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Proceedings Paper

Extraction of shape-based properties
Author(s): Neelima Shrikhande
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Paper Abstract

A model of an object is an image consisting of features of an object. The input is a gray scale image from which features are computed. In his doctoral thesis, J. L. Chen used a model based approach to object recognition. His method is based on Rosin's work for extraction of parts. Both model and scene features are contour based properties. The scene features are matched to the model features by indexing. The following features are computed: convexity, compactness, roundedness, skewness, and the first moment invariant (all using Rosin's algorithm). In this paper, we extend the feature description to include internal and external parts of an object. We use the interpretation tree approach for matching the scene to model, where constraints such as distances and angles between parts are used to prune the interpretation tree. We compare the efficiency of the interpretation tree approach with the indexing method on the data that was used in previous experiments by J. L. Chen.

Paper Details

Date Published: 6 October 1998
PDF: 7 pages
Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); doi: 10.1117/12.325761
Show Author Affiliations
Neelima Shrikhande, Central Michigan Univ. (United States)

Published in SPIE Proceedings Vol. 3522:
Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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