THE ADVANCED DISTRIBUTED REGION OF INTEREST TOOL

This paper details recent work on the use of low-level features for the identification of regions of interest in images. Using-low-level features, the system classifies regions in the image via probability densities estimates for each class. These densities are estimated semi-parametrically, giving...

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Bibliographic Details
Published inPattern recognition Vol. 31; no. 12; pp. 2103 - 2118
Main Authors MARCHETTE, D.J., SOLKA, J.L., GUIDRY, R., GREEN, J.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.12.1998
Elsevier Science
Subjects
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Fractal dimension
Genetic algorithms
Low-level vision
Mixture models
Pattern recognition. Digital image processing. Computational geometry
Statistical pattern recognition
Theoretical computing
Low-level vision
Fractal dimension
Statistical pattern recognition
Mixture models
Genetic algorithms
System architecture
Probability density
Computer vision
Parameter estimation
Fractal
Image processing
Genetic algorithm
Classification
Pattern recognition
Edge detection
Speech processing
Pattern extraction
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Summary:This paper details recent work on the use of low-level features for the identification of regions of interest in images. Using-low-level features, the system classifies regions in the image via probability densities estimates for each class. These densities are estimated semi-parametrically, giving the system great flexibility in the functional form of the densities. This paper details the environment designed to allow easy implementation of pattern recognition algorithms.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0031-3203
1873-5142
DOI:10.1016/S0031-3203(98)00025-9