Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar

A corner detector is one of the components for feature extraction in a sketch interpreter engine. Many conventional corner detectors nowadays are based on mathematical models and equations. This research developed a corner detector without using complicated mathematical models and equation. A chain...

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Main Author:	Subri, Syarul Haniz
Format:	Thesis
Language:	English
Published:	2006
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/2518/1/SyarulHanizSubriMFC2006.pdf
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id	my-utm-ep.2518
record_format	uketd_dc
spelling	my-utm-ep.25182018-06-25T00:40:55Z Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar 2006-05 Subri, Syarul Haniz QA75 Electronic computers. Computer science A corner detector is one of the components for feature extraction in a sketch interpreter engine. Many conventional corner detectors nowadays are based on mathematical models and equations. This research developed a corner detector without using complicated mathematical models and equation. A chain code was used as image or data representation for corner detector data source. Two chain codes applied in this research included Freeman chain code (FCC) and Vertex chain code (VCC). The performance and suitability of these chain codes usage were compared This research focused on the production of an intelligent engine for a sketch interpreter, hence a neural network was chosen to be applied in this corner detector. The neural network package in Matlab software was used by this artificial intelligent method to develop a neural network classifier. Back propagation neural network algorithm was used to develop and produce the classifier for corner detector algorithm. Two dimensional sketch line drawing was involved as an important input in developing and producing the classifier. This research produced the framework for developing chain code corner detector classifier, the neural network classifier, and the development process of VCC from rectangular cell. The algorithm of neural network classifier corner detector for FCC and VCC were also produced. Based on the analysis, the algorithm of neural network classifier corner detector for FCC was found to have more potentials in terms of accuracy of corner detection and suitability of the chain code. This research involved three main components - corner detector, chain code, and neural network. The integration of these components produced a corner detector algorithm and this algorithm can be used in the engine of an intelligent sketch interpreter 2006-05 Thesis http://eprints.utm.my/id/eprint/2518/ http://eprints.utm.my/id/eprint/2518/1/SyarulHanizSubriMFC2006.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System 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 Subri, Syarul Haniz Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar
description	A corner detector is one of the components for feature extraction in a sketch interpreter engine. Many conventional corner detectors nowadays are based on mathematical models and equations. This research developed a corner detector without using complicated mathematical models and equation. A chain code was used as image or data representation for corner detector data source. Two chain codes applied in this research included Freeman chain code (FCC) and Vertex chain code (VCC). The performance and suitability of these chain codes usage were compared This research focused on the production of an intelligent engine for a sketch interpreter, hence a neural network was chosen to be applied in this corner detector. The neural network package in Matlab software was used by this artificial intelligent method to develop a neural network classifier. Back propagation neural network algorithm was used to develop and produce the classifier for corner detector algorithm. Two dimensional sketch line drawing was involved as an important input in developing and producing the classifier. This research produced the framework for developing chain code corner detector classifier, the neural network classifier, and the development process of VCC from rectangular cell. The algorithm of neural network classifier corner detector for FCC and VCC were also produced. Based on the analysis, the algorithm of neural network classifier corner detector for FCC was found to have more potentials in terms of accuracy of corner detection and suitability of the chain code. This research involved three main components - corner detector, chain code, and neural network. The integration of these components produced a corner detector algorithm and this algorithm can be used in the engine of an intelligent sketch interpreter
format	Thesis
qualification_level	Master's degree
author	Subri, Syarul Haniz
author_facet	Subri, Syarul Haniz
author_sort	Subri, Syarul Haniz
title	Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar
title_short	Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar
title_full	Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar
title_fullStr	Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar
title_full_unstemmed	Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar
title_sort	aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Computer Science and Information System
publishDate	2006
url	http://eprints.utm.my/id/eprint/2518/1/SyarulHanizSubriMFC2006.pdf
_version_	1747814424616370176

Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar

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