27 February 1996 Robust parallel clustering algorithm for image segmentation
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Proceedings Volume 2727, Visual Communications and Image Processing '96; (1996) https://doi.org/10.1117/12.233288
Event: Visual Communications and Image Processing '96, 1996, Orlando, FL, United States
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jose Gerardo Tamez-Pena, Jose Gerardo Tamez-Pena, Arnulfo Perez, Arnulfo Perez, } "Robust parallel clustering algorithm for image segmentation", Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233288; https://doi.org/10.1117/12.233288


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