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Nano Fiber Images Thresholding based on Imperial Competitive Algorithm

by Neda Dehghan, Pedram Payvandy, Mohamad Ali Tavanaei
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 6
Year of Publication: 2014
Authors: Neda Dehghan, Pedram Payvandy, Mohamad Ali Tavanaei
10.5120/17380-7916

Neda Dehghan, Pedram Payvandy, Mohamad Ali Tavanaei . Nano Fiber Images Thresholding based on Imperial Competitive Algorithm. International Journal of Computer Applications. 99, 6 ( August 2014), 37-41. DOI=10.5120/17380-7916

@article{ 10.5120/17380-7916,
author = { Neda Dehghan, Pedram Payvandy, Mohamad Ali Tavanaei },
title = { Nano Fiber Images Thresholding based on Imperial Competitive Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 6 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number6/17380-7916/ },
doi = { 10.5120/17380-7916 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:31.708628+05:30
%A Neda Dehghan
%A Pedram Payvandy
%A Mohamad Ali Tavanaei
%T Nano Fiber Images Thresholding based on Imperial Competitive Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 6
%P 37-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nano fibers are widely used in various industries, therefor knowing the morphology is important. Thresholding is a simple but effective technique for image segmentation. The goal of image segmentation is to cluster pixels into salient image regions, i. e. , regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper a novel method is proposed for performing image segmentation. The purpose of this paper proposed an imperial competitive algorithm with the objective function from Kmeans clustering algorithm for Nano fibers image thresholding. Then algorithm used, with the algorithms such as: global threshold, local threshold, Kmeans clustering algorithm and FCM methods were compared. Finally, a powerful algorithm for image thresholding is found. The comparisons and experimental results show that purposed algorithm is better than other methods particularly global and local thresholding, Kmeans and even FCM.

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Index Terms

Computer Science
Information Sciences

Keywords

Imperial Competitive Algorithm Segmention Thresholding Kmeans Clustering Nano Fiber.

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