Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution

Computer graphics is a fast growing field as it contributes significantly to the advancement of modern technology aimed at empowering human and nation wealth creation. Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM) and Computer-Aided Geometric Design (CAGD) are commonly used to reco...

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Main Author:	Pandunata, Priza
Format:	Thesis
Language:	English
Published:	2011
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/id/eprint/32779/5/PrizaPandunataMFSKSM2011.pdf
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id	my-utm-ep.32779
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spelling	my-utm-ep.327792018-05-27T07:51:35Z Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution 2011-11 Pandunata, Priza T Technology (General) Computer graphics is a fast growing field as it contributes significantly to the advancement of modern technology aimed at empowering human and nation wealth creation. Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM) and Computer-Aided Geometric Design (CAGD) are commonly used to reconstruct surfaces in order to obtain a set of limited and disorganized geometric sample values. The process of surface reconstruction consists of two main steps: parameterization and surface fitting. Various solutions have been used in previous studies to reconstruct surfaces such as Non Uniform Rational B-Spline (NURBS) and B-Spline. However, in recent years, Artificial Intelligence (AI) methods such as Advanced Neural Network and Evolutionary Algorithm (EA) have emerged and are extensively used to reconstruct and optimize complex surfaces. This study aims to optimize NURBS surfaces from unstructured 3D data points with feasible control points while preserving the shape of the objects by using Differential Evolution Algorithm (DEA). The Growing Grid Network (GGN) is implemented on a map structure, while DEA is optimally fit on to the NURBS surfaces. In this study, undefined or unstructured data points from several 2D and 3D datasets were used to validate the performance of the proposed method. An error analysis was also conducted to reconfirm the efficacy of the proposed algorithm. This is done by comparing the generated surface with the original surface using other EAs such as: Genetic Algorithm and Particle Swarm Optimization. Experimental results indicate that the proposed Growing Grid Network Differential Evolution (GGNDE) has successfully generated smoother surfaces with lesser number of control points and produced minimum feasible errors while preserving the shape of the objects. 2011-11 Thesis http://eprints.utm.my/id/eprint/32779/ http://eprints.utm.my/id/eprint/32779/5/PrizaPandunataMFSKSM2011.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69751?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Pandunata, Priza Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution
description	Computer graphics is a fast growing field as it contributes significantly to the advancement of modern technology aimed at empowering human and nation wealth creation. Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM) and Computer-Aided Geometric Design (CAGD) are commonly used to reconstruct surfaces in order to obtain a set of limited and disorganized geometric sample values. The process of surface reconstruction consists of two main steps: parameterization and surface fitting. Various solutions have been used in previous studies to reconstruct surfaces such as Non Uniform Rational B-Spline (NURBS) and B-Spline. However, in recent years, Artificial Intelligence (AI) methods such as Advanced Neural Network and Evolutionary Algorithm (EA) have emerged and are extensively used to reconstruct and optimize complex surfaces. This study aims to optimize NURBS surfaces from unstructured 3D data points with feasible control points while preserving the shape of the objects by using Differential Evolution Algorithm (DEA). The Growing Grid Network (GGN) is implemented on a map structure, while DEA is optimally fit on to the NURBS surfaces. In this study, undefined or unstructured data points from several 2D and 3D datasets were used to validate the performance of the proposed method. An error analysis was also conducted to reconfirm the efficacy of the proposed algorithm. This is done by comparing the generated surface with the original surface using other EAs such as: Genetic Algorithm and Particle Swarm Optimization. Experimental results indicate that the proposed Growing Grid Network Differential Evolution (GGNDE) has successfully generated smoother surfaces with lesser number of control points and produced minimum feasible errors while preserving the shape of the objects.
format	Thesis
qualification_level	Master's degree
author	Pandunata, Priza
author_facet	Pandunata, Priza
author_sort	Pandunata, Priza
title	Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution
title_short	Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution
title_full	Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution
title_fullStr	Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution
title_full_unstemmed	Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution
title_sort	optimization of non-uniform relational b-spline surface reconstruction using growing grid-differential evolution
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2011
url	http://eprints.utm.my/id/eprint/32779/5/PrizaPandunataMFSKSM2011.pdf
_version_	1747816058427801600

Optimization of non-uniform relational B-Spline surface reconstruction using growing grid-differential evolution

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