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Proceedings Paper

Enhanced Voronoi diagram method for segmentation of partially overlapped thin objects
Author(s): Q. M. Jonathan Wu; Xiaotian Shi; Ali Jerbi; Chander Prakash Grover
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Paper Abstract

A new clustering technique is developed for segmentation of partially overlapped thin objects. The technique is based on an enhanced Voronoi diagram which partitions random data into clusters where intra-class members possess features of close similarity. An important aspect of this study consists of introducing predicting directional vectors, reminiscent of the first and second principal components, in order to achieve better partitioning of data clusters. Computer implementations of this new partitioning scheme illustrate superior partitioning performance over the standard Voronoi approach. It is shown that the new scheme minimizes the error in data classification. A mathematical framework is provided in support of this new clustering method. Experimental results on partitioning glass fibers are presented to illustrate application of the technique to object segmentation.

Paper Details

Date Published: 1 October 1998
PDF: 9 pages
Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); doi: 10.1117/12.323167
Show Author Affiliations
Q. M. Jonathan Wu, National Research Council Canada (Canada)
Xiaotian Shi, National Research Council Canada (Canada)
Ali Jerbi, National Research Council Canada (Canada)
Chander Prakash Grover, National Research Council Canada (Canada)

Published in SPIE Proceedings Vol. 3460:
Applications of Digital Image Processing XXI
Andrew G. Tescher, Editor(s)

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