Symbol recognition with kernel density matching
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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
Published on: January 5, 2024
Wan Zhang1, Liu Wenyin, Kun Zhang
1Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong. wanzhang@cityu.edu.hk
IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 17, 2006
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.
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