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Reseach Article

Iterative Optimization Scheme for Image Segmentation

Published on May 2013 by M. S. Karande, D. B. Kshirsagar
International Conference on Recent Trends in Engineering and Technology 2013
Foundation of Computer Science USA
ICRTET - Number 1
May 2013
Authors: M. S. Karande, D. B. Kshirsagar
68f1b17d-300b-4487-ba36-ab7a1699d44a

M. S. Karande, D. B. Kshirsagar . Iterative Optimization Scheme for Image Segmentation. International Conference on Recent Trends in Engineering and Technology 2013. ICRTET, 1 (May 2013), 22-25.

@article{
author = { M. S. Karande, D. B. Kshirsagar },
title = { Iterative Optimization Scheme for Image Segmentation },
journal = { International Conference on Recent Trends in Engineering and Technology 2013 },
issue_date = { May 2013 },
volume = { ICRTET },
number = { 1 },
month = { May },
year = { 2013 },
issn = 0975-8887,
pages = { 22-25 },
numpages = 4,
url = { /proceedings/icrtet/number1/11762-1310/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Trends in Engineering and Technology 2013
%A M. S. Karande
%A D. B. Kshirsagar
%T Iterative Optimization Scheme for Image Segmentation
%J International Conference on Recent Trends in Engineering and Technology 2013
%@ 0975-8887
%V ICRTET
%N 1
%P 22-25
%D 2013
%I International Journal of Computer Applications
Abstract

Image Segmentation is an integral part of computer vision. In this paper image segmentation is formulated as label relabeling problem under probability framework. To estimate the label configuration, an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori (MAP) estimation and the maximum likelihood (ML) estimation. This algorithm can automatically partition the image into regions without human intervention. The segmentation obtained is very close to human perception. Comparing to other state-of-the-art algorithms, extensive experiments have shown that this algorithm performs the best.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Maximum A Posteriori Maximum Likelihood