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Automatic Image Segmentation using Ultra Fuzziness

by Ch.v. Narayana, E. Sreenivasa Reddy, M. Seetharama Prasad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 12
Year of Publication: 2012
Authors: Ch.v. Narayana, E. Sreenivasa Reddy, M. Seetharama Prasad
10.5120/7677-0977

Ch.v. Narayana, E. Sreenivasa Reddy, M. Seetharama Prasad . Automatic Image Segmentation using Ultra Fuzziness. International Journal of Computer Applications. 49, 12 ( July 2012), 6-13. DOI=10.5120/7677-0977

@article{ 10.5120/7677-0977,
author = { Ch.v. Narayana, E. Sreenivasa Reddy, M. Seetharama Prasad },
title = { Automatic Image Segmentation using Ultra Fuzziness },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 12 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number12/7677-0977/ },
doi = { 10.5120/7677-0977 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:46:04.851775+05:30
%A Ch.v. Narayana
%A E. Sreenivasa Reddy
%A M. Seetharama Prasad
%T Automatic Image Segmentation using Ultra Fuzziness
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 12
%P 6-13
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, an automatic histogram threshold approach based on a fuzzy measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum/maximum of a entropy criterion function are avoided. Hamid R Tizhoosh defined a membership function to measure the image fuzziness, which makes the methodology totally supervised. We attempt to automate the process by taking an alternate approach. For low contrast images contrast enhancement is assumed. Experimental results demonstrate a quantitative improvement.

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Index Terms

Computer Science
Information Sciences

Keywords

Type-I fuzzy Type-II fuzzy ultrafuzziness

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