GA-based learning for a model-based object recognition system

被引：8

作者：

Soodamani, R ^{[1
]}

Liu, ZQ ^{[1
]}

机构：

[1] Univ Melbourne, Dept Comp Sci, CVMIL, Carlton, Vic 3053, Australia

来源：

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2000年 / 23卷 / 02期

关键词：

model based object recognition; range images; fuzzy attributes; learning; genetic algorithm; performance enhancement;

D O I：

10.1016/S0888-613X(99)00036-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This 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 generalization capability by recognizing unknown instances of known objects are greatly improved. Performance enhancement is achieved by incorporating an off-line learning mechanism using genetic algorithm in the feedback path of the recognition system. (C) 2000 Elsevier Science Inc. All rights reserved.

引用

页码：85 / 109

页数：25

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