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Proceedings Paper

Robust parallel clustering algorithm for image segmentation
Author(s): Jose Gerardo Tamez-Pena; Arnulfo Perez
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Paper Abstract

This paper describes a hierarchical parallel implementation of two clustering algorithms applied to the segmentation of multidimensional images and range images. The proposed hierarchical parallel implementation results in a fast robust segmentation algorithm that can be applied in a number of practical computer vision problems. The clustering process is divided in two basic steps. First, a fast sequential clustering algorithm performs a simple analysis of the image data, which results in a sub optimal classification of the image features. Second, the resulting clusters are analyzed using the minimum volume ellipsoid estimator. The second step is to merge the similar clusters using the number and shape of the ellipsoidal clusters that best represents the data. Both algorithms are implemented in a parallel computer architecture that speeds up the classification task. The hierarchical clustering algorithm is compared against the fuzzy k-means clustering algorithm showing that both approaches gave comparable segmentation results. The hierarchical parallel implementation is tested in synthetic multidimensional images and real range images.

Paper Details

Date Published: 27 February 1996
PDF: 12 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233288
Show Author Affiliations
Jose Gerardo Tamez-Pena, Univ. of Rochester (United States)
Arnulfo Perez, Instituto Tecnologico y de Estudios Superiores de Monterrey (Mexico)


Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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