Learning decompositional shape models from examples.
Published: 2005
Language: eng
Number of pages: 78
Description: 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.
Notes: Source: Masters Abstracts International, Volume: 44-02, page: 0937.

Thesis (M.Sc.)--University of Toronto, 2005.

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