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Symbol Recognition with Kernel Density Matching

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3 Author(s)
Wan Zhang ; Dept. of Comput. Sci., City Univ. of Hong Kong ; Liu Wenyin ; Kun Zhang

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

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:28 ,  Issue: 12 )

Date of Publication:

Dec. 2006

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