This paper presents a dynamic conditional random field (DCRF) model to integrate contextual constraints for object segmentation in image sequences. Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of video frames. The segmentation method employs both intensity and motion cues, and it combines dynamic information and spatial interaction of the observed data. Experimental results show that the proposed approach effectively fuses contextual constraints in video sequences and improves the accuracy of object segmentation.


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    Title :

    A dynamic conditional random field model for object segmentation in image sequences


    Contributors:
    Yang Wang, (author) / Qiang Ji, (author)


    Publication date :

    2005-01-01


    Size :

    246783 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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