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Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing
August 2008 (vol. 30 no. 8)
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.

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Index Terms:
pattern matching, template matching, pattern detection, image similarity measures, Hamming distance, real time, sequential hypothesis testing, composite hypothesis, image statistics, Bayesian statistics, finite populations
Citation:
Ofir Pele, Michael Werman, "Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, pp. 1427-1443, Aug. 2008, doi:10.1109/TPAMI.2007.70794
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