Granulometric classifiers from small samples
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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.