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Proceedings Paper

Hierarchical Local Symmetry: 2-D Shape Representation
Author(s): Kyugon Cho; Stanley M. Dunn
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Paper Abstract

The overall goal of our research is to build a vision learning system which can learn to classify objects from 2-D contour information. The visual representation method for such a vision learning system, called the hierarchical local symmetry (IILS), will be discussed in this paper. The definition and algorithms of smoothed local symmetry (SLS) is reviewed, which was introduced by Brady as a method satisfying stability versus sensitivity criteria of visual representation method. In this paper, HLS, modified SLS, are formalized and a new algorithm to compute the HLS is described. HLS eliminates some redundant information in the SLS and gives us hierarchical information. It also makes it possible to devise more efficient algorithms than that of SLS. Normalized polar coordinate representation (NITII) is used to store the computed HLS with translation, scale, and rotation invariance. Transforming the HLS into the NPCR where the learning process can be performed is also discussed.

Paper Details

Date Published: 1 March 1990
PDF: 12 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969735
Show Author Affiliations
Kyugon Cho, Rutgers University (United States)
Stanley M. Dunn, Rutgers University (United States)

Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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