Symbol recognition with kernel density matching.
basic_science · Level V
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- Record sourced from PubMed, PMID 17108374.
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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.
Medical subject headings
- Algorithms
- Artificial Intelligence
- Electronic Data Processing
- Image Enhancement
- Image Interpretation, Computer-Assisted
- Pattern Recognition, Automated
- Writing