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Learning Hierarchical Shape Models from Examples

  • Alex Levinshtein
  • Cristian Sminchisescu
  • Sven Dickinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)

Abstract

We present an algorithm for automatically constructing a decompositional shape model from examples. Unlike current approaches to structural model acquisition, in which one-to-one correspondences among appearance-based features are used to construct an exemplar-based model, we search for many-to-many correspondences among qualitative shape features (multi-scale ridges and blobs) to construct a generic shape model. Since such features are highly ambiguous, their structural context must be exploited in computing correspondences, which are often many-to-many. The result is a Marr-like abstraction hierarchy, in which a shape feature at a coarser scale can be decomposed into a collection of attached shape features at a finer scale. We systematically evaluate all components of our algorithm, and demonstrate it on the task of recovering a decompositional model of a human torso from example images containing different subjects with dissimilar local appearance.

Keywords

Decompositional Model Abstraction Hierarchy Spherical Code Node Correspondence Attachment Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alex Levinshtein
    • 1
  • Cristian Sminchisescu
    • 1
    • 2
  • Sven Dickinson
    • 1
  1. 1.University of TorontoCanada
  2. 2.TTI-CChicagoUSA

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