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Variational Maximum A Posteriori Model Similarity and Dissimilarity Matching

John Chiverton, Majid Mirmehdi, Xianghua Xie, Variational Maximum A Posteriori Model Similarity and Dissimilarity Matching. International Conference on Pattern Recognition. December 2008. PDF, 1204 Kbytes. External information

Abstract

A new variational Maximum A Posteriori (MAP) contextual modeling approach is presented that minimizes the product of two ratios: (a) the ratio of the model distribution to the distribution of currently estimated foreground pixels; (b) the ratio of the background distribution to the model distribution for all estimated background pixels. This approach provides robust discrimination to identify the division between foreground and background pixels, which is useful for applications such as object tracking.

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