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Reseach Article

Color Image Segmentation using ERKFCM

by C. Mythili, V.kavitha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 20
Year of Publication: 2012
Authors: C. Mythili, V.kavitha
10.5120/5809-8074

C. Mythili, V.kavitha . Color Image Segmentation using ERKFCM. International Journal of Computer Applications. 41, 20 ( March 2012), 21-28. DOI=10.5120/5809-8074

@article{ 10.5120/5809-8074,
author = { C. Mythili, V.kavitha },
title = { Color Image Segmentation using ERKFCM },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 20 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 21-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number20/5809-8074/ },
doi = { 10.5120/5809-8074 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:06.798946+05:30
%A C. Mythili
%A V.kavitha
%T Color Image Segmentation using ERKFCM
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 20
%P 21-28
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Color image segmentation is an important task for computer vision. The segmented RGB color space is not more reliable and accurate for computer vision applications. For this purpose, the proposed approach combines different color spaces such as RGB, HSV, YIQ and XYZ for image segmentation. The combine segmentation of various color spaces to give more accurate segmentation result compared to segmentation of single color space. The images are segmented using K- means clustering and Effective robust kernelized fuzzy c-means(ERKFCM). Two significant criteria namely PSNR (Peak Signal to Noise Ratio) and MSE (Mean square error) are used to evaluate the performance.

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Index Terms

Computer Science
Information Sciences

Keywords

Color Image Segmentation Color Spaces K-means Clustering Erkfcm And Image Fusion