Principle component analysis (PCA) based coin-counting system

1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010 organized by Universiti Malaysia Perlis (UniMAP) and co-organized by Universiti Sains Malaysia (USM) & Universiti Kebangsaan Malaysia (UKM), 2nd - 3rd June 2010 at Eastern & Oriental Hotel, Penang.

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Bibliographic Details
Main Authors:	Mohd. Syafarudy, Abu, Lim, Eng Aik
Other Authors:	syafarudy@unimap.edu.my
Format:	Working Paper
Language:	English
Published:	Universiti Malaysia Perlis (UniMAP) 2010
Subjects:	Rapid descriptor (RD) Neural network Recognition performance Network sizes and training time for networks Regional Conference on Applied and Engineering Mathematics (RCAEM)
Online Access:	http://dspace.unimap.edu.my/xmlui/handle/123456789/10211
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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-10211
record_format	dspace
spelling	my.unimap-102112010-11-15T04:55:36Z Principle component analysis (PCA) based coin-counting system Mohd. Syafarudy, Abu Lim, Eng Aik syafarudy@unimap.edu.my Rapid descriptor (RD) Neural network Recognition performance Network sizes and training time for networks Regional Conference on Applied and Engineering Mathematics (RCAEM) 1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010 organized by Universiti Malaysia Perlis (UniMAP) and co-organized by Universiti Sains Malaysia (USM) & Universiti Kebangsaan Malaysia (UKM), 2nd - 3rd June 2010 at Eastern & Oriental Hotel, Penang. In this paper, a neural network using a feature extraction scheme known as principle component analysis (PCA) is proposed to recognize two-dimensional objects in an image. This approach consists of two stages. First, the procedures of determining the coefficients of rapid descriptor (RD) of 2-D objects from their boundary are described. To speed up the learning process of the neural network, a PCA technique is used to extract the principal components of these RD coefficients. Then, these reduced components are utilized to train a feed-forward neural network for object recognition and classification. We compare recognition performance, network sizes, and training time for networks trained with both reduced and unreduced data. The experimental results show that a significant reduction in training time can be achieved without a sacrifice in classifier accuracy. 2010-11-15T04:55:36Z 2010-11-15T04:55:36Z 2010-06-02 Working Paper Vol.1(16), p.105-108 http://hdl.handle.net/123456789/10211 en Proceedings of the 1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010 Universiti Malaysia Perlis (UniMAP) Institut Matematik Kejuruteraan
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Rapid descriptor (RD) Neural network Recognition performance Network sizes and training time for networks Regional Conference on Applied and Engineering Mathematics (RCAEM)
spellingShingle	Rapid descriptor (RD) Neural network Recognition performance Network sizes and training time for networks Regional Conference on Applied and Engineering Mathematics (RCAEM) Mohd. Syafarudy, Abu Lim, Eng Aik Principle component analysis (PCA) based coin-counting system
description	1st Regional Conference on Applied and Engineering Mathematics (RCAEM-I) 2010 organized by Universiti Malaysia Perlis (UniMAP) and co-organized by Universiti Sains Malaysia (USM) & Universiti Kebangsaan Malaysia (UKM), 2nd - 3rd June 2010 at Eastern & Oriental Hotel, Penang.
author2	syafarudy@unimap.edu.my
author_facet	syafarudy@unimap.edu.my Mohd. Syafarudy, Abu Lim, Eng Aik
format	Working Paper
author	Mohd. Syafarudy, Abu Lim, Eng Aik
author_sort	Mohd. Syafarudy, Abu
title	Principle component analysis (PCA) based coin-counting system
title_short	Principle component analysis (PCA) based coin-counting system
title_full	Principle component analysis (PCA) based coin-counting system
title_fullStr	Principle component analysis (PCA) based coin-counting system
title_full_unstemmed	Principle component analysis (PCA) based coin-counting system
title_sort	principle component analysis (pca) based coin-counting system
publisher	Universiti Malaysia Perlis (UniMAP)
publishDate	2010
url	http://dspace.unimap.edu.my/xmlui/handle/123456789/10211
_version_	1643789772935659520

Principle component analysis (PCA) based coin-counting system

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