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Learning fuzzy modelling through genetic algorithm for object recognition

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2 Author(s)
Soodamani, R. ; Dept. of Comput. Sci., Melbourne Univ., Australia ; Liu, Z.Q.

The paper proposes a genetic-algorithm-based learning strategy that models membership functions of the fuzzy attributes of surfaces in a model based machine vision system. The objective function aims at enhancing recognition performance in terms of maximizing the degree of discrimination among classes. As a result, the accuracy of recognizing known instances of objects and generalisation capability by recognizing unknown instances of known objects are greatly improved. The performance enhancement of a model based object recognition system consisting of a set of synthetic range images is established by incorporating a dynamic off-line learning mechanism using a genetic algorithm in the feedback path of the system

Published in:
Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on

Date of Conference: 4-9 May 1998

Page(s):
656 - 659
Meeting Date :
04 May 1998-09 May 1998
Print ISBN:
0-7803-4869-9
INSPEC Accession Number:
6016633
Conference Location :
Anchorage, AK
Digital Object Identifier :
10.1109/ICEC.1998.700117
Product Type:
Conference Publications

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