Symbol Recognition with Kernel Density Matching

Authors:
Wan Zhang;Liu Wenyin;Kun Zhang
Affiliations:
-;IEEE;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2006

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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.