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Image Segmentation based on FUZZY GLSC Histogram with Dynamic Similarity Discrimination Factor

by N. Swathi, K. Ravi Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 14
Year of Publication: 2012
Authors: N. Swathi, K. Ravi Kumar
10.5120/8490-2438

N. Swathi, K. Ravi Kumar . Image Segmentation based on FUZZY GLSC Histogram with Dynamic Similarity Discrimination Factor. International Journal of Computer Applications. 53, 14 ( September 2012), 28-35. DOI=10.5120/8490-2438

@article{ 10.5120/8490-2438,
author = { N. Swathi, K. Ravi Kumar },
title = { Image Segmentation based on FUZZY GLSC Histogram with Dynamic Similarity Discrimination Factor },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 14 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number14/8490-2438/ },
doi = { 10.5120/8490-2438 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:54:06.060753+05:30
%A N. Swathi
%A K. Ravi Kumar
%T Image Segmentation based on FUZZY GLSC Histogram with Dynamic Similarity Discrimination Factor
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 14
%P 28-35
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image processing applications performs image segmentation as pre-processing technique to extract the features for next stage. The application performance depends on image segmentation, to process the foreground or background objects. The image segmentation plays a vital role in computer vision and image processing applications. In spite of having many thresholding techniques in literature they have their own limitations. This paper proposes a new method of thresholding using Gray Level Spatial Correlation (GLSC) histogram with a dynamic similarity discrimination factor ( ) and Fuzzy logic in deciding the threshold using Shannon's entropy. The similarity discrimination factor ( ) is made dynamic by considering the absolute difference between the global and local mean of the image. Calculating the threshold in the Fuzzyfied region makes the segmentation process the most time efficient than the existing methods. Experimental results prove better efficiency ( ) than the existing methods. The technique out performs in case of low contrast images.

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Index Terms

Computer Science
Information Sciences

Keywords

Entropy Fuzzyfication Fuzzyfied image GLSC histogram threshold

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