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Full-Search-Equivalent Pattern Matching with Incremental Dissimilarity Approximations
January 2009 (vol. 31 no. 1)
pp. 129-141
Federico Tombari, University of Bologna, Bologna
Stefano Mattoccia, University of Bologna, Bologna
Luigi Di Stefano, University of Bologna, Bologna
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.

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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 and Machine Intelligence, vol. 31, no. 1, pp. 129-141, Jan. 2009, doi:10.1109/TPAMI.2008.46
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