Efficient Tracking as Linear Program on Weak Binary Classifiers
Proceedings of the 30th DAGM symposium on Pattern Recognition
Efficient NCC-Based Image Matching Based on Novel Hierarchical Bounds
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Applying parallel design techniques to template matching with GPUs
VECPAR'10 Proceedings of the 9th international conference on High performance computing for computational science
Tracking pedestrian with multi-component online deformable part-based model
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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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 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.