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Proceedings Paper

Clustering granulometric features
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Paper Abstract

Granulometric features have been widely used for classification, segmentation and recently in estimation of parameters in shape models. In this paper we study the inference of clustering based on granulometric features for a collection of structuring probes in the context of random models. We use random Boolean models to represent grains of different shapes and structure. It is known that granulometric features are excellent descriptors of shape and structure of grains. Inference based on clustering these features helps to analyze the consistency of these features and clustering algorithms. This greatly aids in classifier design and feature selection. Features and the order of their addition play a role in reducing the inference errors. We study four different types of feature addition methods and the effect of replication in reducing the inference errors.

Paper Details

Date Published: 22 May 2002
PDF: 7 pages
Proc. SPIE 4667, Image Processing: Algorithms and Systems, (22 May 2002); doi: 10.1117/12.468006
Show Author Affiliations
Marcel Brun, Univ. de Sao Paulo (Brazil)
Yoganand Balagurunathan, Texas A&M Univ. (United States)
Junior Barrera, Univ. de Sao Paulo (Brazil)
Edward R. Dougherty, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 4667:
Image Processing: Algorithms and Systems
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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