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Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing

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2 Author(s)
Pele, O. ; Sch. of Comput. Sci. & Eng., Hebrew Univ. of Jerusalem, Jerusalem ; Werman, M.

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 nonrigid 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's performance is excellent. Implemented on a Pentium IV 3 GHz processor, the detection of a pattern with 2,197 pixels in 640times480 pixel frames, where in each frame the pattern rotated and was highly occluded, proceeds at only 0.022 seconds per frame.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:30 ,  Issue: 8 )

Date of Publication:

Aug. 2008

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