This paper presents an iterative maximum likelihood framework for motion segmentation. Our representation of the segmentation problem is based on a similarity matrix for the motion vectors for pairs of pixel blocks. By applying eigendecomposition to the similarity matrix, we develop a maximum likelihood method for grouping the pixel blocks into objects which share a common motion vector. We experiment with the resulting clustering method on a number of real-world motion sequences. Here ground truth data indicates that the method can result in motion classification errors as low as 3%.


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    Titel :

    Maximum likelihood motion segmentation using eigendecomposition


    Beteiligte:
    Robles-Kelly, A. (Autor:in) / Hancock, E.R. (Autor:in)


    Erscheinungsdatum :

    2001-01-01


    Format / Umfang :

    645813 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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