.png){kind=icon}
返回
GA-based learning for a model-based object recognition system
.png){kind=icon}
PRE
AI
DOI：10.1016/S0888-613X(99)00036-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
model based object recognition
range images
fuzzy attributes
learning
genetic algorithm
performance enhancement
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
3.0K
被引数:
5.1K
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏Five‐ and Eight‐Month‐Old Infants Recognize Their Faces and Voices as Familiar and Social Stimuli
PREAI
分享收藏
查看更多内容