|
rdf:type |
|
|
lifeskim:mentions |
|
|
pubmed:issue |
12
|
|
pubmed:dateCreated |
2006-11-19
|
|
pubmed:abstractText |
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.
|
|
pubmed:language |
eng
|
|
pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:status |
MEDLINE
|
|
pubmed:month |
Dec
|
|
pubmed:issn |
0162-8828
|
|
pubmed:author |
|
|
pubmed:issnType |
Print
|
|
pubmed:volume |
28
|
|
pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
|
|
pubmed:pagination |
2020-4
|
|
pubmed:meshHeading |
pubmed-meshheading:17108374-Algorithms,
pubmed-meshheading:17108374-Artificial Intelligence,
pubmed-meshheading:17108374-Automatic Data Processing,
pubmed-meshheading:17108374-Image Enhancement,
pubmed-meshheading:17108374-Image Interpretation, Computer-Assisted,
pubmed-meshheading:17108374-Information Storage and Retrieval,
pubmed-meshheading:17108374-Pattern Recognition, Automated,
pubmed-meshheading:17108374-Reproducibility of Results,
pubmed-meshheading:17108374-Sensitivity and Specificity,
pubmed-meshheading:17108374-Writing
|
|
pubmed:year |
2006
|
|
pubmed:articleTitle |
Symbol recognition with kernel density matching.
|
|
pubmed:affiliation |
Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong. wanzhang@cityu.edu.hk
|
|
pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
|
