title: Symbol recognition with kernel density matching.
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.
authors.affiliation: Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong. wanzhang@cityu.edu.hk
authors.forename: Wan
authors.initials: W
authors.lastname: Zhang
authors.forename: Liu
authors.initials: L
authors.lastname: Wenyin
authors.forename: Kun
authors.initials: K
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001330:Electronic Data Processing / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014956:Writing