Learning decompositional shape models from examples.

Learning decompositional shape models from ex ...
Alex Levinshtein, Alex Levinsh ...
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January 24, 2010 | History

Learning decompositional shape models from examples.

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.

Publish Date
Language
English
Pages
78

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Book Details


Edition Notes

Source: Masters Abstracts International, Volume: 44-02, page: 0937.

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

Electronic version licensed for access by U. of T. users.

GERSTEIN MICROTEXT copy on microfiche (1 microfiche).

The Physical Object

Pagination
78 leaves.
Number of pages
78

Edition Identifiers

Open Library
OL19216577M
ISBN 10
0494071885

Work Identifiers

Work ID
OL12683369W

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