We propose using the inner-distance between landmark points to build shape descriptors. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that the inner-distance is articulation insensitive and more effective at capturing complex shapes with part structures than Euclidean distance. To demonstrate this idea, it is used to build a new shape descriptor based on shape contexts. After that, we design a dynamic programming based method for shape matching and comparison. We have tested our approach on a variety of shape databases including an articulated shape dataset, MPEG7 CE-Shape-1, Kimia silhouettes, a Swedish leaf database and a human motion silhouette dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.


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    Title :

    Using the inner-distance for classification of articulated shapes


    Contributors:
    Ling, H. (author) / Jacobs, D.W. (author)


    Publication date :

    2005-01-01


    Size :

    470793 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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