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
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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}, doi = {} } |