[1]   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.

[2]   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.

[3]   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.

[4]   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.

[5]   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.

[6]   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.

[7]   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.

[8]   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.

[9]   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.

[10]   TM Breuel. Finding lines under bounded error. Pattern Recognition, 29:167–178, 1996.

[11]   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.

[12]   SR Deans. Hough transform from the Radon transform. IEEE Trans. on Pattern Analysis and Machine Intelligence, 3:185–188, 1981.

[13]   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.

[14]   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.

[15]   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.

[16]   JH Elder. Are edges incomplete? Int. J. of Computer Vision, 34:97–122, 1999.
http://dx.doi.org/10.1023/A:1008183703117.

[17]   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.

[18]   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.

[19]   A Goldenshluger and A Zeevi. The Hough transform estimator. Annals of Statistics, 32:1908–1932, 2004.
http://dx.doi.org/10.1214/009053604000000760.

[20]   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.

[21]   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.

[22]   PVC Hough. Method and means for recognizing complex patterns. US Patent Nr. 3069654, 1962.

[23]   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.

[24]   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.

[25]   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.

[26]   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.

[27]   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.

[28]   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.

[29]   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.

[30]   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.

[31]   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.

[32]   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.

[33]   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.

[34]   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.

[35]   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.

[36]   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.

[37]   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.

[38]   VF Leavers. Which Hough transform? CVGIP: Image Understanding, 58:250–2264, 1993.
http://dx.doi.org/10.1006/ciun.1993.1041.

[39]   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.

[40]   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.

[41]   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.

[42]   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.

[43]   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.

[44]   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.

[45]   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.

[46]   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.

[47]   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.

[48]   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.

[49]   A Papoulis. Probability, Random Variables and Stochastic Processes. McGraw-Hill, 1965.

[50]   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.

[51]   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.

[52]   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.

[53]   J Radon. Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Ber. Verh. Sächs. Akad., 69:262–277, 1917.

[54]   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.

[55]   BW Silverman. Density estimation for statistics and data analysis. Chapman and Hall, 1986.

[56]   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.

[57]   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.

[58]   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.

[59]   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.

[60]   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.

[61]   PA Toft. The Radon Transform - Theory and Implementation. PhD thesis, Department of Mathematical Modelling, Technical University of Denmark, 1996.

[62]   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.

[63]   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.

[64]   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.

[65]   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.

[66]   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.

[67]   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.

[68]   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.

[69]   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.

[70]   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.

[71]   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.