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Segmentation of Noisy Binary Images Containing Irregular Shaped Objects using Genetic Algorithm

by B. D. Phulpagar, R. S. Bichkar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 10
Year of Publication: 2013
Authors: B. D. Phulpagar, R. S. Bichkar
10.5120/12534-9220

B. D. Phulpagar, R. S. Bichkar . Segmentation of Noisy Binary Images Containing Irregular Shaped Objects using Genetic Algorithm. International Journal of Computer Applications. 72, 10 ( June 2013), 56-62. DOI=10.5120/12534-9220

@article{ 10.5120/12534-9220,
author = { B. D. Phulpagar, R. S. Bichkar },
title = { Segmentation of Noisy Binary Images Containing Irregular Shaped Objects using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 10 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 56-62 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number10/12534-9220/ },
doi = { 10.5120/12534-9220 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:37.107706+05:30
%A B. D. Phulpagar
%A R. S. Bichkar
%T Segmentation of Noisy Binary Images Containing Irregular Shaped Objects using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 10
%P 56-62
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Segmentation is one of the most important steps in image analysis. Image segmentation is the process of separating the foreground objects from the background. The earlier techniques use Genetic Algorithms (GAs) to separate the images containing regular, circular and elliptical-shaped objects from the background. The proposed technique uses the GA to segment the images containing irregular shaped objects. The Parallel Computing Genetic Algorithm (PCGA) implemented using Matlab PCT toolbox is also used to reduce computation time of image segmentation. The GA and PCGA are implemented using one-point, two-point and multiple-point crossover operators. The proposed GA-based approach gives us good results for noisy images containing irregular shaped objects as well as circular or elliptical and rectangular objects. The results obtained give 94% to 99% segmentation accuracy for different types of noise (Poisson, salt and pepper, Gaussian and Speckle) and high noise levels (SNR ranging between 1. 75 dB to 8. 75 dB). A significant speedup is obtained by using PCGA compared with the serial GA implementation.

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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Image Segmentation Parallel Computing

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