Granulometric classifiers from small samples

Yoganand Balagurunathan; Ronaldo F. Hashimoto; Seungchan Kim; Junior Barrera; Edward R. Dougherty

doi:10.1117/12.467971

22 May 2002 Granulometric classifiers from small samples

Yoganand Balagurunathan, Ronaldo F. Hashimoto, Seungchan Kim, Junior Barrera, Edward R. Dougherty

Proceedings Volume 4667, Image Processing: Algorithms and Systems; (2002) https://doi.org/10.1117/12.467971
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

Morphological granulometries and their moment features are used as shape descriptors. These features find application in classification, segmentation and estimation. Design of classifiers has been a primary goal of most pattern recognition problems. Small sample design is often a constraint when designing classifiers. We use a recently proposed small sample design method in which the sample observations are spread with a probability mass and the classifiers designed on the spread mass. The designed classifiers are more reliability for relative to the population. Two issues are addressed: design of granulometric classifiers using a small sample, and granulometric classification based on a very small number of features.

Citation Download Citation

Yoganand Balagurunathan, Ronaldo F. Hashimoto, Seungchan Kim, Junior Barrera, and Edward R. Dougherty "Granulometric classifiers from small samples", Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002); https://doi.org/10.1117/12.467971

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