Symbol recognition with kernel density matching

被引：31
|
作者：
Zhang, Wan
Liu, Wenyin
Zhang, Kun
机构：
[1] Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2006年 / 28卷 / 12期
关键词：
symbol recognition; graphics recognition; kernel density; independent component analysis;
D O I：
10.1109/TPAMI.2006.254
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach in various situations.
引用
收藏
页码：2020 / U1
页数：5
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