This paper addresses the problem of segmentation of moving objects in image sequences, which is of key importance in content-based applications. We transform the problem into a graph labeling problem over a region adjacency graph (RAG), by introducing a Markov random field (MRF) model based on spatio-temporal information. The initial partition is obtained by fast, color-based watershed segmentation. The motion of each region is estimated and validated in a hierarchical framework. A dynamic memory, based on object tracking, is incorporated into the segmentation process to maintain temporal coherence. The performance of the algorithm is evaluated on several real-world image sequences.


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    Titel :

    A region-based MRF model for unsupervised segmentation of moving objects in image sequences


    Beteiligte:
    Tsaig, Y. (Autor:in) / Averbuch, A. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    865073 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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