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Target recognition by Fast Optimal Fuzzy C-Means image segmentation

Target recognition by Fast Optimal Fuzzy C-Means image segmentation We propose a novel Fast Optimal Fuzzy C-Means (FOFCM) clustering algorithm to improve target recognition in image processing. FOFCM can find the best clustering number of images by exploiting the characteristics of the given images, and reduce the segmentation time significantly at the same time. Experiments on serials images are employed to demonstrate the performance of FOFCM. The experiment results show that FOFCM has significantly better performance and lower complexity than previously proposed approaches. The correct recognition rate is increased by 29.24%, which is 94.59%. The clustering efficiency is improved by 6–132 times. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Target recognition by Fast Optimal Fuzzy C-Means image segmentation

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2011.041602
Publisher site
See Article on Publisher Site

Abstract

We propose a novel Fast Optimal Fuzzy C-Means (FOFCM) clustering algorithm to improve target recognition in image processing. FOFCM can find the best clustering number of images by exploiting the characteristics of the given images, and reduce the segmentation time significantly at the same time. Experiments on serials images are employed to demonstrate the performance of FOFCM. The experiment results show that FOFCM has significantly better performance and lower complexity than previously proposed approaches. The correct recognition rate is increased by 29.24%, which is 94.59%. The clustering efficiency is improved by 6–132 times.

Journal

International Journal of Signal and Imaging Systems EngineeringInderscience Publishers

Published: Jan 1, 2011

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