Variational Maximum A Posteriori Model Similarity and Dissimilarity Matching

Chiverton John, Majid Mirmehdi, Xie Xianghua

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

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.
Translated title of the contributionVariational Maximum A Posteriori Model Similarity and Dissimilarity Matching
Original languageEnglish
Title of host publicationInternational Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication statusPublished - 2008

Bibliographical note

Other page information: -
Conference Proceedings/Title of Journal: International Conference on Pattern Recognition
Other identifier: 2000931

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