Enhanced Voronoi diagram method for segmentation of partially overlapped thin objects

Q. M. Jonathan Wu; Xiaotian Shi; Ali Jerbi; Chander Prakash Grover

doi:10.1117/12.323167

1 October 1998 Enhanced Voronoi diagram method for segmentation of partially overlapped thin objects

Q. M. Jonathan Wu, Xiaotian Shi, Ali Jerbi, Chander Prakash Grover

Proceedings Volume 3460, Applications of Digital Image Processing XXI; (1998) https://doi.org/10.1117/12.323167
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

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.

Citation Download Citation

Q. M. Jonathan Wu, Xiaotian Shi, Ali Jerbi, and Chander Prakash Grover "Enhanced Voronoi diagram method for segmentation of partially overlapped thin objects", Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); https://doi.org/10.1117/12.323167

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