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Real-Time Pattern Matching Using Projection Kernels
September 2005 (vol. 27 no. 9)
pp. 1430-1445
A novel approach to pattern matching is presented in which time complexity is reduced by two orders of magnitude compared to traditional approaches. The suggested approach uses an efficient projection scheme which bounds the distance between a pattern and an image window using very few operations on average. The projection framework is combined with a rejection scheme which allows rapid rejection of image windows that are distant from the pattern. Experiments show that the approach is effective even under very noisy conditions. The approach described here can also be used in classification schemes where the projection values serve as input features that are informative and fast to extract.

[1] A.J. Ahumada, “Computational Image Quality Metrics: A Review,” Proc. Soc. Information Display Int'l Symp., vol. 24, pp. 305-308, 1998.
[2] S. Baker and T. Kanade, “Hallucinating Faces,” Proc. Fourth Int'l Conf. Automatic Face and Gesture Recognition, p. 83, Mar. 2000.
[3] S. Baker and S.K. Nayar, “Pattern Rejection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 544-549, 1996.
[4] G. Ben-Artzi, H. Hel-Or, and Y. Hel-Or, “Filtering with Gray-Code Kernels,” Proc. 17th Int'l Conf. Pattern Recognition, pp. 556-559, Sept. 2004.
[5] J.L. Bentley, “Multidimensional Binary Search Trees Used for Associative Searching,” Comm. ACM, vol. 18, no. 9, pp. 509-517, 1975.
[6] C.J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
[7] F. Crow, “Summed-Area Tables for Texture Mapping,” Proc. SIGGRAPH, vol. 18, no. 3, pp. 207-212, 1984.
[8] A. Efros and W.T. Freeman, “Image Quilting for Texture Synthesis and Transfer,” Proc. SIGGRAPH, Aug. 2001.
[9] M. Elad, Y. Hel-Or, and R. Keshet, “Pattern Detection Using Maximal Rejection Classifier,” Proc. Int'l Workshop Visual Form, pp. 28-30, May 2000.
[10] A.M. Eskicioglu and P.S. Fisher, “Image Quality Measures and Their Performance,” IEEE Trans. Comm., vol. 43, no. 12, pp. 2959-2965, 1995.
[11] A.W. Fitzgibbon, Y. Wexler, and A. Zisserman, “Image-Based Rendering Using Image-Based Priors,” Proc. Int'l Conf. Computer Vision, 2003.
[12] F. Fleuret and D. Geman, “Graded Learning for Object Detection,” Proc. IEEE Workshop Statistical and Computational Theories of Vision, pp. 544-549, 1999.
[13] W.T. Freeman, T.R. Jones, and E.C. Pasztor, “Example-Based Super-Resolution,” IEEE Computer Graphics and Applications, vol. 22, no. 2, pp. 56-65, Mar./Apr. 2002.
[14] B. Girod, “Whats Wrong with Mean-Squared Error?” Digital Images and Human Vision, A.B. Watson, ed., chapter 15, pp. 207-220. MIT Press, 1993.
[15] G.H. Golub and C.F. Van Loan, Matrix Computations. Baltimore: John Hopkins Univ. Press, 1989.
[16] C.J. Harris and M. Stephens, “A Combined Corner and Edge Detector,” Proc. Fourth Alvey Vision Conf., pp. 147-151, 1988.
[17] Y. Hel-Or and H. Hel-Or, “Real Time Pattern Matching Using Projection Kernels,” Proc. Ninth IEEE Int'l Conf. Computer Vision, pp. 1486-1493, Oct. 2003.
[18] X. Huo and J. Chen, “Building a Cascade Detector and Its Applications in Automatic Target Detection,” Applied Optics, vol. 43, no. 2, pp. 293-303, 2004.
[19] 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.
[20] H. Kitajima, “Energy Packing Efficiency of the Hadamard Transform,” IEEE Trans. Comm., pp. 1256-1258, 1976.
[21] M.H. Lee and M. Kaveh, “Fast Hadamard Transform Based on a Simple Matrix Factorization,” IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 34, no. 6, pp. 1666-1667, 1986.
[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, July 2001.
[23] T. Luczak and W. Szpankowski, “A Suboptimal Lossy Data Compression Based on Approximate Pattern Matching,” IEEE Trans. Information Theory, vol. 43, pp. 1439-1451, 1997.
[24] S. Mallat, A Wavelet Tour of Signal Processing. Academic Press, 1999.
[25] A.M. Mamlouk, J.T. Kim, E. Barth, and T. Martinetz, “One-Class Classification with Subgaussians,” DAGM 2003, Proc. 25th Pattern Recognition Symp., pp. 346-353, 2003.
[26] O.J. Murphy and S.M. Selkow, “The Efficiency of Using K-D Trees for Finding Nearest Neighbors in Discrete Space,” Information Processing Letters, vol. 23, no. 4, pp. 215-218, 1986.
[27] A. Nealen and M. Alexa, “Fast and High Quality Overlap Repair for Patch-Based Texture Synthesis,” Proc. Computer Graphics Int'l, 2004.
[28] C. Papageorgiou, M. Oren, and T. Poggio, “A General Framework for Object Detection,” Proc. Sixth Int'l Conf. Computer Vision, pp. 555-562, Jan 1998.
[29] D.L. Ruderman, “Statistics of Natural Images,” Network: Computation in Neural Systems, vol. 5, no. 4, pp. 517-548, 1994.
[30] D.L. Ruderman and W. Bialek, “Statistics of Natural Images: Scaling in the Woods,” Physical Rev. Letters, vol. 73, no. 6, pp. 814-817, 1994.
[31] H. Samet, Applications of Spatial Data Structures. Reading, Mass.: Addison-Wesley, 1990.
[32] H. Samet, The Design and Analysis of Spatial Data Structures. Reading, Mass.: Addison-Wesley, 1990.
[33] S. Santini and R. Jain, “Similarity Measures,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 871-883, Sept. 1999.
[34] J.L. Shanks, “Computation of the Fast Walsh-Fourier Transform,” IEEE Trans. Computers, vol. 18, pp. 457-459, 1969.
[35] G. Strang, Linear Algebra and Its Applications. Orlando, Fla.: Haecourt Brace Jova novic, 1988.
[36] D. Sundararajan and M.O. Ahmad, “Fast Computation of the Discrete Walsh and Hadamard Transforms,” IEEE Trans. Information Processing, vol. 7, no. 6, pp. 898-904, 1998.
[37] Y. Ukrainitz, Y. Hel-Or, and H. Hel-Or, “Real-Time Normalized Gray-Scale Correlation Using Projection Kernels,” submitted to Proc. Int'l Conf. Computer Vision, 2005.
[38] P. Viola and M. Jones, “Robust Real-Time Object Detection,” Proc. Int'l Conf. Computer Vision Workshop Statistical and Computation Theories of Vision, July 2001.
[39] T. Weissman, E. Ordentlich, G. Seroussi, S. Verdu, and M.J. Weinberger, “Universal Discrete Denoising: Known Channel,” Technical Report HPL-2003-29, HP Labs, Feb. 2003.
[40] J. Kane, W.K. Pratt, and H.C. Andrews, “Hadamard Transform Image Coding,” Proc. IEEE, vol. 57, no. 1, pp. 58-68, 1969.
[41] J. Wu, J.M. Rehg, and M.D. Mullin, “Learning a Rare Event Detection Cascade by Direct Feature Selection,” Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Schölkopf, eds. Cambridge, Mass.: MIT Press, 2004.

Index Terms:
Index Terms- Pattern matching, template matching, pattern detection, feature extraction, Walsh-Hadamard.
Yacov Hel-Or, Hagit Hel-Or, "Real-Time Pattern Matching Using Projection Kernels," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 9, pp. 1430-1445, Sept. 2005, doi:10.1109/TPAMI.2005.184
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