Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs

Objektif penyelidikan ini adalah untuk membangunkan satu kaedah peruasan kecacatan kimpalan automatik yang boleh meruas pelbagai jenis kecacatan kimpalan yang wujud dalam imej radiografi kimpalan. Kaedah segmentasi kecacatan automatik yang dibangunkan terdir:i daripada tiga algoritma utama, iaitu...

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Main Author:	Soo , Say Leong
Format:	Thesis
Language:	English
Published:	2006
Subjects:	TJ1-1570 Mechanical engineering and machinery
Online Access:	http://eprints.usm.my/31360/1/SOO_SAY_LEONG.pdf
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id	my-usm-ep.31360
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spelling	my-usm-ep.313602017-04-17T09:26:37Z Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs 2006-04 Soo , Say Leong TJ1-1570 Mechanical engineering and machinery Objektif penyelidikan ini adalah untuk membangunkan satu kaedah peruasan kecacatan kimpalan automatik yang boleh meruas pelbagai jenis kecacatan kimpalan yang wujud dalam imej radiografi kimpalan. Kaedah segmentasi kecacatan automatik yang dibangunkan terdir:i daripada tiga algoritma utama, iaitu algoritma penyingkiran label, algoritma pengenalpastian bahagian kimpalan dan algoritma segmentasi kecacatan kimpalan. Algoritma penyingkiran label dibangunkan untuk mengenalpasti dan menyingkirkan label yang terdapat pada imej radiograf kimpalan secara automatik, sebelum algoritma pengenalpastian bahagian kimpalan dan algortima segmentasi kecacatan diaplikasikan ke atas imej radiografi. Satu algoritma pengenalpastian bahagian kimpalan juga dibangunkan dengan tujuan mengenalpasti bahagian kimpalan dalam imej radiogaf secara automatik dengan menggunakan profil keamatan yang diperoleh daripada imej radiografi. The objective of the research is to develop an automatic weld defect segmentation methodology to segment different types of defects in radiographic images of welds. The segmentation methodology consists of three main algorithms. namely label removal algorithm. weld extraction algorithm and defect segmentation algorithm. The label removal algorithm was developed to detect and remove labels that are printed on weld radiographs automatically before weld extraction algorithm and defect detection algorithm are applied. The weld extraction algorithm was developed to locate and extract welds automatically from the intensity profiles taken across the image by using graphical analysis. This algorithm was able to extract weld from a radiograph regardless of whether the intensity profile is Gaussian or otherwise. This method is an improvement compared to the previous weld extraction methods which are limited to weld image with Gaussian intensity profiles. Finally. a defect segmentation algorithm was developed to segment the defects automatically from the image using background subtraction and rank leveling method. 2006-04 Thesis http://eprints.usm.my/31360/ http://eprints.usm.my/31360/1/SOO_SAY_LEONG.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Mekanikal
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TJ1-1570 Mechanical engineering and machinery
spellingShingle	TJ1-1570 Mechanical engineering and machinery Soo , Say Leong Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs
description	Objektif penyelidikan ini adalah untuk membangunkan satu kaedah peruasan kecacatan kimpalan automatik yang boleh meruas pelbagai jenis kecacatan kimpalan yang wujud dalam imej radiografi kimpalan. Kaedah segmentasi kecacatan automatik yang dibangunkan terdir:i daripada tiga algoritma utama, iaitu algoritma penyingkiran label, algoritma pengenalpastian bahagian kimpalan dan algoritma segmentasi kecacatan kimpalan. Algoritma penyingkiran label dibangunkan untuk mengenalpasti dan menyingkirkan label yang terdapat pada imej radiograf kimpalan secara automatik, sebelum algoritma pengenalpastian bahagian kimpalan dan algortima segmentasi kecacatan diaplikasikan ke atas imej radiografi. Satu algoritma pengenalpastian bahagian kimpalan juga dibangunkan dengan tujuan mengenalpasti bahagian kimpalan dalam imej radiogaf secara automatik dengan menggunakan profil keamatan yang diperoleh daripada imej radiografi. The objective of the research is to develop an automatic weld defect segmentation methodology to segment different types of defects in radiographic images of welds. The segmentation methodology consists of three main algorithms. namely label removal algorithm. weld extraction algorithm and defect segmentation algorithm. The label removal algorithm was developed to detect and remove labels that are printed on weld radiographs automatically before weld extraction algorithm and defect detection algorithm are applied. The weld extraction algorithm was developed to locate and extract welds automatically from the intensity profiles taken across the image by using graphical analysis. This algorithm was able to extract weld from a radiograph regardless of whether the intensity profile is Gaussian or otherwise. This method is an improvement compared to the previous weld extraction methods which are limited to weld image with Gaussian intensity profiles. Finally. a defect segmentation algorithm was developed to segment the defects automatically from the image using background subtraction and rank leveling method.
format	Thesis
qualification_level	Master's degree
author	Soo , Say Leong
author_facet	Soo , Say Leong
author_sort	Soo , Say Leong
title	Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs
title_short	Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs
title_full	Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs
title_fullStr	Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs
title_full_unstemmed	Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs
title_sort	machine vision application for automatic defect segmentation in weld radiographs
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Mekanikal
publishDate	2006
url	http://eprints.usm.my/31360/1/SOO_SAY_LEONG.pdf
_version_	1747820408294342656

Machine Vision Application For Automatic Defect Segmentation In Weld Radiographs

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