Object detection is challenging partly due to the limited discriminative power of local feature descriptors. We amend this limitation by incorporating spatial constraints among neighboring features. We propose a two-step algorithm. First, a feature together with its spatial neighbors forms a flexible feature template. Two feature templates can be compared more informatively than two individual features without knowing the 3D object model. A large portion of false matches can be excluded after the first step. In a second global matching step, object detection is formulated as a graph-matching problem. A model graph is constructed by applying Delaunay triangulation on the surviving features. The best matching graph in an input image is computed by finding the maximum a posterior (MAP) estimate of a binary Markov random field with triangular maximal clique. The optimization is solved by the max-product algorithm (a.k.a. belief propagation). Experiments on both rigid and non-rigid objects demonstrate the generality and efficacy of the proposed methods.


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    Titel :

    Object detection using 2D spatial ordering constraints


    Beteiligte:
    Li, Y. (Autor:in) / Tsin, Y. (Autor:in) / Genc, Y. (Autor:in) / Kanade, T. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    642743 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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