Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing

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 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 fra...

• Type: Journal
• Year: 2008
• Where: PAMI
• Authors: Ofir Pele, Michael Werman
• Keywords: Distance Family, Image, PAMI 2008, Sampling Algorithm