Symbol Recognition with Kernel Density Matching

Author

Zhang, Wan ; Wenyin, Liu ; Zhang, Kun

Author_Institution

Dept. of Comput. Sci., City Univ. of Hong Kong

Volume

28

Issue

12

fYear

2006

Firstpage

2020

Lastpage

2024

Abstract

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

Keywords

character recognition; gradient methods; graph theory; independent component analysis; search problems; Kullback-Leibler divergence; gradient-based angle searching; graphic symbols; graphics recognition; independent component analysis; kernel density matching; similarity assessment; symbol recognition; Degradation; Density measurement; Graphics; Independent component analysis; Kernel; Noise robustness; Probability distribution; Senior members; Shape; Testing; Symbol recognition; graphics recognition; independent component analysis.; kernel density; Algorithms; Artificial Intelligence; Automatic Data Processing; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Writing;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2006.254

Filename

1717460