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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.
Pattern Analysis and Machine Intelligence, IEEE Transactions on (Volume:30 , Issue: 8 )
Date of Publication: Aug. 2008