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Symbol Recognition with Kernel Density Matching

2006

Article

ei


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.

Author(s): Zhang, W. and Wenyin, L. and Zhang, K.
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 28
Number (issue): 12
Pages: 2020-2024
Year: 2006
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0

Links: Web

BibTex

@article{ZhangWZ2006,
  title = {Symbol Recognition with Kernel Density Matching},
  author = {Zhang, W. and Wenyin, L. and Zhang, K.},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {28},
  number = {12},
  pages = {2020-2024},
  year = {2006}
}