ADVANCED MACHINE LEARNING TECHNIQUES FOR COMPUTER VISION

被引：0

作者：

MOSCATELLI, S ^{[1
]}

KODRATOFF, Y ^{[1
]}

机构：

[1] UNIV PARIS 11,LRI,F-91405 ORSAY,FRANCE

来源：

LECTURE NOTES IN ARTIFICIAL INTELLIGENCE | 1992年 / 617卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptative target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems.

引用

页码：161 / 197

页数：37

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