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Proceedings Paper

Iterative neural networks for skeletonization and thinning
Author(s): Raghu J. Krishnapuram; Ling-Fan Chen
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Paper Abstract

Skeletons provide a compact and elegant description of the shape of binary objects. They are usually obtained by performing a distance transformation on the original binary data or by thinning. In this paper we summarize some of the existing techniques in this area and introduce iterative neural networks for skeletonization and thinning. The networks are trained to learn a deletion rule and they iteratively delete points from the objects until only the skeleton remains. 1.

Paper Details

Date Published: 1 February 1991
PDF: 11 pages
Proc. SPIE 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods, (1 February 1991); doi: 10.1117/12.25218
Show Author Affiliations
Raghu J. Krishnapuram, Univ. of Missouri/Columbia (United States)
Ling-Fan Chen, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 1382:
Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods
David P. Casasent, Editor(s)

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