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Issue No.01 - January (2009 vol.31)
pp: 129-141
Federico Tombari , University of Bologna, Bologna
Stefano Mattoccia , University of Bologna, Bologna
Luigi Di Stefano , University of Bologna, Bologna
ABSTRACT
This paper proposes a novel method for fast pattern matching based on dissimilarity functions derived from the Lp norm, such as the Sum of Squared Differences (SSD) and the Sum of Absolute Differences (SAD). The proposed method is full-search equivalent, i.e. it yields the same results as the Full Search (FS) algorithm. In order to pursue computational savings the method deploys a succession of increasingly tighter lower bounds of the adopted Lp norm-based dissimilarity function. Such bounding functions allow for establishing a hierarchy of pruning conditions aimed at skipping rapidly those candidates that cannot satisfy the matching criterion. The paper includes an experimental comparison between the proposed method and other full-search equivalent approaches known in literature, which proves the remarkable computational efficiency of our proposal.
INDEX TERMS
Computer vision, Pattern matching, Pattern analysis
CITATION
Federico Tombari, Stefano Mattoccia, Luigi Di Stefano, "Full-Search-Equivalent Pattern Matching with Incremental Dissimilarity Approximations", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 1, pp. 129-141, January 2009, doi:10.1109/TPAMI.2008.46
REFERENCES
 [1] 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. [2] D.I. Barnea and H.F. Silverman, “A Class of Algorithms for Digital Image Registration,” IEEE Trans. Computers, vol. 21, no. 2, pp. 179-186, Feb. 1972. [3] F. Tombari, S. Mattoccia, and L. Di Stefano, “Template Matching Based on the lp Norm Using Sufficient Conditions with Incremental Approximations,” Proc. IEEE Int'l Conf. Advanced Video Surveillance Systems, Nov. 2006. [4] H.L. Royden, Real Analysis, third ed. Prentice Hall, 1988. [5] F. Crow, “Summed-Area Tables for Texture Mapping,” Computer Graphics, vol. 18, no. 3, pp. 207-212, 1984. [6] M. McDonnel, “Box-Filtering Techniques,” Computer Graphics and Image Processing, vol. 17, pp. 65-70, 1981. [7] P. Viola and M.J. Jones, “Robust Real-Time Face Detection,” Int'l J. Computer Vision, vol. 57, no. 2, pp. 137-154, 2004. [8] J.P. Lewis, “Fast Template Matching,” Proc. Conf. Vision Interface, pp. 120-123, May 1995. [9] B. Zitová and J. Flusser, “Image Registration Methods: A Survey,” Image and Vision Computing, vol. 21, no. 11, pp. 977-1000, 2003. [10] A. Goshtasby, 2-D and 3-D Image Registration for Medical, Remote Sensing and Industrial Applications. John Wiley & Sons, 2005. [11] C. Sun, “Moving Average Algorithms for Diamond, Hexagon, and General Polygonal Shaped Window Operations,” Pattern Recognition Letters, vol. 27, no. 6, pp. 556-566, 2006. [12] C.H. Lee and L.H. Chen, “A Fast Motion Estimation Algorithm Based on the Block Sum Pyramid,” IEEE Trans. Image Processing, vol. 6, no. 11, pp. 1587-1591, 1997. [13] X.Q. Gao, C.J. Duanmu, and C.R. Zou, “A Multilevel Successive Elimination Algorithm for Block Matching Motion Estimation,” IEEE Trans. Image Processing, vol. 9, no. 3, pp. 501-504, 2000. [14] Z. Pan, K. Kotani, and T. Ohmi, “Fast Encoding Method for Vector Quantization Using Modified $l_{2}$ -Norm Pyramid,” IEEE Signal Processing Letters, vol. 12, no. 9, pp. 609-612, 2005. [15] www.faculty.idc.ac.il/toky/Softwaresoftware.htm , 2008. [16] http://sourceforge.net/projectsopencvlibrary , 2008. [17] http://people.csail.mit.edu/torralbaimages , 2008. [18] www.data-compression.info/CorporaLukasCorpus , 2008. [19] http://zulu.ssc.nasa.govmrsid, 2008.