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Hybrid Algorithm for Edge Detection using Fuzzy Inference System

by Mohammed Y. Kamil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 5
Year of Publication: 2014
Authors: Mohammed Y. Kamil
10.5120/17814-8643

Mohammed Y. Kamil . Hybrid Algorithm for Edge Detection using Fuzzy Inference System. International Journal of Computer Applications. 102, 5 ( September 2014), 37-43. DOI=10.5120/17814-8643

@article{ 10.5120/17814-8643,
author = { Mohammed Y. Kamil },
title = { Hybrid Algorithm for Edge Detection using Fuzzy Inference System },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 5 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number5/17814-8643/ },
doi = { 10.5120/17814-8643 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:22.050241+05:30
%A Mohammed Y. Kamil
%T Hybrid Algorithm for Edge Detection using Fuzzy Inference System
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 5
%P 37-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a novel edge detection algorithm based on fuzzy inference system. Fuzzy inference system has been designed for three inputs using Gaussian membership functions, one output using Triangle membership function that expresses whether the pixel under consideration is "Low", "Medium" or "High" pixel. Rules base comprises of twenty-seven rules, it applied a Mamdani FIS by taking a mask over the image of 3x3 size. The results obtained are compared with Canny edge method and performance parameters used are PSNR of true to false edges. Experimental results shows that the proposed method gives higher PSNR values when compared with Canny edge detection algorithm under all states.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Edge detection Fuzzy logic Fuzzy inference system image processing

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