The Community for Technology Leaders
RSS Icon
Issue No.08 - August (2008 vol.30)
pp: 1427-1443
This paper describes a method for robust real time pattern matching. We first introduce a family of image distance measures, the "Image Hamming Distance Family". Members of this family are robust to occlusion, small geometrical transforms, light changes and non-rigid deformations. We then present a novel Bayesian framework for sequential hypothesis testing on finite populations. Based on this framework, we design an optimal rejection/acceptance sampling algorithm. This algorithm quickly determines whether two images are similar with respect to a member of the Image Hamming Distance Family. We also present a fast framework that designs a near-optimal sampling algorithm. Extensive experimental results show that the sequential sampling algorithm performance is excellent. Implemented on a Pentium 4 3GHz processor, detection of a pattern with 2197 pixels, in 640x480 pixel frames, where in each frame the pattern rotated and was highly occluded, proceeds at only 0.022 seconds per frame.
pattern matching, template matching, pattern detection, image similarity measures, Hamming distance, real time, sequential hypothesis testing, composite hypothesis, image statistics, Bayesian statistics, finite populations
Ofir Pele, Michael Werman, "Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 8, pp. 1427-1443, August 2008, doi:10.1109/TPAMI.2007.70794
[1] A. Wald, Sequential Analysis. Wiley, 1947.
[2] D.I. Barnea and H.F. Silverman, “A Class of Algorithms for Fast Digital Image Registration,” IEEE Trans. Computers, vol. 21, no. 2, pp. 179-186, Feb. 1972.
[3] J. Matas and O. Chum, “Randomized RANSAC with Sequential Probability Ratio Test,” Proc. 10th IEEE Int'l Conf. Computer Vision, vol. 2, pp. 1727-1732, Oct. 2005.
[4] J. Šochman and J. Matas, “Waldboost—Learning for Time-Constrained Sequential Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 150-157, June 2005.
[5] P.E. Anuta, “Spatial Registration of Multispectral and Multitemporal Digital Imagery Using Fast Fourier Transform,” IEEE Trans. Geoscience Electronics, vol. 8, pp. 353-368, 1970.
[6] J.P. Lewis, Fast Normalized Cross-Correlation, http://www.idiom. com/ zilla/Work/nvisionInterface nip.pdf, Sept. 1995.
[7] A.J. Ahumada, “Computational Image Quality Metrics: A Review,” SID Int'l Symp. Digest of Technical Papers, vol. 24, pp. 305-308, 1998.
[8] B. Girod, “Digital Images and Human Vision,” What's Wrong with the Mean-Squared Error? chapter 15, MIT Press, 1993.
[9] A.M. Eskicioglu and P.S. Fisher, “Image Quality Measures and Their Performance,” IEEE Trans. Comm., vol. 43, no. 12, pp. 2959-2965, 1995.
[10] S. Santini and R.C. Jain, “Similarity Measures,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 871-883, Sept. 1999.
[11] B.D. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision,” Proc. Image Understanding Workshop '81, pp. 121-130, 1981.
[12] J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust Wide Baseline Stereo from Maximally Stable Extremal Regions,” Proc. 13th British Machine Vision Conf., vol. 1, pp. 384-393, Sept. 2002.
[13] D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[14] K. Mikolajczyk and C. Schmid, “A Performance Evaluation of Local Descriptors,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, Oct. 2005.
[15] H. Bay, T. Tuytelaars, and L.J.V. Gool, “Surf: Speeded Up Robust Features,” Proc. Ninth European Conf. Computer Vision, vol. 1, pp.404-417, 2006.
[16] V. Lepetit and P. Fua, “Keypoint Recognition Using Randomized Trees,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1465-1479, Sept. 2006.
[17] J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, “Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study,” Int'l J. Computer Vision, vol. 73, no. 2, pp. 213-238, 2007.
[18] D. Keren, M. Osadchy, and C. Gotsman, “Antifaces: A Novel Fast Method for Image Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 7, pp. 747-761, July 2001.
[19] S. Romdhani, P.H.S. Torr, B. Scholkopf, and A. Blake, “Computationally Efficient Face Detection,” Proc. Eighth IEEE Int'l Conf. Computer Vision, pp. 695-700, , 2001.
[20] P. Viola and M.J. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
[21] S. Avidan and M. Butman, “The Power of Feature Clustering: An Application to Object Detection,” Neural Information Processing Systems,, Dec. 2004.
[22] L. Liang, C. Liu, Y.-Q. Xu, B. Guo, and H.-Y. Shum, “Real-Time Texture Synthesis by Patch-Based Sampling,” ACM Trans. Graphics, vol. 20, no. 3, pp. 127-150, 2001.
[23] Y. Hel-Or and H. Hel-Or, “Real-Time Pattern Matching Using Projection Kernels,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 9, pp. 1430-1445, , Sept. 2005.
[24] G. Ben-Artzi, H. Hel-Or, and Y. Hel-Or, “The Gray-Code Filter Kernels,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 382-393, Mar. 2007.
[25] M. Ben-Yehuda, L. Cadany, and H. Hel-Or, “Irregular Pattern Matching Using Projections,” Proc. 12th Int'l Conf. Image Processing, vol. 2, pp. 834-837, 2005.
[26] S.-H. Cha, “Efficient Algorithms for Image Template and Dictionary Matching,” J. Math. Imaging and Vision, vol. 12, no. 1, pp. 81-90, 2000.
[27] B. Zitova and J. Flusser, “Image Registration Methods: A Survey,” Image and Vision Computing, vol. 21, no. 11, pp. 977-1000, Oct. 2003.
[28] J.A.G. Pereira and N.D.A. Mascarenhas, “Digital Image Registration by Sequential Analysis,” Computers and Graphics, vol. 8, pp.247-253, 1984.
[29] N.D.A. Mascarenhas and G.J. Erthal, “Image Registration by Sequential Tests of Hypotheses: The Relationship between Gaussian and Binomial Models,” Computers and Graphics, vol. 16, no. 3, pp. 259-264, 1992.
[30] A.B. Lee, D. Mumford, and J. Huang, “Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model,” Int'l J. Computer Vision, vol. 41, nos. 1/2, pp. 35-59, 2001.
[31] E. Simoncelli and B. Olshausen, “Natural Image Statistics and Neural Representation,” Ann. Rev. Neuroscience, vol. 24, pp. 1193-1216, May 2001.
[32] D.J. Field, “Relations between the Statistics of Natural Images and the Response Properties of Cortical Cells,” J. Optical Soc. Am. A, vol. 4, no. 12, pp. 2379-2394, 1987.
[33] J.G. Daugman, “Entropy Reduction and Decorrelation in Visual Coding by Oriented Neural Receptive Fields,” IEEE Trans. Biomedical Eng., vol. 36, no. 1, pp. 107-114, 1989.
[34] D. Siegmund, Sequential Analysis, Test and Confidence Intervals. Springer-Verlag, 1985.
[35] Y. Amit, 2D Object Detection and Recognition: Models, Algorithms, and Networks. MIT Press, 2002.
[36] R. Zabih and J. Woodfill, “Non-Parametric Local Transforms for Computing Visual Correspondence,” Proc. Third European Conf. Computer Vision, pp. 151-158, 1994.
[37] B. Cyganek, “Comparison of Nonparametric Transformations and Bit Vector Matching for Stereo Correlation,” Proc. 10th Int'l Workshop Combinatorial Image Analysis, pp. 534-547, 2004.
[38] Q. Lv, M. Charikar, and K. Li, “Image Similarity Search with Compact Data Structures,” Proc. 13th ACM Conf. Information and Knowledge Management, pp. 208-217, 2004.
[39] M. Ionescu and A. Ralescu, “Fuzzy Hamming Distance in a Content-Based Image Retrieval System,” Proc. 13th IEEE Int'l Conf. Fuzzy Systems, 2004.
[40] A. Bookstein, V.A. Kulyukin, and T. Raita, “Generalized Hamming Distance,” Information Retrieval, vol. 5, no. 4, pp. 353-375, 2002.
[41] A. Abhyankar, L.A. Hornak, and S. Schuckers, “Biorthogonal-Wavelets-Based Iris Recognition,” Proc. SPIE, vol. 5779, no. 1, pp.59-67, TPAMI.2005.184 ?PSI/5779/591, 2005.
[42] P. Hough, “A Method and Means for Recognizing Complex Patterns,” US Patent 3,069,654, Patent and Trademark Office, 1962.
[43] S.D. Blostein and T.S. Huang, “Detecting Small Moving Objects in Image Sequences Using Sequential Hypothesis Testing,” IEEE Trans. Signal Processing, vol. 39, no. 7, pp. 1611-1629, 1991.
[44] D. Shaked, O. Yaron, and N. Kiryati, “Deriving Stopping Rules for the Probabilistic Hough Transform by Sequential Analysis,” Computer Vision and Image Understanding, vol. 63, no. 3, pp. 512-526, 1996.
[45] A. Amir and M. Lindenbaum, “A Generic Grouping Algorithm and Its Quantitative Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 2, pp. 168-185, Feb. 1998.
[46] M.A. Fischler and R.C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Comm. ACM, vol. 24, no. 6, pp. 381-395, 1981.
[47] H. Gharavi and M. Mills, “Blockmatching Motion Estimation Algorithms—New Results,” IEEE Trans. Circuits and Systems for Video Technology, vol. 37, no. 5, pp. 649-651, 1990.
[48] G. Wyszecki and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae. Wiley, 1982.
[49] R. Brunelli and T. Poggio, “Face Recognition: Features versus Templates,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042-1052, Oct. 1993.
[50] Z. Govindarajulu, Sequential Statistical Procedures, pp. 534-536. Academic Press, 1975.
[51] A. Wald and J. Wolfowitz, “Optimum Character of the Sequential Probability Ratio Test,” Ann. Math. Statistics, vol. 19, pp. 326-339, 1948.
[52] E.L. Lehmann, Testing Statistical Hypotheses, pp. 104-110. John Wiley & Sons, 1959.
[53] H.-J. Mittag and H. Rinne, Statistical Methods of Quality Assurance. Chapman and Hall, 1993.
[54] H.J. Vos, “A Bayesian Sequential Procedure for Determining the Optimal Number of Interrogatory Examples for Concept Learning,” Computers in Human Behavior, vol. 23, no. 1, pp. 609-627, Jan. 2007.
[55] C. Lewis and K. Sheehan, “Using Bayesian Decision Theory to Design a Computerized Mastery Test,” Applied Psychological Measurement, vol. 14, no. 4, pp. 367-386, 1990.
19 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool