Binarization Segmentation Based on Fuzzy Clustering Theory

Hong Jun Wang; Hao Song; Hui Zhao; You Jun Yue

doi:10.4028/www.scientific.net/AMM.740.584

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 740Binarization Segmentation Based on Fuzzy...

Binarization Segmentation Based on Fuzzy Clustering Theory

Abstract:

Aiming at the problem of color image two value segmentation, present an improved two value plant image segmentation method based on fuzzy clustering theory. In the light of the problem that fuzzy clustering algorithm is difficult to segment the low SNR image, improve Membership function; Aiming at the problem that image pixel information is too large, mapping image information to the gray feature space, treat the same gray pixel as a whole, improve operation efficiency. The experimental results show that, the improved algorithm has stronger anti noise ability, higher segmentation accuracy, shorter operation time, has good prospects for engineering applications.

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Periodical:

Applied Mechanics and Materials (Volume 740)

Pages:

584-587

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.740.584

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Online since:

March 2015

Authors:

Hong Jun Wang, Hao Song*, Hui Zhao, You Jun Yue

Keywords:

Binarization, Fuzzy Clustering, Image Segmentation

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* - Corresponding Author

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