A hybrid approach to edge detection using fuzzy sets and cellular learning automata

Edge detection technique has a key role in machine vision and image understanding systems. In machine vision motion track and measurement system based on discrete feature, the exact feature edge orientation in the image is the precondition of the successful completion of the vision measurement task....

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Bibliographic Details
Main Author:	Ghanizadeh, Afshin
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11571/4/AfshinGhanizadehMFSKSM2010.pdf
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id	my-utm-ep.11571
record_format	uketd_dc
spelling	my-utm-ep.115712017-09-27T04:07:34Z A hybrid approach to edge detection using fuzzy sets and cellular learning automata 2010-04 Ghanizadeh, Afshin QA75 Electronic computers. Computer science Edge detection technique has a key role in machine vision and image understanding systems. In machine vision motion track and measurement system based on discrete feature, the exact feature edge orientation in the image is the precondition of the successful completion of the vision measurement task. Edge detection is one of the most commonly used operations in image analysis and digital image processing. Many studies have been conducted to enhance the edge detection algorithms in various domains. In this thesis, a robust edge detection method based on Fuzzy Sets and Cellular Learning Automata (CLA) is proposed. The proposed method includes two steps: (a) extracting the edges and (b) enhancing them by removing unwanted edges and eliminating false edges caused by noise. The performance of the proposed edge detector is tested on various test images with different sizes. The results are compared with Canny and Sobel edge detection methods. Simulation results reveal that the proposed Fuzzy-CLA method can detect edges more smoothly in a shorter amount of time compared to the other edge detectors. 2010-04 Thesis http://eprints.utm.my/id/eprint/11571/ http://eprints.utm.my/id/eprint/11571/4/AfshinGhanizadehMFSKSM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ghanizadeh, Afshin A hybrid approach to edge detection using fuzzy sets and cellular learning automata
description	Edge detection technique has a key role in machine vision and image understanding systems. In machine vision motion track and measurement system based on discrete feature, the exact feature edge orientation in the image is the precondition of the successful completion of the vision measurement task. Edge detection is one of the most commonly used operations in image analysis and digital image processing. Many studies have been conducted to enhance the edge detection algorithms in various domains. In this thesis, a robust edge detection method based on Fuzzy Sets and Cellular Learning Automata (CLA) is proposed. The proposed method includes two steps: (a) extracting the edges and (b) enhancing them by removing unwanted edges and eliminating false edges caused by noise. The performance of the proposed edge detector is tested on various test images with different sizes. The results are compared with Canny and Sobel edge detection methods. Simulation results reveal that the proposed Fuzzy-CLA method can detect edges more smoothly in a shorter amount of time compared to the other edge detectors.
format	Thesis
qualification_level	Master's degree
author	Ghanizadeh, Afshin
author_facet	Ghanizadeh, Afshin
author_sort	Ghanizadeh, Afshin
title	A hybrid approach to edge detection using fuzzy sets and cellular learning automata
title_short	A hybrid approach to edge detection using fuzzy sets and cellular learning automata
title_full	A hybrid approach to edge detection using fuzzy sets and cellular learning automata
title_fullStr	A hybrid approach to edge detection using fuzzy sets and cellular learning automata
title_full_unstemmed	A hybrid approach to edge detection using fuzzy sets and cellular learning automata
title_sort	hybrid approach to edge detection using fuzzy sets and cellular learning automata
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/11571/4/AfshinGhanizadehMFSKSM2010.pdf
_version_	1747814871407263744

A hybrid approach to edge detection using fuzzy sets and cellular learning automata

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