Features construction for starfruit quality inspection

Up to present, the starfruit quality inspection process is performed manually. Manual inspection will cause inconsistency in quality due to human subjective nature, slow processing and labor intensive. Hence, this thesis presents automation process development for the starfruit quality inspection in...

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Main Author:	Mohd. Mokji, Musa
Format:	Thesis
Language:	English
Published:	2009
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/16949/1/MusaMokhjiPFKE2009.pdf
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id	my-utm-ep.16949
record_format	uketd_dc
spelling	my-utm-ep.169492018-06-25T08:59:58Z Features construction for starfruit quality inspection 2009 Mohd. Mokji, Musa TK Electrical engineering. Electronics Nuclear engineering Up to present, the starfruit quality inspection process is performed manually. Manual inspection will cause inconsistency in quality due to human subjective nature, slow processing and labor intensive. Hence, this thesis presents automation process development for the starfruit quality inspection in terms of techniques and algorithms design based on image processing. Basically, there are three main processes of the starfruit quality inspection discussed in this thesis, which are the maturity index classification, skin defect estimation and shape defect estimation. Throughout these processes, new features constructed based on colors and shape are proposed. In maturity index classification, a two-color feature, M, is proposed to differentiate six maturity indices of the starfruit. With the two-color feature, one third of computational data is reduced compared to the typical 3-color features. For skin defect estimation process, a new gray level co-occurrence matrix (GLCM) statistical feature is introduced. This feature has the ability to segment skin defect areas on non-homogenous in illumination and color of the starfruit image. As the GLCM consumes high computation, this thesis proposed a new algorithm based on Haar wavelet that reduces computational burden. Lastly, a shape-based feature is constructed for the shape defect estimation process where a modification of Melkmen convex hull algorithm is designed in order to construct the feature. Experimental results prove that these features are able to convey the three main processes of the starfruit quality inspection process where high accuracies were achieved; 93.33% for the maturity index classification feature, 82% for the skin defect estimation feature and 96% for the shape defect estimation feature. 2009 Thesis http://eprints.utm.my/id/eprint/16949/ http://eprints.utm.my/id/eprint/16949/1/MusaMokhjiPFKE2009.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohd. Mokji, Musa Features construction for starfruit quality inspection
description	Up to present, the starfruit quality inspection process is performed manually. Manual inspection will cause inconsistency in quality due to human subjective nature, slow processing and labor intensive. Hence, this thesis presents automation process development for the starfruit quality inspection in terms of techniques and algorithms design based on image processing. Basically, there are three main processes of the starfruit quality inspection discussed in this thesis, which are the maturity index classification, skin defect estimation and shape defect estimation. Throughout these processes, new features constructed based on colors and shape are proposed. In maturity index classification, a two-color feature, M, is proposed to differentiate six maturity indices of the starfruit. With the two-color feature, one third of computational data is reduced compared to the typical 3-color features. For skin defect estimation process, a new gray level co-occurrence matrix (GLCM) statistical feature is introduced. This feature has the ability to segment skin defect areas on non-homogenous in illumination and color of the starfruit image. As the GLCM consumes high computation, this thesis proposed a new algorithm based on Haar wavelet that reduces computational burden. Lastly, a shape-based feature is constructed for the shape defect estimation process where a modification of Melkmen convex hull algorithm is designed in order to construct the feature. Experimental results prove that these features are able to convey the three main processes of the starfruit quality inspection process where high accuracies were achieved; 93.33% for the maturity index classification feature, 82% for the skin defect estimation feature and 96% for the shape defect estimation feature.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohd. Mokji, Musa
author_facet	Mohd. Mokji, Musa
author_sort	Mohd. Mokji, Musa
title	Features construction for starfruit quality inspection
title_short	Features construction for starfruit quality inspection
title_full	Features construction for starfruit quality inspection
title_fullStr	Features construction for starfruit quality inspection
title_full_unstemmed	Features construction for starfruit quality inspection
title_sort	features construction for starfruit quality inspection
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2009
url	http://eprints.utm.my/id/eprint/16949/1/MusaMokhjiPFKE2009.pdf
_version_	1747815125765586944

Features construction for starfruit quality inspection

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