This paper presents a novel approach for landmark-based shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression problem. To well describe nonrigid shape deformation, this paper measures the shape difference using a thin-plate spline model. The proposed approach is capable of preserving the topology of the template shape in the deformation. This property is achieved by inserting a set of additional points and imposing a set of linear equality and/or inequality constraints. The underlying optimization problem is solved using a quadratic programming algorithm. The proposed method has been tested using practical data in the context of shape-based image segmentation. Some relevant practical issues, such as missing detected landmarks and selection of the regularization parameter are also briefly discussed.


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    Titel :

    Landmark-based shape deformation with topology-preserving constraints


    Beteiligte:
    Song Wang, (Autor:in) / Jim Xiuquan Ji, (Autor:in) / Zhi-Pei Liang, (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    691048 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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