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Proceedings Paper

A Neural Network Technique for Feature Extraction to Improve Object Recognition
Author(s): Michael C. Stinson
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Paper Abstract

This paper reports on an object recognition system that combines a neural network global approach with assistance from local features. The Relevant Feature Technique uses a global classifier to determine a characteristic class and uses the local relevant features of that class to improve the recognition of the visual object. Predominantly local features are difficult to utilize in a neural network environment because they are local, and may not be considered significant to the globally sensitive neural network. In the technique shown here, locally relevant features are used to influence and constrain global recognition process.

Paper Details

Date Published: 1 March 1990
PDF: 7 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969755
Show Author Affiliations
Michael C. Stinson, Central Michigan-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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