Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu

This project is developed to train the computer programs to recognize objects in the pictures. The purpose of the project is basically to extract and identify each object elements in an image picture. The reviews about the project had been done through the study about image recognition and back-prop...

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Main Author:	Sabtu, Melati
Format:	Thesis
Language:	English
Published:	2005
Subjects:	Neural networks (Computer science) Pattern recognition systems
Online Access:	https://ir.uitm.edu.my/id/eprint/9397/1/TD_MELATI%20SABTU%20CS%2005_5%201.pdf
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id	my-uitm-ir.9397
record_format	uketd_dc
spelling	my-uitm-ir.93972017-01-25T08:05:19Z Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu 2005 Sabtu, Melati Neural networks (Computer science) Pattern recognition systems This project is developed to train the computer programs to recognize objects in the pictures. The purpose of the project is basically to extract and identify each object elements in an image picture. The reviews about the project had been done through the study about image recognition and back-propagation neural network. Several methods that related to this project also been derived through discussion about the image extraction, image preprocessing and some techniques about image segmentation. Gaining information from some resources such as articles and journals contribute various information and knowledge in process of investigation and discussion in order to make this project work smoothly. The project used Back-propagation Neural Network for the algorithm to classified images. Images that capture using digital camera will perform through the algorithm to classified images. The methodology used in the development of this project is basically based on the eight major steps. There are problem assessment, data acquisition, cropping, pre-processing, design, training, testing and documentation. There are three main programs work together. The programs are back-propagation neural network program, training and performance program and recognition program. The momentum rate, learning rate, the number of nodes and layers are the important factors that affect the neural network performance. For overall, the back-propagation algorithm has been proved as a method that can be used for recognition areas. 2005 Thesis https://ir.uitm.edu.my/id/eprint/9397/ https://ir.uitm.edu.my/id/eprint/9397/1/TD_MELATI%20SABTU%20CS%2005_5%201.pdf text en public other degree Universiti Teknologi MARA Faculty of Information Technology and Quantitative Sciences
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Neural networks (Computer science) Pattern recognition systems
spellingShingle	Neural networks (Computer science) Pattern recognition systems Sabtu, Melati Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu
description	This project is developed to train the computer programs to recognize objects in the pictures. The purpose of the project is basically to extract and identify each object elements in an image picture. The reviews about the project had been done through the study about image recognition and back-propagation neural network. Several methods that related to this project also been derived through discussion about the image extraction, image preprocessing and some techniques about image segmentation. Gaining information from some resources such as articles and journals contribute various information and knowledge in process of investigation and discussion in order to make this project work smoothly. The project used Back-propagation Neural Network for the algorithm to classified images. Images that capture using digital camera will perform through the algorithm to classified images. The methodology used in the development of this project is basically based on the eight major steps. There are problem assessment, data acquisition, cropping, pre-processing, design, training, testing and documentation. There are three main programs work together. The programs are back-propagation neural network program, training and performance program and recognition program. The momentum rate, learning rate, the number of nodes and layers are the important factors that affect the neural network performance. For overall, the back-propagation algorithm has been proved as a method that can be used for recognition areas.
format	Thesis
qualification_name	other
qualification_level	Bachelor degree
author	Sabtu, Melati
author_facet	Sabtu, Melati
author_sort	Sabtu, Melati
title	Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu
title_short	Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu
title_full	Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu
title_fullStr	Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu
title_full_unstemmed	Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu
title_sort	recognition of isolated elements picture using backpropagation neural network / melati sabtu
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Information Technology and Quantitative Sciences
publishDate	2005
url	https://ir.uitm.edu.my/id/eprint/9397/1/TD_MELATI%20SABTU%20CS%2005_5%201.pdf
_version_	1783733179460354048

Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu

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