Granulometric classifiers from small samples Conference Paper uri icon

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.

name of conference

  • Image Processing: Algorithms and Systems

published proceedings

  • IMAGE PROCESSING: ALGORITHMS AND SYSTEMS

author list (cited authors)

  • Balagurunathan, Y., Hashimoto, R. F., Kim, S., Barrera, J., & Dougherty, E. R.

citation count

  • 0

complete list of authors

  • Balagurunathan, Y||Hashimoto, RF||Kim, S||Barrera, J||Dougherty, ER

editor list (cited editors)

  • Dougherty, E. R., Astola, J. T., & Egiazarian, K. O.

publication date

  • May 2002