Variational Maximum A Posteriori model similarity and dissimilarity matching

John Chiverton, Majid Mirmehdi, Xianghua Xie

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    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.
    Original languageEnglish
    Title of host publication19th International conference on pattern recognition, 2008. ICPR 2008
    Place of PublicationPiscataway
    PublisherIEEE/ IAPR
    Pages1-4
    ISBN (Electronic)9781424421756
    ISBN (Print)9781424421749
    DOIs
    Publication statusPublished - 1 Dec 2008
    Event19th International conference on pattern recognition - Florida, United States
    Duration: 8 Dec 200811 Dec 2008

    Conference

    Conference19th International conference on pattern recognition
    Abbreviated titleICPR 2008
    CountryUnited States
    CityFlorida
    Period8/12/0811/12/08

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