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

页码：2020 / U1

页数：5

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