitz. Affine-invariant geometric shape priors
for region-based active contours. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 28:1352–1357, 2007.
http://dx.doi.org/10.1109/TPAMI.2006.154.
[220] K Fredriksson. Engineering efficient metric indexes. Pattern Recognition Letters,
28:75–84, 2007.
http://dx.doi.org/10.1016/j.patrec.2006.06.012.
[221] K Fredriksson, V Mäkinen, and G Navarro. Rotation and lighting invariant template
matching. Information and Computation, 205:1096–1113, 2006.
http://dx.doi.org/10.1016/j.ic.2007.03.002.
[222] K Fredriksson, G Navarro, and E Ukkonen. Faster than FFT: Rotation Invariant Combinatorial Template Matching, volume 2, pages 75–112. Transworld Research Network, 2002.
[223] K Fredriksson, G Navarro, and E Ukkonen. Optimal exact and fast approximate two dimensional pattern matching allowing rotations. In Proc. of the 13th Annual Symposium on Combinatorial Pattern Matching (CPM’02), volume 2373 of Lecture Notes in Computer Science, pages 235–248. Springer, 2002.
[224] K Fredriksson, G Navarro, and E Ukkonen. Sequential and indexed two-dimensional
combinatorial template matching allowing rotations. Theoretical Computer Science A,
347:239–275, 2005.
http://dx.doi.org/10.1016/j.tcs.2005.06.029.
[225] K Fredriksson and E Ukkonen. Faster template matching without FFT. In Proc. of
the International Conference on Image Processing (ICIP’01), volume 1, pages 678–681,
2001.
http://dx.doi.org/10.1109/ICIP.2001.959136.
[226] Y Freund and RE Schapire. A short introduction to boosting. J. of the Japanese Society for Artificial Intelligence, 14:771–780, 1999.
[227] JH Friedman. Regularized discriminant analysis. J. of the American Statistical
Association, 84(405):165–175, 1987.
http://dx.doi.org/10.2307/2289860.
[228] RW Frischholz and KP Spinnler. A class of algorithms for real-time subpixel registration. In Proc. of Europto Conference, 1993.
[229] B Froba and A Ernst. Face detection with the modified census transform. In Proc.
of the 6th International Conference on Automatic Face and Gesture Recognition (FG’04),
pages 91–96, 2004.
http://dx.doi.org/10.1109/AFGR.2004.1301514.
[230] B Froba and C Kublbeck. Orientation template matching for face localization in
complex visual scenes. In Proc. of the International Conference on Image Processing
(ICIP’00), volume 2, pages 251–254, 2000.
http://dx.doi.org/10.1109/ICIP.2000.899291.
[231] K Fukunaga. Statistical Pattern Recognition. Academic Press, 2nd edition, 1990.
[232] J Gao. Robust L1 principal component analysis and its Bayesian variational inference.
Neural Computation, 20:555–572, 2008.
http://dx.doi.org/10.1162/neco.2007.11-06-397.
[233] Y Gao. Efficiently comparing face images using a modified Hausdorff distance. IEE
Proceedings - Vision, Image and Signal Processing, 150:346–350, 2003.
http://dx.doi.org/10.1049/ip-vis:20030805.
[234] Y Gao and MKH Leung. Face recognition using line edge map. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 24:764–779, 2002.
http://dx.doi.org/10.1109/TPAMI.2002.1008383.
[235] P Garcia-Martinez and HH Arsenault. Nonlinear radial-harmonic correlation using
binary decomposition for scale-invariant pattern recognition. Optics Communications,
223:273–282, 2003.
http://dx.doi.org/10.1016/S0030-4018(03)01680-8.
[236] A Garrido and N Perez De La Blanca. Physically-based active shape models
initialization and optimization. Pattern Recognition, 31:1003–1017, 1998.
http://dx.doi.org/10.1016/S0031-3203(97)00125-8.
[237] J Gaspar, C Decco, J Okamoto, and J Santos-Victor. Constant resolution
omnidirectional cameras. In Proc. of the 3rd Workshop on Omnidirectional Vision, pages
27–34, 2002.
http://dx.doi.org/10.1109/OMNVIS.2002.1044487.
[238] P Gastaldo and R Zunino. Hausdorff distance for robust and adaptive template
selection in visual target detection. Electronic Letters, 38:1651–1653, 2002.
http://dx.doi.org/10.1049/el:20021179.
[239] J Gause, PYK Cheung, and W Luk. Reconfigurable shape-adaptive template
matching architectures. In Proc. of the 10th Annual IEEE Symposium on
Field-Programmable Custom Computing Machines, pages 98–107, 2002.
http://dx.doi.org/10.1109/FPGA.2002.1106665.
[240] DM Gavrila. Multi-feature hierarchical template matching using distance transforms.
In Proc. of the 14th IAPR International Conference on Pattern Recognition (ICPR’98),
volume 1, pages 439–444, 1998.
http://dx.doi.org/10.1109/ICPR.1998.711175.
[241] DM Gavrila and J Giebel. Virtual sample generation for template-based shape
matching. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’01), volume 1, pages 676–681, 2001.
http://dx.doi.org/10.1109/CVPR.2001.990540.
[242] KR Gegenfurtner and DC Kiper. Color vision. Annual Review of Neuroscience,
26:181–206, 2003.
http://dx.doi.org/10.1146/annurev.neuro.26.041002.131116.
[243] D Geiger, TL Liu, and MJ Donahue. Sparse representations for image
decompositions. Int. J. of Computer Vision, 33:139–156, 1999.
http://dx.doi.org/10.1023/A:1008146126392.
[244] R Genov and G Cauwenberghs. Kerneltron: support vector machine in silicon. IEEE
Trans. on Neural Networks, 14:1426–1434, 2003.
http://dx.doi.org/10.1109/TNN.2003.816345.
[245] R Gentleman and DT Lang. Statistical analyses and reproducible research. J. of
Computational & Graphical Statistics, 16:1–23, 2007.
http://dx.doi.org/10.1198/106186007X178663.
[246] M Gharavi-Alkhansari. A fast globally optimal algorithm for template matching using
low-resolution pruning. IEEE Trans. on Image processing, 10:526–533, 2001.
http://dx.doi.org/10.1109/83.913587.
[247] A Giachetti. Matching techniques to compute image motion. Image and Vision
Computing, 18:247–260, 2000.
http://dx.doi.org/10.1016/S0262-8856(99)00018-9.
[248] RA Gideon and RA Hollister. A rank correlation coefficient resistant to outliers. J.
of the American Statistical Association, 82:656–666, 1987.
http://dx.doi.org/10.2307/2289479.
[249] JM Gilbert and W Yang. A real time face recognition system using custom vlsi
hardware. In Proc. of Computer Architectures for Machine Perception, pages 58–66, 1993.
http://dx.doi.org/10.1109/CAMP.1993.622458.
[250] S Girard and S Iovleff. Auto-associative models and generalized principal component
analysis. J. of Multivariate Analysis, 93:21–39, 2005.
http://dx.doi.org/10.1016/j.jmva.2004.01.006.
[251] R Gnanadesikan and JR Kettenring. Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics, 28:81–124, 1972.
[252] A Goldenshluger and A Zeevi. The Hough transform estimator. Annals of Statistics,
32:1908–1932, 2004.
http://dx.doi.org/10.1214/009053604000000760.
[253] CM Goral, KE Torrance, DP Greenberg, and B Battaile. Modeling the interaction
of light between diffuse surfaces. Computer Graphics, 18:213–222, 1984.
http://dx.doi.org/10.1145/964965.808601.
[254] C Gräßl, T Zinßer, and H Niemann. A probabilistic model-based template matching approach for robust object tracking in real-time. In Vision, Modeling, and Visualization 2004, pages 81–88, 2004.
[255] P Gravel, G Beaudoin, and JA De Guise. A method for modeling noise in medical
images. IEEE Trans. on Medical Imaging, 23:1221–1232, 2004.
http://dx.doi.org/10.1109/TMI.2004.832656.
[256] H Greenspan, J Goldberger, and L Ridel. A continuous probabilistic framework for
image matching. Computer Vision and Image Understanding, 84:384–406, 2001.
http://dx.doi.org/10.1006/cviu.2001.0946.
[257] U Grenander and MI Miller. Computational anatomy: an emerging discipline. Quarterly of Applied Mathematics, LVI:617–694, 1998.
[258] WEL Grimson and DP Huttenlocher. On the sensitivity of the Hough transform
for object recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence,
12:255–274, 1990.
http://dx.doi.org/10.1109/34.49052.
[259] R Gross, S Baker, I Matthews, and T Kanade. Face recognition across pose and illumination. In Stan Z. Li and Anil K. Jain, editors, Handbook of Face Recognition. Springer-Verlag, 2004.
[260] R Gross, I Matthews, and S Baker. Appearance-based face recognition and
light-fields. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26:449–465, 2004.
http://dx.doi.org/10.1109/TPAMI.2004.1265861.
[261] R Gross, I Matthews, and S Baker. Appearance-based face recognition and light
fields. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26:449–465, 2004.
http://dx.doi.org/10.1109/TPAMI.2004.1265861.
[262] P Grother, R Micheals, and PJ Phillips. Face Recognition Vendor Test 2002
Performance Metrics. In Proc. of the 4th International Conference on Audio-and
Video-Based Biometric Person Authentication, volume 2688 of Lecture Notes in Computer
Science, pages 937–945. Springer, 2003.
http://dx.doi.org/10.1007/3-540-44887-X.
[263] H Gu and Q Ji. An automated face reader for fatigue detection. In Proc. of the
6th International Conference on Automatic Face and Gesture Recognition (FG’04), pages
111–116, 2004.
http://dx.doi.org/10.1109/AFGR.2004.1301517.
[264] L Gu, SZ Li, and H-J Zhang. Learning probabilistic distribution model for multi-view
face detection. In Proc. of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR’01), volume 2, pages 116–122, 2001.
http://dx.doi.org/10.1109/CVPR.2001.990934.
[265] B Guo, KM Lam, KH Lin, and WC Siu. Human face recognition based on spatially
weighted Hausdorff distance. Pattern Recognition Letters, 24:499–507, 2003.
http://dx.doi.org/10.1016/S0167-8655(02)00272-6.
[266] G Guo, SZ Li, and K Chan. Face recognition by support vector machines. In Proc.
of the 4th International Conference on Automatic Face and Gesture Recognition (FG’00),
pages 196–201, 2000.
http://dx.doi.org/10.1109/AFGR.2000.840634.
[267] M Gyulassy and M Harlander. Elastic tracking and neural network algorithms for
complex pattern recognition. Computer Physics Communications, 66:31–46, 1991.
http://dx.doi.org/10.1016/0010-4655(91)90005-6.
[268] M Hagedoorn and R Veltkamp. Metric pattern spaces. Technical Report 1999-03, Utrecht University, Information and Computing Sciences, 1999.
[269] G Halder, P Callaerts, and WJ Gehring. New perspectives on eye evolution. Current
Opinion in Genetics & Development, 5:602–609, 1995.
http://dx.doi.org/10.1093/jhered/esi027.
[270] PM Hall, D Marshall, and RR Martin. Incremental eigenanalysis for classification. In Proc. of the British Machine Vision Conference (BMVC’98), pages 286–295, 1998.
[271] FR Hampel. The influence curve and its role in robust estimation. J. of the American
Statistical Association, 69:383–393, 1974.
http://dx.doi.org/10.2307/2285666.
[272] FR Hampel, PJ Rousseeuw, EM Ronchetti, and WA Stahel. Robust statistics: the approach based on influence functions. J. Wiley & Sons, 1986.
[273] I Han, ID Yun, and SU Lee. Model-based object recognition using the Hausdorff
distance with explicit pairing. In Proc. of the International Conference on Image
Processing (ICIP’99), pages 83–87, 1999.
http://dx.doi.org/10.1109/ICIP.1999.819524.
[274] TX Han, V Ramesh, Y Zhut, and TS Huang. On optimizing template matching via
performance characterization. In Proc. of the 10th International Conference on Computer
Vision and Pattern Recognition (ICCV’05), volume 1, pages 182–189, 2005.
http://dx.doi.org/10.1109/ICCV.2005.178.
[275] X Han, C Xu, and JL Prince. A topology preserving level set method for geometric
deformable models. IEEE Trans. on Pattern Analysis and Machine Intelligence,
25:755–768, 2003.
http://dx.doi.org/10.1109/TPAMI.2003.1201824.
[276] KV Hansen and PA Toft. Fast curve estimation using preconditioned generalized
Radon transform. tip, 5:1651–1661, 1996.
http://dx.doi.org/10.1109/83.544572.
[277] LK Hansen, J Larsen, FA Nielsen, SC Strother, E Rostrup, R Savoy, C Svarer, and
OB Paulson. Generalizable patterns in neuroimaging: How many principal components?
NeuroImage, 9:534–544, 1999.
http://dx.doi.org/10.1006/nimg.1998.0425.
[278] R Haralick, SR Sternberg, and X Zhuang. Image analysis using mathematical morphology. IEEE Trans. on Pattern Analysis and Machine Intelligence, 9:523–550, 1987.
[279] DM Hawkins and GJ McLachlan. High breakdown linear discriminant analysis. J. of the American Statistical Association, 92:136–143, 1997.
[280] E Hecht. Optics. Addison-Wesley, 2nd edition, 1987.
[281] B Heisele, P Ho, and T Poggio. Face recognition with support vector machines:
global versus component-based approach. In Proc. of the 8th International Conference on
Computer Vision and Pattern Recognition (ICCV’01), volume 2, pages 688–694, 2001.
http://dx.doi.org/10.1109/ICCV.2001.937693.
[282] B Heisele, P Ho, J Wu, and T Poggio. Face recognition: component-based versus
global approaches. Computer Vision and Image Understanding, 91:6–21, 2003.
http://dx.doi.org/10.1016/S1077-3142(03)00073-0.
[283] Y Hel-Or and H Hel-Or. Generalized pattern matching using orbit decomposition.
In Proc. of the International Conference on Image Processing (ICIP’03), volume 3, pages
69–72, 2003. Nayar work re-discovered!
http://dx.doi.org/10.1109/ICIP.2003.1247183.
[284] Y Hel-Or and H Hel-Or. Real-time pattern matching using projection kernels. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 27:1430–1445, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.184.
[285] Y Hel-Or and H Hel-Or. Real-time pattern matching using projection kernels. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 27:1430–1445, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.184.
[286] Y Hel-Or and H Hel-Or. Real-time pattern matching using projection kernels. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 27:1430–1445, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.184.
[287] AJ Hii, CE Hann, JG Chase, and EE Van Houten. Fast normalized cross correlation
for motion tracking using basis functions. Computer Methods and Programs in
Biomedicine, 82:144–156, 2006.
http://dx.doi.org/10.1016/j.cmpb.2006.02.007.
[288] T Hofman, B Scholkopf, and AJ Smola. Kernel methods in machine learning. Annals
of Statistics, 36:1171–1220, 2008.
http://dx.doi.org/10.1214/009053607000000677.
[289] K Honda, N Sugiura, and H Ichihashi. Robust local principal component analyzer with fuzzy clustering. In Proc. of the International Joint Conference on Neural Networks, volume 1, pages 732–737, 2003.
[290] BK Horn. Robot Vision. The MIT Press, 1986.
[291] BKP Horn and BG Schunk. Determining optical flow. Artificial Intelligence,
17:185–203, 1981.
http://dx.doi.org/10.1016/0004-3702(81)90024-2.
[292] K Hornik, M Stinchcombe, and H White. Multilayer feedforward network are universal approximators. Neural Networks, 2:359–366, 1989.
[293] K Hotta. View-invariant face detection method based on local PCA cells. In Proc. of
the 12th International Conference on Image Analysis and Processing, pages 57–62, 2003.
http://dx.doi.org/10.1109/ICIAP.2003.1234025.
[294] PVC Hough. Method and means for recognizing complex patterns. US Patent Nr. 3069654, 1962.
[295] PO Hoyer. Non-negative matrix factorization with sparseness constraints. J. of Machine Learning Research, 5:1457–1469, 2004.
[296] CW Hsu, CC Chang, and CJ Lin. A practical guide to support vector classification. Technical report, Dept. of Computer Science, National Taiwan University, 2008.
[297] Y Hu and Z Wang. A similarity measure based on Hausdorff distance for human face
recognition. In Proc. of the 18th IAPR International Conference on Pattern Recognition
(ICPR’06), volume 3, pages 1131–1134, 2006.
http://dx.doi.org/10.1109/ICPR.2006.174.
[298] XS Hua, X Chen, and HJ Zhang. Robust video signature based on ordinal measure.
In Proc. of the International Conference on Image Processing (ICIP’04), volume 1, pages
685–688, 2004.
http://dx.doi.org/10.1109/ICIP.2004.1418847.
[299] C Huang, H Ai, Y Li, and S Lao. High-performance rotation invariant multiview
face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 29:671–686,
2007.
http://dx.doi.org/10.1109/TPAMI.2007.1011.
[300] H-C Huang, Y-P Hung, and W-L Hwang. Adaptive early jump-out technique for fast
motion estimation in video coding. In Proc. of the 13th IAPR International Conference
on Pattern Recognition (ICPR’96), volume 2, pages 864–868, 1996.
http://dx.doi.org/10.1109/ICPR.1996.547199.
[301] K Huang, Y Ma, and R Vidal. Minimum effective dimension for mixtures of
subspaces: A robust GPCA algorithm and its applications. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’04), volume 2, pages
631–638, 2004.
http://dx.doi.org/10.1109/CVPR.2004.155.
[302] PJ Huber. Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35:73–101, 1964.
[303] PJ Huber. Robust statistics. J. Wiley & Sons, New-York, 1981.
[304] DJ Hunt, LW Nolte, and AR Reibman. Hough transform and signal detection theory
performance for images with additive noise. Computer Vision, Graphics and Image
Processing, 52:386–401, 1990.
http://dx.doi.org/10.1016/0734-189X(90)90082-7.
[305] DJ Hunt, LW Nolte, and WH Ruedger. Performance of the Hough transform and its
relationship to statistical signal detection theory. Computer Vision, Graphics and Image
Processing, 43:221–238, 1988.
http://dx.doi.org/10.1016/0734-189X(88)90062-X.
[306] DP Huttenlocher and EW Jaquith. Computing visual correspondence: incorporating
the probability of a false match. In Proc. of the 5th International Conference on Computer
Vision and Pattern Recognition (ICCV’95), pages 515–522, 1995.
http://dx.doi.org/10.1109/ICCV.1995.466896.
[307] DP Huttenlocher, GA Klanderman, and WJ Rucklidge. Comparing images using the
Hausdorff distance under translation. In Proc. of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR’92), pages 654–656, 1992.
http://dx.doi.org/10.1109/CVPR.1992.223209.
[308] DP Huttenlocher, GA Klanderman, and WJ Rucklidge. Comparing images using
the Hausdorff distance. IEEE Trans. on Pattern Analysis and Machine Intelligence,
15:850–863, 1993.
http://dx.doi.org/10.1109/34.232073.
[309] DP Huttenlocher and WJ Rucklidge. Multi-resolution technique for comparing
images using the Hausdorff distance. In Proc. of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR’93), pages 705–706, 1993.
http://dx.doi.org/10.1109/CVPR.1993.341019.
[310] K Hwang and FA Briggs. Computer Architecture and Parallel Processing. McGraw-Hill, 1984.
[311] HT Hytti. Characterization of digital image noise properties based on RAW data. In Image Quality and System Performance III, volume 6059 of Proceedings of SPIE, 2005.
[312] A Hyvarinen and E Oja. Independent component analysis: algorithms and
applications. Neural Networks, 13:411–430, 2000.
http://dx.doi.org/10.1016/S0893-6080(00)00026-5.
[313] K Ikeuchi, T Shakunaga, MD Wheeler, and T Yamazaki. Invariant histograms and
deformable template matching for sar target recognition. In Proc. of the IEEE Conference
on Computer Vision and Pattern Recognition (CVPR’96), pages 100–105, 1996.
http://dx.doi.org/10.1109/CVPR.1996.517060.
[314] J Illingworth and JV Kittler. A survey of the Hough transform. Computer Vision,
Graphics and Image Processing, 44:87–116, 1988.
http://dx.doi.org/10.1016/S0734-189X(88)80033-1.
[315] AK Jain, RPW Duin, and J Mao. Statistical pattern recognition: A review. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 22:4–37, 2000.
http://dx.doi.org/10.1109/34.824819.
[316] AK Jain, P Flynn, and AA Ross, editors. Handbook of Biometrics. Springer, 2007.
[317] AK Jain, K Nandakumar, and A Ross. Score normalization in multimodal biometric
systems. Pattern Recognition, 38:2270–2285, 2005.
http://dx.doi.org/10.1016/j.patcog.2005.01.012.
[318] AK Jain and A Ross. Multibiometric systems. Communications of the ACM,
47:34–40, 2004.
http://dx.doi.org/10.1145/962081.962102.
[319] AK Jain, Y Zhong, and MP Dubuisson-Jolly. Deformable template models: A review.
Signal Processing, 71:109–129, 1998.
http://dx.doi.org/10.1016/S0165-1684(98)00139-X.
[320] AK Jain, Y Zhong, and S Lakshmanan. Object matching using deformable
templates. IEEE Trans. on Pattern Analysis and Machine Intelligence, 18:267–278, 1996.
http://dx.doi.org/10.1109/34.485555.
[321] CV Jawahar and PJ Narayanan. Generalised correlation for multi-feature
correspondence. Pattern Recognition, 35:1303–1313, 2002.
http://dx.doi.org/10.1016/S0031-3203(01)00111-X.
[322] SC Jeng and WH Tsai. Scale- and orientation-invariant generalized Hough transform
- a new approach. Pattern Recognition, 24:1037–1051, 1991.
http://dx.doi.org/10.1016/0031-3203(91)90120-T.
[323] HW Jensen. Realistic Image Synthesis Using Photon Mapping. AK Peters, 2001.
[324] Q Ji and RM Haralick. Error propagation for the Hough transform. Pattern
Recognition Letters, 22:813–823, 2001.
http://dx.doi.org/10.1016/S0167-8655(01)00026-5.
[325] M Jogan, E Zagar, and A Leonardis. Karhunen-Loeve expansion of a set of rotated
templates. IEEE Trans. on Image processing, 12:817–825, 2003.
http://dx.doi.org/10.1109/TIP.2003.813141.
[326] HJ Johnson and GE Christensen. Consistent landmark and intensity-based image
registration. IEEE Trans. on Medical Imaging, 21:450–461, 2002.
http://dx.doi.org/10.1109/TMI.2002.1009381.
[327] S Johnson. The relationship between the matched-filter operator and the target
signature space-orthogonal projection classifier. IEEE Trans. on Geoscience and Remote
Sensing, 38:283–286, 2000.
http://dx.doi.org/10.1109/36.823920.
[328] K Jonsson, J Matas, J Kittler, and YP Li. Learning support vectors for face
verification and recognition. In Proc. of the 4th International Conference on Automatic
Face and Gesture Recognition (FG’00), pages 208–213, 2000.
http://dx.doi.org/10.1109/AFGR.2000.840636.
[329] F Jurie and M Dhome. Real time 3D template matching. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’01), volume 1, pages
791–796, 2001.
http://dx.doi.org/10.1109/CVPR.2001.990559.
[330] F Jurie and M Dhome. A simple and efficient template matching algorithm. In
Proc. of the 8th International Conference on Computer Vision and Pattern Recognition
(ICCV’01), pages 544–549, 2001.
http://dx.doi.org/10.1109/ICCV.2001.937673.
[331] F Jurie and M Dhome. Hyperplane approximation for template matching. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 24:996–1000, 2002.
http://dx.doi.org/10.1109/TPAMI.2002.1017625.
[332] A Just, Y Rodriguez, and M Marcel. Hand posture classification and recognition
using the modified census transform. In Proc. of the 7th International Conference on
Automatic Face and Gesture Recognition (FG’06), pages 351–356, 2006.
http://dx.doi.org/10.1109/FGR.2006.62.
[333] A Kadyrov and M Petrou. The Trace transform and its applications. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 23:811–828, 2001.
http://dx.doi.org/10.1109/34.946986.
[334] A Kadyrov and M Petrou. The invaders algorithm: Range of values modulation
for accelerated correlation. IEEE Trans. on Pattern Analysis and Machine Intelligence,
28:1882–1886, 2006.
http://dx.doi.org/10.1109/TPAMI.2006.234.
[335] IA Kakadiaris, G Passalis, G Toderici, MN Murtuza, Y Lu, N Karampatziakis, and
T Theoharis. Three-dimensional face recognition in the presence of facial expressions: An
annotated deformable model approach. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 29:640–649, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.1017.
[336] P Kalocsai, W Zhao, and E Elagin. Face similarity space as perceived by humans
and artificial systems. In Proc. of the 3rd International Conference on Automatic Face
and Gesture Recognition (FG’98), pages 177–180, 1998.
http://dx.doi.org/10.1109/AFGR.1998.670945.
[337] H Kalviainen, P Hirvonen, L Xu, and E Oja. Probabilistic and non-probabilistic
Hough transforms: overview and comparisons. Image and Vision Computing, 13:239–252,
1995.
http://dx.doi.org/10.1016/0262-8856(95)99713-B.
[338] S Kaneko, I Murase, and S Igarashi. Robust image registration by increment sign
correlation. Pattern Recognition, 35:2223–2234, 2002.
http://dx.doi.org/10.1016/S0031-3203(01)00177-7.
[339] S Kaneko, Y Satoh, and S Igarashi. Using selective correlation coefficient for robust
image registration. Pattern Recognition, 36:1165–1173, 2003.
http://dx.doi.org/10.1016/S0031-3203(02)00081-X.
[340] T Kaneko and O Hori. Template update criterion for template matching of image
sequences. In Proc. of the 16th IAPR International Conference on Pattern Recognition
(ICPR’02), volume 2, pages 1–5, 2002.
http://dx.doi.org/10.1109/ICPR.2002.1048221.
[341] T Kaneko and O Hori. Feature selection for reliable tracking using template
matching. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’03), volume 1, pages 796–802, 2003.
http://dx.doi.org/10.1109/CVPR.2003.1211434.
[342] A Kapoor, YA Qi, and RW Picard. Fully automatic upper facial action recognition.
In Proc. of the IEEE International Workshop on Analysis and Modeling of Faces and
Gestures (AMFG’03), pages 195–202, 2003.
http://dx.doi.org/10.1109/AMFG.2003.1240843.
[343] YB Karasik. A recursive formula for convolutions/correlations and its application in
pattern recognition. Pattern Recognition Letters, 19:53–56, 1998.
http://dx.doi.org/10.1016/S0167-8655(97)00149-9.
[344] J Karhunen and J Joutsensalo. Learning of robust principal component subspace.
In Proc. of the International Joint Conference on Neural Networks, volume 3, pages
2409–2412, 1993.
http://dx.doi.org/10.1109/IJCNN.1993.714211.
[345] S Karungaru, M Fukumi, and N Akamatsu. Face recognition using genetic algorithm
based template matching. In Communications and Information Technology, 2004. ISCIT
2004. IEEE International Symposium on, volume 2, pages 1252–1257, 2004.
http://dx.doi.org/10.1109/ISCIT.2004.1413920.
[346] M Kass, A Witkin, and D Terzopoulos. Snakes: Active contour models. Int. J. of
Computer Vision, 1:321–331, 1988.
http://dx.doi.org/10.1007/BF00133570.
[347] AA Kassim, T Tan, and KH Tan. A comparative study of efficient generalised Hough
transform techniques. Image and Vision Computing, 17:737–748, 1999.
http://dx.doi.org/10.1016/S0262-8856(98)00156-5.
[348] T Kawanishi, T Kurozumi, K Kashino, and S Takagi. A fast template matching
algorithm with adaptive skipping using inner-subtemplates’ distances. In Proc. of
the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’04), pages
654–657, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334614.
[349] Q Ke and T Kanade. Robust L1 norm factorization in the presence of outliers and
missing data by alternative convex programming. In Proc. of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 739–746, 2005.
http://dx.doi.org/10.1109/CVPR.2005.309.
[350] Y Keller, A Averbuch, and M Israeli. Pseudopolar-based estimation of large
translations, rotations, and scalings in images. IEEE Trans. on Image processing, 14:12–22,
2005.
http://dx.doi.org/10.1109/TIP.2004.838692.
[351] Y Keller, A Averbuch, and O Miller. Robust phase correlation. In Proc. of the
17th IAPR International Conference on Pattern Recognition (ICPR’04), volume 2, pages
740–743, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334365.
[352] RA Kerekes and BVKV Kumar. Correlation filters with controlled scale response.
IEEE Trans. on Image processing, 15:1794–1802, 2006.
http://dx.doi.org/10.1109/TIP.2006.873468.
[353] D Keren, M Osadchy, and C Gotsman. Antifaces: A novel, fast method for image
detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23:747–761, 2001.
http://dx.doi.org/10.1109/34.935848.
[354] AL Kesidis and N Papamarkos. On the inverse Hough transform. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 21:1329–1343, 1999.
http://dx.doi.org/10.1109/34.817411.
[355] D Keysers, W Macherey, H Ney, and J Dahmen. Adaptation in statistical pattern
recognition using tangent vectors. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 26:269–274, 2004.
http://dx.doi.org/10.1109/TPAMI.2004.1262198.
[356] M Khosravi and RW Schafer. Low complexity matching criteria for image/video
applications. In Proc. of the International Conference on Image Processing (ICIP’94),
volume 3, pages 776–780, 1994.
http://dx.doi.org/10.1109/ICIP.1994.413783.
[357] M Khosravi and RW Schafer. Template matching based on a grayscale hit-or-miss
transform. IEEE Trans. on Image processing, 5:1060–1066, 1996.
http://dx.doi.org/10.1109/83.503921.
[358] J Kim, J Choi, J Yi, and M Turk. Effective representation using ICA for face
recognition robust to local distortion and partial occlusion. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 27:1977–1981, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.242.
[359] J Kim and JA Fessler. Intensity-based image registration using robust correlation
coefficients. IEEE Trans. on Medical Imaging, 23:1430–1444, 2004.
http://dx.doi.org/10.1109/TMI.2004.835313.
[360] J Kim, V Kolmogorov, and R Zabih. Visual correspondence using energy
minimization and mutual information. In Proc. of the 9th International Conference on
Computer Vision and Pattern Recognition (ICCV’03), pages 1033–1040, 2003.
http://dx.doi.org/10.1109/ICCV.2003.1238463.
[361] SH Kim and RH Park. An efficient algorithm for video sequence matching using the
modified Hausdorff distance and the directed divergence. IEEE Trans. on Circuits and
Systems for Video Technology, 12:592–596, 2002.
http://dx.doi.org/10.1109/TCSVT.2002.800512.
[362] SH Kim, HR Tizhoosh, and M Kamel. Choquet integral-based aggregation of image
template matching algorithms. In Proc. of the 22nd International Conference of the North
American Fuzzy Information Processing Society (NAFIPS’03), pages 143–148, 2003.
http://dx.doi.org/10.1109/NAFIPS.2003.1226771.
[363] T-K Kim and J Kittler. Locally linear discriminant analysis for multimodally
distributed classes for face recognition with a single model image. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 27:318–327, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.58.
[364] N Kiryati, H Kalviainen, and S Alaoutinen. Randomized or probabilistic Hough
transform: unified performance evaluation. Pattern Recognition Letters, 21:1157–1164,
2000.
http://dx.doi.org/10.1016/S0167-8655(00)00077-5.
[365] J Kittler. Probabilistic relaxation and the Hough transform. Pattern Recognition,
33:705–714, 2000.
http://dx.doi.org/10.1016/S0031-3203(99)00081-3.
[366] R Kjeldsen and A Aner. Improving face tracking with 2d template warping. In Proc.
of the 4th International Conference on Automatic Face and Gesture Recognition (FG’00),
pages 129–135, 2000.
http://dx.doi.org/10.1109/AFGR.2000.840623.
[367] R Klette and P Zamperoni. Measures of correspondence between binary patterns.
Image and Vision Computing, 5:287–295, 1987.
http://dx.doi.org/10.1016/0262-8856(87)90005-9.
[368] R Knothe, S Romdhani, and T Vetter. Combining PCA and LFA for surface
reconstruction from a sparse set of control points. In Proc. of the 7th International
Conference on Automatic Face and Gesture Recognition (FG’06), pages 637–644, 2006.
http://dx.doi.org/10.1109/FGR.2006.31.
[369] D Knuth. Literate programming. CSLI Lecture Notes 27, Center for the Study of Language and Information, Stanford, California, 1992.
[370] A Kohandani, O Basir, and M Kamel. A fast algorithm for template matching. In
Proc. of the 3rd International Conference on Image Analysis and Recognition (ICIAR’06),
volume 4142 of Lecture Notes in Computer Science, pages 398–409. Springer, 2006.
http://dx.doi.org/10.1007/11867661_36.
[371] R Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model. In Proc. of the International Joint Conference on Artificial Intelligence, pages 1137–1145, 1995.
[372] A Koloydenko and D Geman. Ordinal coding of image microstructure. In Proc. of the International Conference on Image Processing, Computer Vision, & Pattern Recognition, volume 2, pages 613–620, 2006.
[373] SG Kong, J Heo, F Boughorbel, Y Zheng, BR Abidi, A Koschan, M Yi, and
MA Abidi. Multiscale fusion of visible and thermal IR images for illumination-invariant
face recognition. Int. J. of Computer Vision, 71:215–233, 2007.
http://dx.doi.org/10.1007/s11263-006-6655-0.
[374] Y Koren and L Carmel. Robust linear dimensionality reduction. IEEE Trans. on
Visualization and Computer Graphics, 10:459–470, 2004.
http://dx.doi.org/10.1109/TVCG.2004.17.
[375] I Kotsia and I Pitas. Facial expression recognition in image sequences using geometric
deformation features and support vector machines. IEEE Trans. on Image processing,
16:172–187, 2007.
http://dx.doi.org/10.1109/TIP.2006.884954.
[376] D Kottke and PD Fiore. Systolic array for acceleration of template based ATR. In
Proc. of the International Conference on Image Processing (ICIP’97), pages 869–872,
1997.
http://dx.doi.org/10.1109/ICIP.1997.648104.
[377] W Krattenthaler, KJ Mayer, and M Zeiller. Point correlation: a reduced-cost
template matching technique. In Proc. of the International Conference on Image
Processing (ICIP’94), volume 1, pages 208–212, 1994.
http://dx.doi.org/10.1109/ICIP.1994.413305.
[378] D Krishnaswamy and P Banerjeer. Exploiting task and data parallelism in parallel
Hough and Radon transforms. In Proc. of the Internation Conference on Parallel
Processing, pages 441–444, 1997.
http://dx.doi.org/10.1109/ICPP.1997.622678.
[379] V Krouverk. POVMan v1.2. http://www.aetec.ee/fv/vkhomep.nsf/pages/povman2, 2005.
[380] S Krüger and A Calway. Image registration using multiresolution frequency domain correlation. In Proc. of the British Machine Vision Conference (BMVC’98), 1998.
[381] C Küblbeck and A Ernts. Face detection and tracking in video sequences using the
modified census transformation. Image and Vision Computing, 24:564–572, 2006.
http://dx.doi.org/10.1016/j.imavis.2005.08.005.
[382] Z Kulpa. PICASSO, PICASSO-SHOW and PAL - a development of a high-level software system for image processing. In Duff MJB and Levialdi S, editors, Languages and Architectures for Image Processing, pages 13–24. Academic Press, 1981.
[383] BVKV Kumar. Minimum-variance synthetic discriminant functions. J. of the Optical
Society of America A, 3:1579–1584, 1986.
http://dx.doi.org/10.1364/JOSAA.3.001579.
[384] BVKV Kumar and JD Brasher. Relationship between maximizing the signal-to-noise
ratio and minimizing the classification error probability for correlation filters. Optics
Letters, 17:940–942, 1992.
http://dx.doi.org/10.1364/OL.17.000940.
[385] BVKV Kumar, D Casasent, and H Murakami. Principal-component imagery for statistical pattern recognition correlators. Optical Engineering, 21:43–47, 1982.
[386] BVKV Kumar, A Mahalanobis, S Song, SRF Sims, and JF Epperson. Minimum squared error synthetic discriminant functions. Optical Engineering, 31:915–922, 1992.
[387] BVKV Kumar, M Savvides, and C Xie. Correlation pattern recognition for face
recognition. Proceedings of the IEEE, 94:1963–1976, 2006.
http://dx.doi.org/10.1109/JPROC.2006.884094.
[388] B Kurt, M Gokmen, and AK Jain. Image compression based on centipede model. In Springer, editor, Proc. of the 9th International Conference on Image Analysis and Processing, volume 1310 of Lecture Notes in Computer Science, pages 303–310, 1997.
[389] OK Kwon, DG Sim, and RH Park. New Hausdorff distances based on robust
statistics for comparing images. In Proc. of the International Conference on Image
Processing (ICIP’96), volume 3, pages 21–24, 1996.
http://dx.doi.org/10.1109/ICIP.1996.560359.
[390] OK Kwon, DG Sim, and RH Park. Robust Hausdorff distance matching algorithms
using pyramidal structures. Pattern Recognition, 34:2005–2013, 2001.
http://dx.doi.org/10.1016/S0031-3203(00)00132-1.
[391] AB Kyatkin and GS Chirikjian. Pattern matching as a correlation on the discrete
motion group. Computer Vision and Image Understanding, 74:22–35, 1999.
http://dx.doi.org/10.1006/cviu.1999.0745.
[392] KF Lai and RT Chin. Deformable contours: modeling and extraction. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 17:1084–1090, 1995.
http://dx.doi.org/10.1109/34.473235.
[393] KF Lai, CM Ngo, and S Chan. Tracking of deformable contours by synthesis and
match. In Proc. of the 13th IAPR International Conference on Pattern Recognition
(ICPR’96), volume 1, pages 657–661, 1996.
http://dx.doi.org/10.1109/ICPR.1996.546106.
[394] S-H Lai and S-D Wei. Reliable image matching based on relative gradients. In Proc.
of the 16th IAPR International Conference on Pattern Recognition (ICPR’02), volume 2,
pages 802–805, 2002.
http://dx.doi.org/10.1109/ICPR.2002.1048424.
[395] M Lalonde and L Gagnon. Variable neighborhood search for geometrically deformable
templates. In Proc. of the 16th IAPR International Conference on Pattern Recognition
(ICPR’02), volume 2, pages 689–692, 2002.
http://dx.doi.org/10.1109/ICPR.2002.1048395.
[396] WCY Lam, LTS Lam, KSY Yuen, and DNK Leung. An analysis on quantizing the
Hough space. Pattern Recognition Letters, 15:1127–1135, 1994.
http://dx.doi.org/10.1016/0167-8655(94)90128-7.
[397] Z-D Lan, R Mohr, and P Remagnino. Robust matching by partial correlation. In Proc. of the British Machine Vision Conference (BMVC’95), volume 2, pages 651–660, 1995.
[398] ZD Lan and R Mohr. Non-parametric invariants and application to matching. Technical Report 3246, INRIA, 1997.
[399] ZD Lan and R Mohr. Robust location based partial correlation. In Proc. of the
International Conference on Computer Analysis of Images and Patterns, volume 1296 of
Lecture Notes in Computer Science, pages 313–320. Springer, 1997.
http://dx.doi.org/10.1007/3-540-63460-6_132.
[400] ZD Lan and R Mohr. Direct linear sub-pixel correlation by incorporation of neighbor
pixels’ information and robust estimation of window transformation. Machine Vision and
Applications, 10:256–268, 1998.
http://dx.doi.org/10.1007/s001380050077.
[401] ZD Lan and R Mohr. Direct linear sub-pixel correlation by incorporation of neighbor
pixels’ information and robust estimation of window transformation. Machine Vision and
Applications, 10:256–268, 1998.
http://dx.doi.org/10.1007/s001380050077.
[402] MF Land. Visual acuity in insects. Annual Review of Entomology, 42:147–177, 1997.
http://dx.doi.org/10.1146/annurev.ento.42.1.147.
[403] M Last. The uncertainty principle of cross-validation. In Proceedings of the IEEE International Conference on Granular Computing, pages 275–280, 2006.
[404] N Lawrence. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. of Machine Learning Research, 6:1783–1816, 2005.
[405] VF Leavers. The dynamic generalized Hough transform: Its relationship to the
probabilistic Hough transforms and an application to the concurrent detection of circles
and ellipses. CVGIP: Image Understanding, 56:381–398, 1992.
http://dx.doi.org/10.1016/1049-9660(92)90049-9.
[406] VF Leavers. Which Hough transform? CVGIP: Image Understanding, 58:250–2264,
1993.
http://dx.doi.org/10.1006/ciun.1993.1041.
[407] VF Leavers. Use of the two-dimensional Radon transform to generate a taxonomy
of shape for the characterization of abrasive powder particles. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 22:1411–1423, 2000.
http://dx.doi.org/10.1109/34.895975.
[408] DD Lee and HS Seung. Learning the parts of objects by non-negative matrix
factorization. Nature, 401:788–791, 1999.
http://dx.doi.org/10.1038/44565.
[409] K-C Lee, J Ho, M-H Yang, and D Kriegman. Video-based face recognition using
probabilistic appearance manifolds. In Proc. of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR’03), volume 1, pages 313–320, 2003.
http://dx.doi.org/10.1109/CVPR.2003.1211369.
[410] Y Lee, Y Lin, and G Wahba. Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data. J. of the American Statistical Association, 99:67–81, 2004.
[411] YH Lee and JC Shim. Curvature based human face recognition using depth weighted
Hausdorff distance. In Proc. of the International Conference on Image Processing
(ICIP’04), volume 3, pages 1429–1432, 2004.
http://dx.doi.org/10.1109/ICIP.2004.1421331.
[412] S Levialdi, A Moggiolo-Schettini, M Napoli, G Tortora, and G Uccella. On the design and implementation of PIXAL, a language for image processing. In Duff MJB and Levialdi S, editors, Languages and Architectures for Image Processing, pages 89–98. Academic Press, 1981.
[413] MS Lew and TS Huang. Optimal multi-scale matching. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’99), volume 1, pages
1088–1093, 1999.
http://dx.doi.org/10.1109/CVPR.1999.786922.
[414] MS Lew and N Huijsmans. Information theory and face detection. In Proc. of the
13th IAPR International Conference on Pattern Recognition (ICPR’96), volume 3, pages
601–605, 1996.
http://dx.doi.org/10.1109/ICPR.1996.547017.
[415] MS Lew, N Sube, and TS Huang. Improving visual matching. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’00), volume 2, pages
58–65, 2000.
http://dx.doi.org/10.1109/CVPR.2000.854737.
[416] JP Lewis. Fast template matching. In Proc. of Vision Interface, pages 120–123, 1995.
[417] RR Lewis. Making shaders more physically plausible. In Proc. of the Eurographics
Workshop on Rendering, pages 47–62, 1993.
http://dx.doi.org/10.1111/1467-8659.1320109.
[418] F Li and MKH Leung. Hierarchical identification of palmprint using line-based Hough
transform. In Proc. of the 18th IAPR International Conference on Pattern Recognition
(ICPR’06), volume 4, pages 149–152, 2006.
http://dx.doi.org/10.1109/ICPR.2006.622.
[419] H Li, J Tao, and K Zhang. Efficient and robust feature extraction by maximum
margin criterion. IEEE Trans. on Neural Networks, 17:157–165, 2006.
http://dx.doi.org/10.1109/TNN.2005.860852.
[420] H Li and A Yezzi. Local or global minima: Flexible dual-front active contours. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 29:1–14, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.250595.
[421] SZ Li, Q Fu, L Gu, B Scholkopf, Y Cheng, and H Zhang. Kernel machine based
learning for multi-view face detection and pose estimation. In Proc. of the 8th International
Conference on Computer Vision and Pattern Recognition (ICCV’01), volume 2, pages
674–679, 2001.
http://dx.doi.org/10.1109/ICCV.2001.937691.
[422] SZ Li, XW Hou, HJ Zhang, and QS Chen. Learning spatially localized, parts-based
representation. In Proc. of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR’01), volume 1, pages 207–212, 2001.
http://dx.doi.org/10.1109/CVPR.2001.990477.
[423] Y Li. On incremental and robust subspace learning. Pattern Recognition,
37:1509–1518, 2004.
http://dx.doi.org/10.1016/j.patcog.2003.11.010.
[424] Y Li, S Gong, J Sherrah, and H Liddell. Support vector machine based multi-view
face detection and recognition. Image and Vision Computing, 22:413–427, 2004.
http://dx.doi.org/10.1016/j.imavis.2003.12.005.
[425] Y Li, L-Q Xu, J Morphett, and R Jacobs. An integrated algorithm of incremental
and robust PCA. In Proc. of the International Conference on Image Processing (ICIP’03),
volume 1, pages 245–248, 2003.
http://dx.doi.org/10.1109/ICIP.2003.1246944.
[426] Z Li and X Tang. Bayesian face recognition using support vector machine and face
clustering. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’04), volume 2, pages 374–380, 2004.
http://dx.doi.org/10.1109/CVPR.2004.1315188.
[427] X Liang and JS-N Jean. Mapping of generalized template matching onto
reconfigurable computers. IEEE Trans. on Very Large Scale Integration (VLSI) Systems,
11:485–498, 2003.
http://dx.doi.org/10.1109/TVLSI.2003.812306.
[428] KH Lin, KM Lam, and WC Siu. Spatially eigen-weighted Hausdorff distances for
human face recognition. Pattern Recognition, 36:1827–1834, 2003.
http://dx.doi.org/10.1016/S0031-3203(03)00011-6.
[429] YH Lin, CH Chen, and CC Wei. New method for subpixel image matching with
rotation invariance by combining the parametric template method and the ring projection
transform process. Optical Engineering, 45, 2006.
http://dx.doi.org/10.1117/1.2213609.
[430] JD Lipson. Elements of Algebra and Algebraic Computing. Addison Wesley Longman Publishing Co, 1981.
[431] C Liu, WT Freeman, R Szeliski, and SB Kang. Noise estimation from a single
image. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’06), volume 1, pages 901–908, 2006.
http://dx.doi.org/10.1109/CVPR.2006.207.
[432] C Liu and H Wechsler. A unified Bayesian framework for face recognition. In Proc. of
the International Conference on Image Processing (ICIP’98), volume 1, pages 151–155,
1998.
http://dx.doi.org/10.1109/ICIP.1998.723447.
[433] C Liu and H Wechsler. Comparative assessment of independent component analysis (ICA) for face recognition. In Proc. of the 2nd International Conference on Audio-and Video-Based Biometric Person Authentication, pages 211–216, 1999.
[434] ZY Liu and L Xu. Topological local principal component analysis. Neurocomputing,
55:739–745, 2003.
http://dx.doi.org/10.1016/S0925-2312(03)00414-4.
[435] M Loog, RPW Duin, and R Haeb-Umbach. Multiclass linear dimension reduction
by weighted pairwise Fisher criteria. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 23:762–766, 2001.
http://dx.doi.org/10.1109/34.935849.
[436] C Lu, T Zhang, R Zhang, and C Zhang. Adaptive robust kernel PCA algorithm. In
Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP’03), volume 6, pages 621–624, 2003.
http://dx.doi.org/10.1109/ICASSP.2003.1201758.
[437] C-D Lu, T-Y Zhang, X-Z Du, and C-P Li. A robust kernel PCA algorithm. In Proc.
of the International Conference on Machine Learning and Cybernetics, volume 5, pages
3084–3087, 2004.
http://dx.doi.org/10.1109/ICMLC.2004.1378562.
[438] J Lu, KN Plataniotis, and AN Venetsanopoulos. A kernel machine based approach
for multi-view face recognition. In Proc. of the International Conference on Image
Processing (ICIP’02), volume 1, pages 265–268, 2002.
http://dx.doi.org/10.1109/ICIP.2002.1038010.
[439] J Lu, KN Plataniotis, and AN Venetsanopoulos. Face recognition using kernel direct
discriminant analysis algorithms. IEEE Trans. on Neural Networks, 14:117–126, 2003.
http://dx.doi.org/10.1109/TNN.2002.806629.
[440] J Lu, KN Plataniotis, and AN Venetsanopoulos. Face recognition using LDA-based
algorithms. IEEE Trans. on Neural Networks, 14:195–200, 2003.
http://dx.doi.org/10.1109/TNN.2002.806647.
[441] X Lu, AK Jain, and D Colbry. Matching 2.5D face scans to 3d models. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 28:31–43, 2006.
http://dx.doi.org/10.1109/TPAMI.2006.15.
[442] CL Luengo Hendriks, M van Ginkel, PW Verbeek, and LJ van Vliet. The generalized
Radon transform: Sampling, accuracy and memory considerations. Pattern Recognition,
38:2494–2505, 2005.
http://dx.doi.org/10.1016/j.patcog.2005.04.018.
[443] J Luettin, NA Thacker, and SW Beet. Locating and tracking facial speech features.
In Proc. of the 13th IAPR International Conference on Pattern Recognition (ICPR’96),
volume 1, pages 652–656, 1996.
http://dx.doi.org/10.1109/ICPR.1996.546105.
[444] LM Lui, Y Wang, F Chan, and PM Thompson. Automatic landmark tracking and
its application to the optimization of brain conformal mapping. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’06), volume 2, pages
1784–1792, 2006.
http://dx.doi.org/10.1109/CVPR.2006.67.
[445] J MacLean and J Tsotsos. Fast pattern recognition using gradient-descent search
in an image pyramid. In Proc. of the 15th IAPR International Conference on Pattern
Recognition (ICPR’00), volume 2, pages 873–877, 2000.
http://dx.doi.org/10.1109/ICPR.2000.906213.
[446] A Mahalanobis and D Casasent. Performance evaluation of minimum average correlation energy filters. Applied Optics, 30:561–572, 1991.
[447] A Mahalanobis, BVKV Kumar, and D Casasent. Minimum average correlation energy filters. Applied Optics, 26(17):3633–3640, 1987.
[448] D Maio and D Maltoni. Real-time face location on gray-scale static images. Pattern
Recognition, 33:1525–1539, 2000.
http://dx.doi.org/10.1016/S0031-3203(99)00130-2.
[449] U Mansmann, M Ruschhaupt, and W Huber. Reproducible statistical analysis in
microarray profiling studies. In Proc of the 7th International Conference on Applied
Parallel Computing (PARA’04), volume 3732 of Lecture Notes in Computer Science, pages
939–948. Springer, 2004.
http://dx.doi.org/10.1007/11558958_114.
[450] A-R Mansouri, DP Mukherjee, and ST Acton. Constraining active contour evolution
via lie groups of transformation. IEEE Trans. on Image processing, 13:853–863, 2004.
http://dx.doi.org/10.1109/TIP.2004.826128.
[451] KV Mardia, W Qian, D Shah, and KMA de Souza. Deformable template recognition
of multiple occluded objects. IEEE Trans. on Pattern Analysis and Machine Intelligence,
19:1035–1042, 1997.
http://dx.doi.org/10.1109/34.615452.
[452] R Marfil, A Bandera, JA Rodriguez, and F Sandoval. Real-time template-based
tracking of non-rigid objects using bounded irregular pyramids. In Proc. of the IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS’04), volume 1, pages
301–306, 2004.
http://dx.doi.org/10.1109/IROS.2004.1389368.
[453] A Margalit and A Rosenfeld. Using feature probabilities to reduce the expected
computational cost of template matching. Computer Vision, Graphics and Image
Processing, 52:110–123, 1990.
http://dx.doi.org/10.1016/0734-189X(90)90125-F.
[454] RA Maronna and RH Zamar. Robust estimates of location and dispersion for
high-dimensional datasets. Technometrics, 44:307–317, 2002.
http://dx.doi.org/10.1198/004017002188618509.
[455] D Marr. Vision. W.H. Freeman, 1982.
[456] SR Marschner, SH Westin, EPF Lafortune, KE Torrance, and DP Greenberg. Image-based BRDF measurement including human skin. In Proc. of the Eurographics Workshop on Rendering, pages 139–152, 1999.
[457] AM Martinez. Recognizing imprecisely localized, partially occluded, and expression
variant faces from a single sample per class. IEEE Trans. on Pattern Analysis and Machine
Intelligence, 24:748–763, 2002.
http://dx.doi.org/10.1109/TPAMI.2002.1008382.
[458] AM Martinez and AC Kak. PCA versus LDA. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 23:228–233, 2001.
http://dx.doi.org/10.1109/34.908974.
[459] TL Marzetta and LA Shepp. A surprising Radon transform result and its application
to motion detection. IEEE Trans. on Image processing, 8:1039–1049, 1999.
http://dx.doi.org/10.1109/83.777085.
[460] L Mason, J Baxter, P Bartlett, and M Frean. Boosting algorithms as gradient descent. In Proc. of Advances in Neural Information Processing Systems, pages 512–518, 1999.
[461] Mathematical Society of Japan. Encyclopedic Dictionary of Mathematics. MIT Press, 2 edition, 1993.
[462] ME Mavroforakis and S Theodoridis. A geometric approach to support vector
machine (SVM) classification. IEEE Trans. on Neural Networks, 17:671–682, 2006.
http://dx.doi.org/10.1109/TNN.2006.873281.
[463] B McCane, K Novins, D Crannitch, and B Galvin. On benchmarking optical flow.
Computer Vision and Image Understanding, 84:126–143, 2001.
http://dx.doi.org/10.1006/cviu.2001.0930.
[464] C Menard. Robust Stereo and Adaptive Matching in Correlation Scale-Space. PhD thesis, Institute of Automation, Vienna University of Technology, 1997.
[465] AP Mendonca and EAB da Silva. Two-dimensional discriminative filters for image
template detection. In Proc. of the International Conference on Image Processing
(ICIP’01), volume 3, pages 680–683, 2001.
http://dx.doi.org/10.1109/ICIP.2004.1421503.
[466] AP Mendonca and EAB da Silva. Multiple template detection using impulse
restoration and discriminative filters. Electronic Letters, 39:1172–1174, 2003.
http://dx.doi.org/10.1049/el:20030793.
[467] AP Mendonca and EAB da Silva. Reduced cross-discrimination for discriminative
filters. In Proc. of the International Conference on Image Processing (ICIP’04), volume 3,
pages 2115–2118, 2004.
http://dx.doi.org/10.1109/ICIP.2004.1421503.
[468] RM Mersereau. The processing of hexagonally sampled two-dimensional signals. Proceedings of the IEEE, 67:930–949, 1979.
[469] G Metta, A Gasteratos, and G Sandini. Learning to track colored objects with
log-polar vision. Mechatronics, 14:989–1006, 2004.
http://dx.doi.org/10.1016/j.mechatronics.2004.05.003.
[470] M Meytlis and L Sirovich. On the dimensionality of face space. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 29:1262–1267, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.1033.
[471] R Milanese, M Cherbuliez, and T Pun. Invariant content-based image retrieval using the Fourier-Mellin transform. In Proc. of the International Conference on Advances in Pattern Recognition, pages 73–82, 1998.
[472] P Milanfar. Two-dimensional matched filtering for motion estimation. IEEE Trans.
on Image processing, 8:438–444, 1999.
http://dx.doi.org/10.1109/83.748900.
[473] N Mingtian and SE Reichenbach. Pattern matching by sequential subdivision of
transformation space. In Proc. of the 17th IAPR International Conference on Pattern
Recognition (ICPR’04), volume 2, pages 145–148, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334082.
[474] TP Minka. Automatic choice of dimensionality for PCA. In Proc. of Advances in Neural Information Processing Systems, volume 13, pages 598–604, 2001.
[475] T Mita, T Kaneko, and O Hori. A probabilistic approach to fast and robust template
matching and its application to object categorization. In Proc. of the 18th IAPR
International Conference on Pattern Recognition (ICPR’06), volume 2, pages 597–601,
2006.
http://dx.doi.org/10.1109/ICPR.2006.153.
[476] A Mittal and V Ramesh. An intensity-augmented ordinal measure for visual
correspondence. In Proc. of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR’06), volume 1, pages 849–856, 2006.
http://dx.doi.org/10.1109/CVPR.2006.56.
[477] I Mizera and C Muller. Breakdown points of Cauchy regression-scale estimators.
Statistics & Probability Letters, 57:79–89, 2002.
http://dx.doi.org/10.1016/S0167-7152(02)00057-3.
[478] B Moghaddam, T Jebara, and A Pentland. Bayesian face recognition. Pattern Recognition, 33:1771–1782, 2000.
[479] B Moghaddam and A Pentland. Probabilistic visual learning for object recognition. In Proc. of the 5th International Conference on Computer Vision and Pattern Recognition (ICCV’95), pages 786–793, 1995.
[480] B Moghaddam, W Wahid, and A Pentland. Beyond eigenfaces: probabilistic
matching for face recognition. In Proc. of the 3rd International Conference on Automatic
Face and Gesture Recognition (FG’98), pages 30–35, 1998.
http://dx.doi.org/10.1109/AFGR.1998.670921.
[481] B Moghaddam and M-H Yang. Learning gender with support faces. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 24:707–711, 2002.
http://dx.doi.org/10.1109/34.1000244.
[482] E Montiel, AS Aguado, and MS Nixon. On resolving ambiguities in arbitrary-shape extraction by the Hough transform. In Proc. of the British Machine Vision Conference (BMVC’00), 2000.
[483] DM Mount, NS Netanyahu, and J LeMoigne. Efficient algorithms for robust feature
matching. Pattern Recognition, 32:17–38, 1999.
http://dx.doi.org/10.1016/S0031-3203(98)00086-7.
[484] X Mu, M Artiklar, MH Hassoun, and P Watta. Training algorithms for robust face
recognition using a template-matching approach. In Proc. of the International Joint
Conference on Neural Networks, volume 4, pages 2877–2882, 2001.
http://dx.doi.org/10.1109/IJCNN.2001.938833.
[485] ST Mueller and J Zhang. Upper and lower bounds of area under ROC curves and index of discriminability of classifier performance. In Proc. of the ICML 2006 Workshop on ROC Analysis in Machine Learning, pages 41–46, 2006.
[486] S Mukherji. Fast algorithms for binary cross-correlation. In Proc. of the IEEE
International Geoscience and Remote Sensing Symposium, volume 1, pages 340–343,
2005.
http://dx.doi.org/10.1109/IGARSS.2005.1526177.
[487] C Muller. Redescending M-estimators in regression analysis, cluster analysis and image analysis. Discussiones Mathematicae - Probability and Statistics, 24:59–75, 2004.
[488] N Muller and B Herbst. On the use of SDF-type filters for distortion invariant image
location. In Proc. of the 15th IAPR International Conference on Pattern Recognition
(ICPR’00), volume 3, pages 526–529, 2000.
http://dx.doi.org/10.1109/ICPR.2000.903599.
[489] C Mungi, NP Galatsanos, and D Schonfeld. On the relation of image restoration
and template matching: application to block-matching motion estimation. In Proc. of the
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’96),
volume 4, pages 2112–2115, 1996.
http://dx.doi.org/10.1109/ICASSP.1996.545732.
[490] H Murase and SK Nayar. Illumination planning for object recognition using
parametric eigenspaces. IEEE Trans. on Pattern Analysis and Machine Intelligence,
16:1219–1227, 1994.
http://dx.doi.org/10.1109/34.387485.
[491] S Nagashima, T Aoki, T Higuchi, and K Kobayashi. A subpixel image matching technique using phase-only correlation. In Proc. of the International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS2006), pages 701–704, 2006.
[492] K Nandakumar, C Yi, SC Dass, and AK Jain. Likelihood ratio based biometric score
fusion. IEEE Trans. on Pattern Analysis and Machine Intelligence, 30:342–347, 2008.
http://dx.doi.org/10.1109/TPAMI.2007.70796.
[493] D Nandy and J Ben-Arie. EXM eigen templates for detecting and classifying arbitrary
junctions. In Proc. of the International Conference on Image Processing (ICIP’98),
volume 1, pages 211–215, 1998. Feature templates.
http://dx.doi.org/10.1109/ICIP.1998.723459.
[494] I Naseem and M Deriche. Robust human face detection in complex color images. In
Proc. of the International Conference on Image Processing (ICIP’05), volume 2, pages
338–341, 2005.
http://dx.doi.org/10.1109/10.1109/ICIP.2005.1530061.
[495] S Nassif and D Capson. Real-time template matching using cooperative windows.
In Proc. of the IEEE Canadian Conference on Electrical and Computer Engineering,
volume 2, pages 391–394, 1997.
http://dx.doi.org/10.1109/CCECE.1997.608240.
[496] SK Nayar and V Branzoi. Adaptive dynamic range imaging: Optical control of pixel
exposures over space and time. Proc. of the 9th International Conference on Computer
Vision and Pattern Recognition (ICCV’03), 2:1168–1175, 2003.
http://dx.doi.org/10.1109/ICCV.2003.1238624.
[497] SK Nayar, V Branzoi, and TE Boult. Programmable imaging: Towards a flexible
camera. Int. J. of Computer Vision, 70:7–22, 2006.
http://dx.doi.org/10.1007/s11263-005-3102-6.
[498] CJ Needham and RD Boyle. Multi-resolution template kernels. In Proc. of the
17th IAPR International Conference on Pattern Recognition (ICPR’04), volume 2, pages
233–236, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334138.
[499] J Ng and H Cheung. Biometric Authentication, volume 3072 of Lecture Notes in Computer Science, chapter Dynamic Local Feature Analysis for Face Recognition. Springer, 2004.
[500] DCL Ngo, ABJ Teoh, and A Goh. Biometric hash: high-confidence face recognition.
IEEE Trans. on Circuits and Systems for Video Technology, 16:771–775, 2006.
http://dx.doi.org/10.1109/TCSVT.2006.873780.
[501] W Niblack and D Petkovic. On improving the accuracy of the Hough transform.
Machine Vision and Applications, 3:87–106, 1990.
http://dx.doi.org/10.1007/BF01212193.
[502] J Nicolas, MJ Yzuel, and J Campos. Colour pattern recognition by three-dimensional
correlation. Optics Communications, 184:335–343, 2000.
http://dx.doi.org/10.1016/S0030-4018(00)00953-6.
[503] K Nishino and SK Nayar. Corneal imaging system: Environment from eyes. Int. J.
of Computer Vision, 70:23–40, 2006.
http://dx.doi.org/10.1007/s11263-006-6274-9.
[504] K Nishino, SK Nayar, and T Jebara. Clustered blockwise PCA for representing visual
data. IEEE Trans. on Pattern Analysis and Machine Intelligence, 27:1675–1679, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.193.
[505] R Nohre. Deformed template matching by the Viterbi algorithm. Pattern Recognition
Letters, 17:1423–1428, 1996.
http://dx.doi.org/10.1016/S0167-8655(96)00107-9.
[506] LM Novak, GJ Owirka, and CM Netishen. Radar target identification using spatial
matched filters. Pattern Recognition, 27:607–617, 1994.
http://dx.doi.org/10.1016/0031-3203(94)90040-X.
[507] M Ohlsson, C Peterson, and A Yuille. Track finding with deformable templates - the
elastic arms approach. Computer Physics Communications, 71:77–98, 1992.
http://dx.doi.org/10.1016/0010-4655(92)90074-9.
[508] H Okuda, M Hashimoto, and K Sumi. Robust picture matching using optimum
selection of partial template. In Proc. of the 41st SICE Annual Conference (SICE’02),
volume 1, pages 550–552, 2002.
http://dx.doi.org/10.1109/SICE.2002.1195465.
[509] H Okuda, M Hashimoto, K Sumi, and S Kaneko. Optimum motion estimation
algorithm for fast and robust digital image stabilization. IEEE Trans. on Consumer
Electronics, 52:276–280, 2006. This is the latest paper on HDTM - we need to check if
previous references must be interted as well.
http://dx.doi.org/10.1109/SICE.2002.1195465.
[510] CF Olson. Improving the generalized Hough transform through imperfect grouping.
Image and Vision Computing, 16:627–634, 1998.
http://dx.doi.org/10.1016/S0262-8856(98)00083-3.
[511] CF Olson. Constrained Hough transforms for curve detection. Computer Vision and
Image Understanding, 73:329–345, 1999.
http://dx.doi.org/10.1006/cviu.1998.0728.
[512] CF Olson. A general method for geometric feature matching and model extraction.
Int. J. of Computer Vision, 45:39–54, 2001.
http://dx.doi.org/10.1023/A:1012317923177.
[513] CF Olson. Image registration by aligning entropies. In Proc. of the IEEE Conference
on Computer Vision and Pattern Recognition (CVPR’01), volume 2, pages 331–336, 2001.
http://dx.doi.org/10.1109/CVPR.2001.990979.
[514] CF Olson. Maximum-likelihood image matching. IEEE Trans. on Pattern Analysis
and Machine Intelligence, 24:853–857, 2002.
http://dx.doi.org/10.1109/TPAMI.2002.1008392.
[515] E Osuna, R Freund, and F Girosi. Training support vector machines: an application
to face detection. In Proc. of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR’97), pages 130–136, 1997.
http://dx.doi.org/10.1109/CVPR.1997.609310.
[516] AJ O’Toole, PJ Phillips, J Fang, J Ayyad, N Penard, and H Abdi. Face recognition
algorithms surpass humans matching faces over changes in illumination. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 29(9):1642–1646, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.1107.
[517] S Ouyang, Z Bao, and G-S Liao. Robust recursive least squares learning algorithm
for principal component analysis. IEEE Trans. on Neural Networks, 11:215–221, 2000.
http://dx.doi.org/10.1109/72.822524.
[518] Z Pan, G Healey, M Prasad, and B Tromberg. Face recognition in hyperspectral
images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 25:1552–1560, 2003.
http://dx.doi.org/10.1109/TPAMI.2003.1251148.
[519] Z Pan, AG Rust, and H Bolouri. Image redundancy reduction for neural network
classification using discrete cosine transforms. In Proc. of the International Joint
Conference on Neural Networks, volume 3, pages 149–154, 2000.
http://dx.doi.org/10.1109/IJCNN.2000.861296.
[520] M Pantic, I Patras, and M Valstar. Learning spatiotemporal models of facial expressions. In Proc. of the International Conference Measuring Behaviour, pages 7–10, 2005.
[521] M Pantic and L Rothkrantz. Facial action recognition for facial expression analysis
from static face images. IEEE Trans. on Systems, Man and Cybernetics, Part B,
34:1449–1461, 2004.
http://dx.doi.org/10.1109/TSMCB.2004.825931.
[522] G Papandreou and P Maragos. Multigrid geometric active contour models. IEEE
Trans. on Image processing, 16:229–240, 2007.
http://dx.doi.org/10.1109/TIP.2006.884952.
[523] A Papoulis. Probability, Random Variables and Stochastic Processes. McGraw-Hill, 1965.
[524] CH Park and H Park. A comparison of generalized linear discriminant analysis
algorithms. Pattern Recognition, 41:1083–1097, 2008.
http://dx.doi.org/10.1016/j.patcog.2007.07.022.
[525] JH Park, Z Zhang, H Zha, and R Kasturi. Local smoothing for manifold learning. In
Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’04),
volume 2, pages 452–459, 2004.
http://dx.doi.org/10.1109/CVPR.2004.1315199.
[526] S-C Park, S-H Lim, B-K Sin, and S-W Lee. Tracking non-rigid objects using
probabilistic Hausdorff distance matching. Pattern Recognition, 38:2373–2384, 2005.
http://dx.doi.org/10.1016/j.patcog.2005.01.015.
[527] R Patnaik and D Casasent. Illumination invariant face recognition and impostor
rejection using different MINACE filter algorithms. In Proc. of SPIE, volume 5816, pages
94–104, 2005.
http://dx.doi.org/10.1117/12.603060.
[528] MP Patricio, F Cabestaing, O Colot, and P Bonnet. A similarity-based adaptive
neighborhood method for correlation-based stereo matching. In Proc. of the International
Conference on Image Processing (ICIP’04), volume 2, pages 1341–1344, 2004.
http://dx.doi.org/10.1109/ICIP.2004.1419747.
[529] PS Penev. Redundancy and dimensionality reduction in sparse-distributed representations of natural objects in terms of their local features. In Proc. of Advances in Neural Information Processing Systems, volume 13, pages 901–907, 2001.
[530] PS Penev and L Sirovich. The global dimensionality of face space. In Proc. of the
4th International Conference on Automatic Face and Gesture Recognition (FG’00), pages
264–273, 2000.
http://dx.doi.org/10.1109/AFGR.2000.840645.
[531] S. Perreault and P Hebert. Median filtering in constant time. IEEE Trans. on Image
processing, 16:2389–2394, 2007.
http://dx.doi.org/10.1109/TIP.2007.902329.
[532] M Petrou and A Kadyrov. Affine invariant features from the trace transform. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 26:30–44, 2004.
http://dx.doi.org/10.1109/TPAMI.2004.1261077.
[533] PJ Phillips, PJ Flynn, T Scruggs, KW Bowyer, C Jin, K Hoffman, J Marques,
M Jaesik, and W Worek. Overview of the face recognition grand challenge. In Proc. of the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1,
pages 947–954, 2005.
http://dx.doi.org/10.1109/CVPR.2005.268.
[534] PJ Phillips, PJ Flynn, T Scruggs, KW Bowyer, and W Worek. Preliminary face
recognition grand challenge results. In Proc. of the 7th International Conference on
Automatic Face and Gesture Recognition (FG’06), pages 15–24, 2006.
http://dx.doi.org/10.1109/FGR.2006.87.
[535] PJ Phillips, P Grother, R Micheals, Dm Blackburne, E Tabassi, and M Bone. Face Recognition Vendor Test 2002 Evaluation Report. Technical Report NISTIR 6965, National Institute of Standards and Technology, 2003.
[536] PJ Phillips, M Hyeonjoon, SA Rizvi, and PJ Rauss. The FERET evaluation
methodology for face-recognition algorithms. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 22:1090–1104, 2000.
http://dx.doi.org/10.1109/34.879790.
[537] PJ Phillips, A Martin, CL Wilson, and M Przybocki. An introduction to evaluating
biometric systems. IEEE Computer, 33:56–63, 2000.
http://dx.doi.org/10.1109/2.820040.
[538] PJ Phillips, WT Scruggs, AJ O’Toole, PJ Flynn, KW Bowyer, CL Schott, and M Sharpe. FRVT 2006 and ICE 2006 large-scale results. Technical Report NISTIR 7408, National Institute of Standards and Technology, 2007.
[539] JPW Pluim, JBA Maintz, and MA Viergever. Mutual-information-based registration
of medical images: a survey. IEEE Trans. on Medical Imaging, 22:986–1004, 2003.
http://dx.doi.org/10.1109/TMI.2003.815867.
[540] T. Poggio and R. Brunelli. A novel approach to graphics. A.I. Memo No. 1354, Massachusetts Institute of Technology, 1992.
[541] T Poggio and F Girosi. Regularization algorithms for learning that are equivalent to
multilayer networks. Science, 247:978–982, 1990.
http://dx.doi.org/10.1126/science.247.4945.978.
[542] T Poggio and F Girosi. Notes on PCA, regularization, sparsity and support vector machines. Technical Report A.I.Memo-1632, MIT Artificial Intelligence Laboratory, 1998.
[543] T Poggio and S Smale. The mathematics of learning: dealing with data. Notices of the AMS, 50:537–544, 2003.
[544] HV Poor. Robust matched filters. IEEE Trans. on Information Theory, 29:677–687, 1983.
[545] V Popovici. Kernel-based classifiers with applications to face detection. PhD thesis, Ecole Polytechnique Federale de Lausanne, 2004.
[546] V Popovici, J Thiran, Y Rodriguez, and S Marcel. On performance evaluation of
face detection and localization algorithms. In Proc. of the 17th IAPR International
Conference on Pattern Recognition (ICPR’04), volume 1, pages 313–317, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334115.
[547] V Popovici and JP Thiran. Pattern recognition using higher-order local
autocorrelation coefficients. Pattern Recognition Letters, 25:1107–1113, 2004.
http://dx.doi.org/10.1016/j.patrec.2004.03.007.
[548] WH Press, SA Teukolsky, WT Vetterling, and BP Flannery. Numerical Recipes. Cambridge University Press, 3rd edition, 2007.
[549] JP Princen, J Illingworth, and JV Kittler. A formal definition of the Hough
transform: Properties and relationships. J. of Mathematical Imaging and Vision,
1:153–168, 1992.
http://dx.doi.org/10.1007/BF00122210.
[550] JP Princen, J Illingworth, and JV Kittler. Hypothesis testing: A framework for
analyzing and optimizing Hough transform performance. IEEE Trans. on Pattern Analysis
and Machine Intelligence, 16:329–341, 1994.
http://dx.doi.org/10.1109/34.277588.
[551] F Provost and T Fawcett. Robust classification for imprecise environments. Machine
Learning, 42:203–231, 2001.
http://dx.doi.org/10.1023/A:1007601015854.
[552] A Pujol, J Vitria, F Lumbreras, and JJ Villanueva. Topological principal component
analysis for face encoding and recognition. Pattern Recognition Letters, 22:769–776, 2001.
http://dx.doi.org/10.1016/S0167-8655(01)00027-7.
[553] RJ Pumphrey. The theory of the fovea. J. of Experimental Biology, 25:299–312, 1948.
[554] F Qureshi and D Terzopoulos. Surveillance camera scheduling: a virtual vision approach. Multimedia Systems, 12:269–283, 2006.
[555] F Qureshi and D Terzopoulos. Smart camera networks in virtual reality. Proceedings of the IEEE, 2008.
[556] J Radon. Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Ber. Verh. Sächs. Akad., 69:262–277, 1917.
[557] AN Rajagopalan, R Chellappa, and NT Koterba. Background learning for robust
face recognition with PCA in the presence of clutter. IEEE Trans. on Image processing,
14:832–843, 2005.
http://dx.doi.org/10.1109/TIP.2005.847288.
[558] KR Rao and J Ben-Arie. Edge detection and feature extraction by non-orthogonal
image expansion for optimal discriminative SNR. In Proc. of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR’93), pages 791–792, 1993.
http://dx.doi.org/10.1109/CVPR.1993.341178.
[559] KR Rao and J Ben-Arie. Multiple template matching using the expansion filter.
IEEE Trans. on Circuits and Systems for Video Technology, 4:490–503, 1994.
http://dx.doi.org/10.1109/76.322996.
[560] KR Rao and J Ben-Arie. Optimal edge detection using expansion matching and
restoration. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16:1169–1182,
1994.
http://dx.doi.org/10.1109/34.387490.
[561] YN Rao and JC Principe. Robust on-line principal component analysis based on a
fixed-point approach. In Proc. of the IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP’02), volume 1, pages 981–984, 2002.
http://dx.doi.org/10.1109/ICASSP.2002.1005906.
[562] AL Ratan, WEL Grimson, and WM Wells. Object detection and localization by
dynamic template warping. Int. J. of Computer Vision, 36:131–147, 2000.
http://dx.doi.org/10.1023/A:1008147915077.
[563] G Ravichandran and D Casasent. Minimum noise and correlation energy optical correlation filter. Applied Optics, 31:1823–1833, 1992.
[564] G Ravichandran and D Casasent. Advanced in-plane rotation-invariant correlation
filters. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16:415–420, 1994.
http://dx.doi.org/10.1109/34.277595.
[565] BS Reddy and BN Chatterji. An fft-based technique for translation, rotation, and
scale-invariant image registration. IEEE Trans. on Image processing, 5:1266–1271, 1996.
http://dx.doi.org/10.1109/83.506761.
[566] AP Reeves. A systematically designed binary array processor. IEEE Trans. on Computers, C-29:278–287, 1980.
[567] WJJ Rey. Robust statistical methods, volume 690 of Lecture Notes in Mathematics. Springer, 1978.
[568] J Robinson. Covariance matrix estimation for appearance-based face image processing. In Proc. of the British Machine Vision Conference (BMVC’05), volume 1, pages 389–398, 2005.
[569] Y Rodriguez, F Carinaux, S Bengio, and J Mariethoz. Measuring the performance
of face localization systems. Image and Vision Computing, 224:882–893, 2006.
http://dx.doi.org/10.1016/j.imavis.2006.02.012.
[570] Y Rodriguez and S Marcel. Face authentication using adapted local binary pattern
histograms. In Proc. of the 9th European Conference on Computer Vision (ECCV’06),
pages 321–332, 2006.
http://dx.doi.org/10.1007/11744085_25.
[571] AS Rojer and EL Schwartz. Design considerations for a space-variant visual sensor
with complex-logarithmic geometry. In Proc. of the 15th IAPR International Conference
on Pattern Recognition (ICPR’00), volume 2, pages 278–285, 1990.
http://dx.doi.org/10.1109/ICPR.1990.119370.
[572] S Romdhani, J Ho, T Vetter, and DJ Kriegman. Face recognition using 3-D models:
Pose and illumination. Proceedings of the IEEE, 94:1977–1999, 2006.
http://dx.doi.org/10.1109/JPROC.2006.886019.
[573] S Romdhani, P Torr, B Scholkopf, and A Blake. Computationally efficient face
detection. In Proc. of the 8th International Conference on Computer Vision and Pattern
Recognition (ICCV’01), pages 695–700, 2001.
http://dx.doi.org/10.1109/ICCV.2001.937694.
[574] A Roorda and DR Williams. The arrangement of the three cone classes in the living human retina. Nature, 397:520–522, 1999.
[575] AJ Rossini and F Leisch. Literate statistical practice. UW Biostatistics Working Paper Series 194, University of Washington, WA, Usa, 2003.
[576] J Rubinstein, J Segman, and Y Zeevi. Recognition of distorted patterns by invariance
kernels. Pattern Recognition, 24:959–967, 1991.
http://dx.doi.org/10.1016/0031-3203(91)90093-K.
[577] W Rucklidge. Efficient guaranteed search for gray-level patterns. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’97), pages 717–723,
1997.
http://dx.doi.org/10.1109/CVPR.1997.609405.
[578] TD Russ, MW Koch, and CQ Little. A 2D range Hausdorff approach for 3D face
recognition. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’05), volume 3, page 169, 2005.
http://dx.doi.org/10.1109/CVPR.2005.561.
[579] J Sadr, S Mukherjee, K Thoresz, and P Sinha. The fidelity of local ordinal encoding. In Proc. of Advances in Neural Information Processing Systems, volume 14, pages 1279–1286, 2002.
[580] H Sahbi and D Geman. A hierarchy of support vector machines for pattern detection. J. of Machine Learning Research, 7:2087–2123, 2006.
[581] H Sahbi, D Geman, and N Boujemaa. Face detection using coarse-to-fine support
vectors classifiers. In Proc. of the International Conference on Image Processing (ICIP’02),
volume 3, pages 925–928, 2002.
http://dx.doi.org/10.1109/ICIP.2002.1039124.
[582] K Saitwal, AA Maciejewski, and RG Roberts. Fast eigenspace decomposition of
correlated images using their low-resolution properties. In Proc. of the IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS’04), volume 3, pages
2707–2712, 2004.
http://dx.doi.org/10.1109/IROS.2004.1389818.
[583] K Saitwal, AA Maciejewski, RG Roberts, and BA Draper. Using the low-resolution
properties of correlated images to improve the computational efficiency of eigenspace
decomposition. IEEE Trans. on Image processing, 15:2376–2387, 2006.
http://dx.doi.org/10.1109/TIP.2006.875231.
[584] G Sandini, J Santos-Victor, T Pajdla, and F Berton. Omniviews: direct
omnidirectional imaging based on a retina-like sensor. In Proc. of IEEE Sensors, volume 1,
pages 27–30, 2002.
http://dx.doi.org/10.1109/ICSENS.2002.1036981.
[585] A Santuari, O Lanz, and R Brunelli. Synthetic movies for computer vision applications. In 3rd IASTED International Conference: Visualization, Imaging, and Image Processing - VIIP 2003, pages 1–6, 2003.
[586] A. Santuari, O. Lanz, and R. Brunelli. Synthetic Movies for Computer Vision Applications. In 3rd IASTED International Conference: Visualization, Imaging, and Image Processing - VIIP 2003, pages 1–6, 2003.
[587] M Savvides, BVKV Kumar, and PK Khosla. Eigenphases vs eigenfaces. In Proc. of
the 17th IAPR International Conference on Pattern Recognition (ICPR’04), volume 3,
pages 810–813, 2004.
http://dx.doi.org/10.1109/ICPR.2004.1334652.
[588] RE Schapire, Y Freund, P Bartlett, and WS Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. In Proc. of the 14th International Conference on Machine Learning, 1997.
[589] RE Schapire, Y Freund, P Bartlett, and WS Lee. Boosting the margin: A new
explanation for the effectiveness of voting methods. Annals of Statistics, 26:1651–1686,
1998.
http://dx.doi.org/10.1214/aos/1024691352.
[590] S Scherer, P Werth, and A Pinz. The discriminatory power of ordinal measures -
towards a new coefficient. In Proc. of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR’99), volume 1, pages 1076–1081, 1999.
http://dx.doi.org/10.1109/CVPR.1999.786920.
[591] B Scholkopf, C Burges, and V Vapnik. Incorporating invariances in support vector machines. In Proceedings of the International Conference on Artificial Neural Networks, volume 1112 of Lecture Notes in Computer Science, pages 47–52. Springer, 1996.
[592] B Scholkopf, A Smola, and KR Muller. Nonlinear component analysis as a kernel
eigenvalue problem. Neural Computation, 10:1299–1319, 1998.
http://dx.doi.org/10.1162/089976698300017467.
[593] B Scholkopf and AJ Smola. Learning with Kernels. The MIT Press, 2002.
[594] D Schonfeld. On the relation of order-statistics filters and template matching: optimal
morphological pattern recognition. IEEE Trans. on Image processing, 9:945–949, 2000.
http://dx.doi.org/10.1109/83.841540.
[595] S Schuster and S Amtsfeld. Template-matching describes visual pattern-recognition tasks in the weakly electric fish gnathonemus petersii. jeb, 205:549–557, 2002.
[596] N Sebe, MS Lew, and DP Huijsmans. Toward improved ranking metrics. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 22:1132–1143, 2000.
http://dx.doi.org/10.1109/34.879793.
[597] AK Seghouane and A Cichocki. Bayesian estimation of the number of principal
components. Signal Processing, 87:562–568, 2006.
http://dx.doi.org/10.1016/j.sigpro.2006.09.001.
[598] J Segman, J Rubinstein, and YY Zeevi. The canonical coordinates method for
pattern deformation: theoretical and computational considerations. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 14:1171–1183, 1992.
http://dx.doi.org/10.1109/34.177382.
[599] S Serneels and T Verdonck. Principal component analysis for data containing outliers
and missing elements. Computational Statistics and Data Analysis, 52:1712–1727, 2008.
http://dx.doi.org/10.1016/j.csda.2007.05.024.
[600] J Serra. Introduction to mathematical morphology. Computer Vision, Graphics and
Image Processing, 35:283–305, 1986.
http://dx.doi.org/10.1016/0734-189X(86)90002-2.
[601] OG Sezer, Y Altunbasak, and A Ercil. Face recognition with independent
component-based super-resolution. In Visual Communications and Image Processing,
volume 6077 of Proc. of SPIE, 2006.
http://dx.doi.org/10.1117/12.645868.
[602] B-Z Shaick and LP Yaroslavsky. Object localization using linear adaptive filters. In Proc. of the Vision Modeling and Visualization Conference, pages 11–18, 2001.
[603] G Shakhnarovich and B Moghaddam. Handbook of Face Recognition, chapter Face Recognition in Subspaces. Springer-Verlag, 2004.
[604] G Shama and HJ Trussell. Digital color imaging. IEEE Trans. on Image processing, 6:901–932, 1997.
[605] T Shan, BC Lovell, and S Chen. Face recognition robust to head pose from one
sample image. In Proc. of the 18th IAPR International Conference on Pattern Recognition
(ICPR’06), volume 1, pages 515–518, 2006.
http://dx.doi.org/10.1109/ICPR.2006.527.
[606] VA Shapiro and VH Ivanov. Real-time Hough/Radon transform: algorithm and
architectures. In Proc. of the International Conference on Image Processing (ICIP’94),
volume 3, pages 630–634, 1994.
http://dx.doi.org/10.1109/ICIP.1994.413813.
[607] A Shashua, A Levin, and S Avidan. Manifold pursuit: a new approach to appearance
based recognition. In Proc. of the 16th IAPR International Conference on Pattern
Recognition (ICPR’02), volume 3, pages 590–594, 2002.
http://dx.doi.org/10.1109/10.1109/ICPR.2002.1048008.
[608] BV Sheela, C Rajagopal, and K Padmanabhan. Tempo: Template matching by
parametric optimization. Pattern Recognition Letters, 14:65–69, 1993.
http://dx.doi.org/10.1016/0167-8655(93)90133-X.
[609] GL Shevlyakov and NO Vilchevski. Minimax variance estimation of a correlation
coefficient for ϵ-contaminated bivariate normal distributions. Statistics and Probability
Letters, 57:91–100, 2002.
http://dx.doi.org/10.1016/S0167-7152(02)00058-5.
[610] L Shitu, Z Qi, L Feilu, and W Yanling. An improved correlation tracking algorithm
based on adaptive template modification. In Proc. of the International Conference on
Information Acquisition, pages 313–315, 2004.
http://dx.doi.org/10.1109/ICIA.2004.1373377.
[611] HY Shum, K Ikeuchi, and R Reddy. Principal component analysis with missing data
and its application to polyhedral object modeling. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 17:854–867, 1995.
http://dx.doi.org/10.1109/34.406651.
[612] BW Silverman. Density estimation for statistics and data analysis. Chapman and Hall, 1986.
[613] DG Sim, OK Kwon, and RH Park. Object matching algorithms using robust
Hausdorff distance measures. IEEE Trans. on Image processing, 8:425–429, 1999.
http://dx.doi.org/10.1109/83.748897.
[614] Singular Inversions. FaceGen Modeller 3.2. http://www.facegen.com, 2008.
[615] P Sinha. Qualitative representations for recognition. In Proc. of the 2nd International Workshop on Biologically Motivated Computer Vision, volume 2525 of Lecture Notes in Computer Science, pages 249–262. Springer, 2002.
[616] P Sinha, B Balas, Y Ostrovsky, and R Russell. Face recognition by humans:
Nineteen results all computer vision researchers should know about. Proceedings of the
IEEE, 94:1948–1962, 2006. Face Recognition.
http://dx.doi.org/10.1109/JPROC.2006.884093.
[617] L Sirovich and M Kirby. Low-dimensional procedure for the characterization of
human faces. J. of the Optical Society of America A, 4:519–524, 1987.
http://dx.doi.org/10.1364/JOSAA.4.000519.
[618] S Sista, CA Bouman, and JP Allebach. Fast image search using a multiscale
stochastic model. In Proc. of the International Conference on Image Processing (ICIP’95),
volume 2, pages 23–26, 1995.
http://dx.doi.org/10.1109/ICIP.1995.537455.
[619] J Sklansky. On the Hough technique for curve detection. IEEE Trans. on Computers,
C-27:923–926, 1978.
http://dx.doi.org/10.1109/TC.1978.1674971.
[620] D Skočaj and A Leonardis. Incremental and robust learning of subspace
representations. Image and Vision Computing, 26:27–38, 2008.
http://dx.doi.org/10.1016/j.imavis.2005.07.028.
[621] MB Skouson and ZP Liang. Template deformation constrained by shape priors. In
Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’00),
volume 1, pages 511–516, 2000.
http://dx.doi.org/10.1109/CVPR.2000.855862.
[622] F Smeraldi. A nonparametric approach to face detection using ranklets. In Proc. of the 4th International Conference on Audio-and Video-Based Biometric Person Authentication, volume 2688 of Lecture Notes in Computer Science, pages 351–359. Springer, 2003.
[623] M Soffer and N Kiryati. Guaranteed convergence of the Hough transform. Computer
Vision and Image Understanding, 69:119–134, 1998.
http://dx.doi.org/10.1006/cviu.1997.0557.
[624] S Srisuk and W Kurutach. New robust Hausdorff distance-based face detection. In
Proc. of the International Conference on Image Processing (ICIP’01), volume 1, pages
1022–1025, 2001.
http://dx.doi.org/10.1109/ICIP.2001.959222.
[625] S Srisuk, M Petrou, W Kurutach, and A Kadyrov. Face authentication using the
trace transform. In Proc. of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR’03), volume 1, pages 305–312, 2003.
http://dx.doi.org/10.1109/CVPR.2003.1211368.
[626] VV Starovoitov, C Kose, and B Sankur. Generalized distance based matching
of nonbinary images. In Proc. of the International Conference on Image Processing
(ICIP’98), volume 1, pages 803–807, 1998.
http://dx.doi.org/10.1109/ICIP.1998.723632.
[627] RS Stephens. Probabilistic approach to the Hough transform. Image and Vision
Computing, 9:66–71, 1991.
http://dx.doi.org/10.1016/0262-8856(91)90051-P.
[628] CV Stewart. Bias in robust estimation caused by discontinuities and multiple
structures. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19:818–833, 1997.
http://dx.doi.org/10.1109/34.608280.
[629] CV Stewart. Robust parameter estimation in computer vision. SIAM Review,
41:513–537, 1999.
http://dx.doi.org/10.1137/S0036144598345802.
[630] GC Stockman and AK Agrawala. Equivalence of Hough curve detection to template
matching. Communications of the ACM, 20:820–822, 1977.
http://dx.doi.org/10.1145/359863.359882.
[631] G Su, Y Shang, C Du, and J Wang. A multimodal and multistage face recognition
method for simulated portrait. In Proc. of the 18th IAPR International Conference on
Pattern Recognition (ICPR’06), volume 3, pages 1013–1017, 2006.
http://dx.doi.org/10.1109/ICPR.2006.108.
[632] T Su and JG Dy. Automated hierarchical mixtures of probabilistic principal
component analyzers. In Proc. of the International Conference on Machine Learning
(ICML’04), pages 98–105, 2004.
http://dx.doi.org/10.1145/1015330.1015393.
[633] SI Sudharsanan, A Mahalanobis, and MK Sundareshan. Unified framework for the
synthesis of synthetic discriminant functions with reduced noise variance and sharp
correlation structure. Optical Engineering, 29:1021–1028, 1990.
http://dx.doi.org/10.1117/12.55698.
[634] S Sun, H Park, DR Haynor, and Y Kim. Fast template matching using
correlation-based adaptive predictive search. Int. J. of Imaging Systems and Technology,
13:169–178, 2003.
http://dx.doi.org/10.1002/ima.10055.
[635] Y Sun, FD Fracchia, MS Drew, and TW Calvert. A spectrally based framework for
realistic image synthesis. The Visual Computer, 17:429–444, 2001.
http://dx.doi.org/10.1007/s003710100116.
[636] Z Sun, T Tan, and Y Wang. Robust encoding of local ordinal measures: A general framework of iris recognition. In Proc. of the ECCC’04 International Workshop on Biometric Authentication, volume 3087 of Lecture Notes in Computer Science, pages 270–282. Springer, 2004.
[637] Z Sun, T Tan, Y Wang, and SZ Li. Ordinal palmprint represention for personal
identification. In Proc. of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR’05), volume 1, pages 279–284, 2005.
http://dx.doi.org/10.1109/CVPR.2005.267.
[638] RW Swiniarski, HK Lim, JH Shin, and A Skowron. Independent component analysis, principal component analysis and rough sets in hybrid mammogram classification. In Proc. of the International Conference on Image Processing, Computer Vision, & Pattern Recognition, pages 640–645, 2006. There is also a ’Independent Component Analysis, Principal Component Analysis and Rough Sets in Face Recognition’.
[639] S Tabbone and L Wendling. Technical symbols recognition using the two-dimensional
Radon transform. In Proc. of the 16th IAPR International Conference on Pattern
Recognition (ICPR’02), volume 3, pages 200–203, 2002.
http://dx.doi.org/10.1109/ICPR.2002.1047829.
[640] Barnabás Takács. Comparing face images using the modified Hausdorff distance.
Pattern Recognition, 31:1873–1881, 1998.
http://dx.doi.org/10.1016/S0031-3203(98)00076-4.
[641] K Takita, T Aoki, Y Sasaki, T Higuchi, and K Kobayashi. High-accuracy subpixel image registration based on phase-only correlation. IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, E86A:1925–1934, 2003.
[642] X Tan, S Chen, ZZ Zhou, and F Zhang. Face recognition from a single image per
person: A survey. Pattern Recognition, 39:1725–1745, 2006.
http://dx.doi.org/10.1016/j.patcog.2006.03.013.
[643] K Tanaka, M Sano, S Ohara, and M Okudaira. A parametric template method and
its application to robust matching. In Proc. of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR’00), volume 1, pages 620–627, 2000.
http://dx.doi.org/10.1109/CVPR.2000.855877.
[644] JD Tebbens and P Schlesinger. Improving implementation of linear discriminant
analysis for the high dimension/small sample size problem. Computational Statistics and
Data Analysis, 52:423–437, 2007.
http://dx.doi.org/10.1016/j.csda.2007.02.001.
[645] A Thayananthan, R Navaratnam, PHS Torr, and R Cipolla. Likelihood models for template matching using the PDF projection theorem. In Proc. of the British Machine Vision Conference (BMVC’04), 2004.
[646] A Thayananthan, B Stenger, PHS Torr, and R Cipolla. Shape context and chamfer
matching in cluttered scenes. In Proc. of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR’03), volume 1, pages 127–133, 2003.
http://dx.doi.org/10.1109/CVPR.2003.1211346.
[647] The Aqsis Team. Aqsis v1.2. http://www.aqsis.org/, 2007.
[648] The Povray Team. The Persistence Of Vision Raytracer v3.6. http://www.povray.org/, 2008.
[649] PM Thompson and AW Toga. A framework for computational anatomy. Computing
and Visualization in Science, 5:13–34, 2002.
http://dx.doi.org/10.1007/s00791-002-0084-6.
[650] L Tian and SI Kamata. An efficient algorithm for point matching using Hilbert
scanning distance. In Proc. of the 18th IAPR International Conference on Pattern
Recognition (ICPR’06), volume 3, pages 873–876, 2006.
http://dx.doi.org/10.1109/ICPR.2006.237.
[651] YL Tian, T Kanade, and JF Cohn. Recognizing action units for facial expression
analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23:97–115, 2001.
http://dx.doi.org/10.1109/34.908962.
[652] ME Tipping and CM Bishop. Probabilistic principal component analysis. J. of the
Royal Statistical Society: Series B (Statistical Methodology), 61:611–622, 1999.
http://dx.doi.org/10.1111/1467-9868.00196.
[653] R Tjahyadi, L Wanquan, A Senjian, and S Venkatesh. Face recognition based on ordinal correlation approach. In Proc. of the International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP2005), pages 349–354, 2005.
[654] PA Toft. The Radon Transform - Theory and Implementation. PhD thesis, Department of Mathematical Modelling, Technical University of Denmark, 1996.
[655] PA Toft. Using the generalized Radon transform for detection of curves in noisy
images. In Proc. of the IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP’96), volume 4, pages 2219–2222, 1996.
http://dx.doi.org/10.1109/ICASSP.1996.545862.
[656] F Tombari, S Mattoccia, and L Di Stefano. Template matching based on the lp
norm using sufficient conditions with incremental approximations. In Proc. of the IEEE
International Conference on Video and Signal Based Surveillance (AVSS’06), pages 20–20,
2006.
http://dx.doi.org/10.1109/AVSS.2006.110.
[657] F Tombari, S Mattoccia, and L Di Stefano. Full search-equivalent pattern matching
with incremental dissimilarity approximations. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 31:129–141, 2009.
http://dx.doi.org/10.1109/TPAMI.2008.46.
[658] VJ Traver, A Bernardino, P Moreno, and J Santos-Victor. Appearance-based object
detection in space-variant images: A multi-model approach. In Proc. of the International
Conference on Image Analysis and Recognition, volume 3212 of Lecture Notes in Computer
Science, pages 538–546. Springer, 2004.
http://dx.doi.org/10.1007/b100437.
[659] VJ Traver and F Pla. Dealing with 2D translation estimation in log-polar imagery.
Image and Vision Computing, 21:145–160, 2003.
http://dx.doi.org/10.1016/S0262-8856(02)00150-6.
[660] B Triggs. Empirical filter estimation for subpixel interpolation and matching. In
Proc. of the 8th International Conference on Computer Vision and Pattern Recognition
(ICCV’01), volume 2, pages 550–557, 2001.
http://dx.doi.org/10.1109/ICCV.2001.937674.
[661] D-M Tsai and C-T Lin. Fast normalized cross correlation for defect detection. Pattern
Recognition Letters, 24:2625–2631, 2003.
http://dx.doi.org/10.1016/S0167-8655(03)00106-5.
[662] PH Tsai and T Jan. Expression-invariant face recognition system using subspace
model analysis. In Proc. of the IEEE International Conference on Systems, Man, and
Cybernetics, volume 2, pages 1712–1717, 2005.
http://dx.doi.org/10.1109/ICSMC.2005.1571395.
[663] VA Tucker. The deep fovea, sideways vision and spiral flight paths in raptors. J. of Experimental Biology, 203:3745–3754, 2000.
[664] M Turk and A Pentland. Eigenfaces for recognition. Journal of Cognitive
Neuroscience, 3:71–86, 1991.
http://dx.doi.org/10.1162/jocn.1991.3.1.71.
[665] K Turkowski. Graphic Gems, chapter Filters for Common Resampling Tasks. Academic Press, 1990.
[666] M Uenohara and T Kanade. Use of Fourier and Karhunen-Loeve decomposition for
fast pattern matching with a large set of templates. IEEE Trans. on Pattern Analysis
and Machine Intelligence, 19:891–898, 1997.
http://dx.doi.org/10.1109/34.608291.
[667] F Ullah and S Kaneko. Using orientation codes for rotation-invariant template
matching. Pattern Recognition, 37:201–209, 2004.
http://dx.doi.org/10.1016/S0031-3203(03)00184-5.
[668] R Unnikrishnan, C Pantofaru, and M Hebert. Toward objective evaluation of image
segmentation algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence,
29:929–944, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.1046.
[669] M Valstar, M Pantic, and I Patras. Motion history for facial action detection in
video. In Proc. of the IEEE International Conference on Systems, Man, and Cybernetics,
pages 635–640, 2004.
http://dx.doi.org/10.1109/ICSMC.2004.1398371.
[670] MF Valstar and M Pantic. Fully automatic facial action unit detection and temporal
analysis. In Proc. of the Conference on Computer Vision and Pattern Recognition
Workshop, page 149, 2006.
http://dx.doi.org/10.1109/CVPRW.2006.85.
[671] E Valveny and E Marti. Application of deformable template matching to symbol
recognition in handwritten architectural drawings. In Proc. of the 5th International
Conference on Document Analysis and Recognition, pages 483–486, 1999.
http://dx.doi.org/10.1109/ICDAR.1999.791830.
[672] M van Ginkel, CL Luengo Hendriks, and LJ van Vliet. A short introduction to the Radon and Hough transforms and how they relate to each other. Technical Report QI-2004-01, Delft Univeristy of Technology, 2004.
[673] M van Ginkel, MA Kraaijveld, LJ van Vliet, EP Reding, PW Verbeek, and HJ Lammers. Robust curve detection using a Radon transform in orientation space. In Proc. of the Scandinavian Conference on Image Analysis, pages 125–132, 2003.
[674] JD van Ouvwerkerk. Image super-resolution survey. Image and Vision Computing,
24:1039–1052, 2006.
http://dx.doi.org/10.1016/j.imavis.2006.02.026.
[675] JD van Ouwerkerk. Image super-resolution survey. Image and Vision Computing,
24:1039–1052, 2006.
http://dx.doi.org/10.1016/j.imavis.2006.02.026.
[676] TM van Veen and FCA Groen. Discretization errors in the Hough transform. Pattern
Recognition, 14:137–145, 1981.
http://dx.doi.org/10.1016/0031-3203(81)90055-8.
[677] T VanCourt, Y Gu, and MC Herbordt. Three-dimensional template correlation:
object recognition in 3D voxel data. In Proc. of the 7th International Workshop on
Computer Architecture for Machine Perception, pages 153–158, 2005.
http://dx.doi.org/10.1109/CAMP.2005.52.
[678] VN Vapnik. Statistical Learning Theory. Wiley, 1998.
[679] M Vatsa, R Singh, and P Gupta. Face recognition using multiple recognizers. In
Proc. of the IEEE International Conference on Systems, Man, and Cybernetics, volume 3,
pages 2186–2190, 2004.
http://dx.doi.org/10.1109/ICSMC.2004.1400652.
[680] PW Verbeek. A class of sampling-error free measures in oversampled band-limited
images. Pattern Recognition Letters, 3:287–292, 1985.
http://dx.doi.org/10.1016/0167-8655(85)90009-1.
[681] J Vermaak and P Perez. Constrained subspace modeling. In Proc. of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR’03), volume 2, pages
106–113, 2003.
http://dx.doi.org/10.1109/CVPR.2003.1211459.
[682] MA Vicente, PO Hoyer, and A Hyvärinen. Equivalence of some common linear
feature extraction techniques for appearance-based object recognition tasks. IEEE Trans.
on Pattern Analysis and Machine Intelligence, 29:896–900, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.1074.
[683] R Vidal, Y Ma, and S Sastry. Generalized principal components analysis (GPCA).
IEEE Trans. on Pattern Analysis and Machine Intelligence, 27:1945–1959, 2005.
http://dx.doi.org/10.1109/TPAMI.2005.244.
[684] P Viola and MJ Jones. Robust real-time face detection. Int. J. of Computer Vision,
57:137–154, 2004.
http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fb.
[685] EP Vivek and N Sudha. Robust Hausdorff distance measure for face recognition.
Pattern Recognition, 40:431–442, 2007.
http://dx.doi.org/10.1016/j.patcog.2006.04.019.
[686] R Volkel, M Eisner, and KJ Weible. Miniaturized imaging systems. Microelectronic
Engineering, 67-68:461–472, 2003.
http://dx.doi.org/10.1016/S0167-9317(03)00102-3.
[687] RS Wallace, PW Ong, BB Bederson, and EL Schwartz. Space variant image
processing. Int. J. of Computer Vision, 13:71–90, 1994.
http://dx.doi.org/10.1007/BF01420796.
[688] C Wallraven, A Schwaninger, and HH Bülthoff. Learning from humans:
Computational modeling of face recognition. Network: Computation in Neural Systems,
16:401–418, 2005.
http://dx.doi.org/10.1080/09548980500508844.
[689] C Wallraven, A Schwaninger, S Schuhmacher, and HH Bülthoff. View-based recognition of faces in man and machine: Re-visiting inter-extra-ortho. In Proc. of the 2nd International Workshop on Biologically Motivated Computer Vision, volume 2525 of Lecture Notes in Computer Science, pages 651–660. Springer, 2002.
[690] C Wang and W Wang. Links between ppca and subspace methods for complete
gaussian density estimation. IEEE Trans. on Neural Networks, 17:789–792, 2006.
http://dx.doi.org/10.1109/TNN.2006.871718.
[691] Q Wang, Y Deng, and S Liu. Rotation-invariant pattern recognition using
morphological phase-only correlation. Optics Communications, 257:39–53, 2006.
http://dx.doi.org/10.1016/j.optcom.2005.07.015.
[692] Q Wang and S Liu. Morphological phase-only correlation. Optics Communications,
244:93–104, 2005.
http://dx.doi.org/10.1016/j.optcom.2004.09.046.
[693] Q Wang and S Liu. Shift- and scale-invariant pattern recognition using morphological
phase-only correlation. Optics and Laser Technology, 39:569–576, 2007.
http://dx.doi.org/10.1016/j.optlastec.2005.10.006.
[694] X Wang and X Tang. Random sampling LDA for face recognition. In Proc. of the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR’04), volume 2,
pages 259–265, 2004.
http://dx.doi.org/10.1109/CVPR.2004.1315172.
[695] Y Wang and G Baciu. Robust object matching using a modified version of the
Hausdorff measure. Int. J. of Image and Graphics, 2:361–374, 2002.
http://dx.doi.org/10.1142/S0219467802000688.
[696] Y Wang and CS Chua. Robust face recognition from 2D and 3D images using
structural Hausdorff distance. Image and Vision Computing, 24:176–185, 2006.
http://dx.doi.org/10.1016/j.imavis.2005.09.025.
[697] Y Wang, H Lu, and G Sun. A fast search algorithm for template matching based on
inequality criterion. In Proc. of the 7th International Conference on Signal Processing,
volume 2, pages 1211–1214, 2004.
http://dx.doi.org/10.1109/ICOSP.2004.1441542.
[698] Z Wang, RK Rao, D Nandy, J Ben-Arie, and N Jojic. A generalized expansion
matching based feature extractor. In Proc. of the 13th IAPR International Conference on
Pattern Recognition (ICPR’96), pages 29–33, 1996. Feature templates.
http://dx.doi.org/10.1109/ICPR.1996.546718.
[699] M Wasim and RG Brereton. Determination of the number of significant components
in liquid chromatography nuclear magnetic resonance spectroscopy. Chemometrics and
intelligent laboratory systems, 72:133–151, 2004.
http://dx.doi.org/10.1016/j.chemolab.2004.01.008.
[700] M Watanabe and SK Nayar. Telecentric optics for focus analysis. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 19:1360–1365, 1997.
http://dx.doi.org/10.1109/34.643894.
[701] T Watanabe, C-W Lee, A Tsukamoto, and M Yachida. Real-time gesture recognition
using maskable template model. In Proc. of the 3rd IEEE International Conference on
Multimedia Computing and Systems, pages 341–348, 1996.
http://dx.doi.org/10.1109/MMCS.1996.534997.
[702] B Weiss. Fast median and bilateral filtering. ACM Transactions on Graphics,
25:519–526, 2006.
http://dx.doi.org/10.1145/1141911.1141918.
[703] T Weyrich, W Matusik, H Pfister, B Bickel, C Donner, C Tu, J McAndless,
J Lee, A Ngan, HW Jensen, and Markus Gross. Analysis of human faces using
a measurement-based skin reflectance model. ACM Transactions on Graphics,
25:1013–1024, 2006.
http://dx.doi.org/10.1145/1141911.1141987.
[704] J Whitehill and CW Omlin. Haar features for FACS AU recognition. In Proc. of the
7th International Conference on Automatic Face and Gesture Recognition (FG’06), pages
97–101, 2006.
http://dx.doi.org/10.1109/FGR.2006.61.
[705] J Whitehill and CW Omlin. Local versus global segmentation for facial expression
recognition. In Proc. of the 7th International Conference on Automatic Face and Gesture
Recognition (FG’06), pages 357–362, 2006.
http://dx.doi.org/10.1109/FGR.2006.74.
[706] H Wildenauer, T Melzer, and H Bischof. A gradient-based eigenspace approach to
dealing with occlusions and non-gaussian noise. In Proc. of the 16th IAPR International
Conference on Pattern Recognition (ICPR’02), volume 2, pages 977–980, 2002.
http://dx.doi.org/10.1109/ICPR.2002.1048469.
[707] DL Wilson, AJ Baddeley, and RA Owens. A new metric for gray-scale image
comparison. Int. J. of Computer Vision, 24:5–17, 1997.
http://dx.doi.org/10.1023/A:1007978107063.
[708] AP Witkin, D Terzopoulos, and M Kass. Signal matching through scale space. Int.
J. of Computer Vision, 1:133–144, 1987.
http://dx.doi.org/10.1007/BF00123162.
[709] B Wohlberg and K Vixie. Invariant template matching with tangent vectors. Optical
Engineering, to appear, 2007.
http://dx.doi.org/10.1117/1.2715984.
[710] LB Wolff. Polarization vision: a new sensory approach to image understanding. Image
and Vision Computing, 15:81–93, 1995.
http://dx.doi.org/10.1016/S0262-8856(96)01123-7.
[711] AM Wood. The interaction between hardware, software and algorithms. In Duff MJB and Levialdi S, editors, Languages and Architectures for Image Processing, pages 1–11. Academic Press, 1981.
[712] QX Wu. A correlation-relaxation-labeling framework for computing optical
flow-template matching from a new perspective. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 17:843–853, 1995.
http://dx.doi.org/10.1109/34.406650.
[713] C Xu and JL Prince. Snakes, shapes, and gradient vector flow. IEEE Trans. on
Image processing, 7:359–369, 1998.
http://dx.doi.org/10.1109/83.661186.
[714] L Xu and E Oja. Randomized hough transform (rht): Basic mechanisms, algorithms,
and computational complexities. Computer Vision, Graphics and Image Processing,
57:131–154, 1993.
http://dx.doi.org/10.1006/ciun.1993.1009.
[715] L Xu, E Oja, and P Kultanen. A new curve detection method: Randomized Hough
transform (RHT). Pattern Recognition Letters, 11:331–338, 1990.
http://dx.doi.org/10.1016/0167-8655(90)90042-Z.
[716] L Xu and AL Yuille. Robust principal component analysis by self-organizing rules
based on statistical physics approach. IEEE Trans. on Neural Networks, 6:131–143, 1995.
http://dx.doi.org/10.1109/72.363442.
[717] P Yan and KW Bowyer. Biometric recognition using 3d ear shape. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 29:1297–1308, 2007.
http://dx.doi.org/10.1109/TPAMI.2007.1067.
[718] AY Yang, SR Rao, and Y Ma. Robust statistical estimation and segmentation
of multiple subspaces. In Proc. of the Conference on Computer Vision and Pattern
Recognition Workshop, page 99, 2006.
http://dx.doi.org/10.1109/CVPRW.2006.178.
[719] CHT Yang, SH Lai, and LW Chang. Hybrid image matching combining Hausdorff
distance with normalized gradient matching. Pattern Recognition, 40:1173–1181, 2007.
http://dx.doi.org/10.1016/j.patcog.2006.09.014.
[720] J Yang and Jy Yang. Why can LDA be performed in PCA tranformed space? Pattern
Recognition, 36:563–566, 2003.
http://dx.doi.org/10.1016/S0031-3203(02)00048-1.
[721] J Yang, D Zhang, AF Frangi, and J Yang. Two-dimensional PCA: A new approach
to appearance-based face representation and recognition. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 26:131–137, 2004.
http://dx.doi.org/10.1109/TPAMI.2004.10004.
[722] J Yang, D Zhang, and JY Yang. Constructing PCA baseline algorithms to reevaluate
ICA-based face-recognition performances. IEEE Trans. on Systems, Man and Cybernetics,
Part B, 37:1015–1021, 2007.
http://dx.doi.org/10.1109/TSMCB.2007.891541.
[723] M-H Yang, N Ahuja, and D Kriegman. Face recognition using kernel eigenfaces. In
Proc. of the International Conference on Image Processing (ICIP’00), volume 1, pages
37–40, 2000.
http://dx.doi.org/10.1109/ICIP.2000.900886.
[724] Q Yang, X Ding, and Z Chen. Discriminant local feature analysis of facial images.
In Proc. of the International Conference on Image Processing (ICIP’03), volume 2, pages
863–866, 2003.
http://dx.doi.org/10.1109/ICIP.2003.1246817.
[725] QZ Ye. The signed euclidean transform and its applications. In Proc. of the 19th IAPR International Conference on Pattern Recognition (ICPR’88), volume 1, pages 495–499, 1988.
[726] A Yeung and N Barnes. Efficient active monocular fixation using the log-polar sensor.
Int. J. of Intelligent Systems Technologies and Applications, 1:157–173, 2005.
http://dx.doi.org/10.1504/IJISTA.2005.007313.
[727] X Yi and OI Camps. Line feature-based recognition using Hausdorff distance. In
Proc. of the IEEE Symposium on Computer Vision, pages 79–84, 1995.
http://dx.doi.org/10.1109/ISCV.1995.476981.
[728] X Yi and OI Camps. Robust occluding contour detection using the Hausdorff
distance. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR’97), pages 962–968, 1997.
http://dx.doi.org/10.1109/CVPR.1997.609444.
[729] X Yi and OI Camps. Line-based recognition using a multidimensional Hausdorff
distance. IEEE Trans. on Pattern Analysis and Machine Intelligence, 21:901–916, 1999.
http://dx.doi.org/10.1109/34.790430.
[730] RKK Yip, PKS Tam, and DNK Leung. Modification of Hough transform for object
recognition using a 2-dimensional array. Pattern Recognition, 28:1733–1744, 1995.
http://dx.doi.org/10.1016/0031-3203(95)00031-T.
[731] E Yoruk, E Konukoglu, B Sankur, and J Darbon. Shape-based hand recognition.
IEEE Trans. on Image processing, 15:1803–1815, 2006.
http://dx.doi.org/10.1109/TIP.2006.873439.
[732] J You, P Bhattacharya, and S Hungenahally. Real-time object recognition:
Hierarchical image matching in a parallel virtual machine environment. In Proc. of the
14th IAPR International Conference on Pattern Recognition (ICPR’98), volume 1, pages
275–277, 1998.
http://dx.doi.org/10.1109/ICPR.1998.711134.
[733] J You, W Lu, J Li, G Gindi, and Z Liang. Image matching for translation, rotation
and uniform scaling by the Radon transform. In Proc. of the International Conference on
Image Processing (ICIP’98), volume 1, pages 847–851, 1998.
http://dx.doi.org/10.1109/ICIP.1998.723649.
[734] SD You and GE Ford. Object recognition based on projection. In Proc. of the
International Joint Conference on Neural Networks, volume 4, pages 31–36, 1992.
http://dx.doi.org/10.1109/IJCNN.1992.227293.
[735] J Yu, Q Tian, T Rui, and TS Huang. Integrating discriminant and descriptive
information for dimension reduction and classification. IEEE Trans. on Circuits and
Systems for Video Technology, 17:372–377, 2007.
http://dx.doi.org/10.1109/TCSVT.2007.890861.
[736] S Yu, K Yu, V Tresp, H-P Kriegel, and M Wu. Supervised probabilistic principal
component analysis. In Proc. of the 12th ACM SIGKDD international conference on
Knowledge discovery and data mining (KDD’06), pages 464–473, 2006.
http://dx.doi.org/10.1145/1150402.1150454.
[737] X Yu and MKH Leung. Shape recognition using curve segment Hausdorff distance.
In Proc. of the 18th IAPR International Conference on Pattern Recognition (ICPR’06),
volume 3, pages 441–444, 2006.
http://dx.doi.org/10.1109/ICPR.2006.1050.
[738] Y Yuan and K Barner. An active shape model based tactile hand shape recognition
with support vector machines. In Proc. of the 40th Annual Conference on Information
Sciences and Systems, pages 1611–1616, 2006.
http://dx.doi.org/10.1109/CISS.2006.286393.
[739] A Yuille. Generalized deformable models, statistical physics and matching problems.
Neural Computation, 2:1–24, 1990.
http://dx.doi.org/10.1162/neco.1990.2.1.1.
[740] AL Yuille, PW Hallinan, and DS Cohen. Feature extraction from faces using
deformable templates. Int. J. of Computer Vision, 8:99–111, 1992.
http://dx.doi.org/10.1007/BF00127169.
[741] AL Yuille, K Honda, and C Peterson. Particle tracking by deformable templates. In
Proc. of the International Joint Conference on Neural Networks, volume 1, pages 7–12,
1991.
http://dx.doi.org/10.1109/IJCNN.1991.155141.
[742] R Zabih and J Woodfill. Non-parametric local transforms for computing visual correspondence. In Proc. of the 3rd European Conference on Computer Vision (ECCV’94), volume 801 of Lecture Notes in Computer Science, pages 151–158. Springer, 1994.
[743] Y Zhan, J Ye, D Niu, and P Cao. Facial expression recognition based on Gabor
wavelet transformation and elastic templates matching. Int. J. of Image and Graphics,
6:125–138, 2006.
http://dx.doi.org/10.1142/S0219467806002112.
[744] J Zhang, Z Ou, and H Wei. Fingerprint matching using phase-only correlation and Fourier-Mellin transforms. In Proc. of the Sixth International Conference on Intelligent Systems Designs and Applications, volume 2, pages 379–383, 2006.
[745] Q Zhang and Y-W Leung. A class of learning algorithms for principal component
analysis and minor component analysis. IEEE Trans. on Neural Networks, 11:529–533,
2000.
http://dx.doi.org/10.1109/72.839022.
[746] Z Zhang and W Wriggers. Local feature analysis: a statistical theory for reproducible
essential dynamics of large macromolecules. Proteins, 64:391–403, 2006.
http://dx.doi.org/10.1002/prot.20983.
[747] C Zhao, W Shi, and Y Deng. A new Hausdorff distance for image matching. Pattern
Recognition Letters, 26:581–586, 2005.
http://dx.doi.org/10.1016/j.patrec.2004.09.022.
[748] J Zhen, A Balasuriya, and S Challa. Target tracking with Bayesian fusion based
template matching. In Proc. of the International Conference on Image Processing
(ICIP’05), volume 2, pages 826–829, 2005.
http://dx.doi.org/10.1109/ICIP.2005.1530183.
[749] G Zheng and R Modarres. A robust estimate of the correlation coefficient for bivariate
normal distribution using ranked set sampling. J. of Statistical Planning and Inference,
136:298–309, 2006.
http://dx.doi.org/10.1016/j.jspi.2004.06.006.
[750] Z Zhu, H Lu, and Z Li. Novel object recognition based on hypothesis generation
and verification. In Proc. of the 3rd International Conference on Image and Graphics
(ICIG’04), pages 88–91, 2004.
http://dx.doi.org/10.1109/ICIG.2004.106.
[751] Z Zhu, M Tang, and H Lu. A new robust circular Gabor based object matching by
using weighted Hausdorff distance. Pattern Recognition Letters, 25:515–523, 2004.
http://dx.doi.org/10.1016/j.patrec.2003.12.014.