Classification of binary insect images using fuzzy and gaussian artmap neural networks

Object recognition and classification is an essential routine in our daily lives. Our eyes act as a camera capturing the image of particular object and sending it to the brain to be recognized. Thus, the eye vision system inspires researchers to create machine vision systems. As a significant part...

Full description

Saved in:

Bibliographic Details
Main Author:	Shahrul Nizam, Yaakob
Format:	Thesis
Language:	English
Subjects:	Neural networks (Computer science) Cognition in animals Object recognition Moment invariant
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63439/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63439/2/Full%20text.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-unimap-63439
record_format	uketd_dc
spelling	my-unimap-634392019-11-29T03:39:23Z Classification of binary insect images using fuzzy and gaussian artmap neural networks Shahrul Nizam, Yaakob Assoc. Prof. Dr. Puteh Saad Object recognition and classification is an essential routine in our daily lives. Our eyes act as a camera capturing the image of particular object and sending it to the brain to be recognized. Thus, the eye vision system inspires researchers to create machine vision systems. As a significant part of the machine vision system, this research focused on two (2) important phases of the system; feature extraction and classification. As for the feature extraction six (6) different types of moment invariant techniques namely Geometric moment invariant (GMI), United moment invariant (UMI), Zernike moment invariant (ZMI), Legendre moment invariant (LMI), Tchebichefmoment invariant (I'MI) and Krawtchouk moment invariant (KMI) are used to extract the global shape features of the binary insect images. These features are then channeled to the Fuzzy ARTMAP (FAM) and Gaussian ARTMAP( GAM )neural network to be classified and recognized. In the GAM neural network, a gamma threshold is proposed to find the optimal value for gamma parameter acting as the initial value for a Gaussian distribution in the training phase. It is found that KMI is the best technique for features extraction of the global shape information of the insect images as compared to GMI, UM!, ZM!, LMI and TMI. The finding is based on the lowest value of Total Min Absolute Error (I'PMAE) (0.03%-1.01). The training and testing method for both neural networks is based on 4- folds cross validation technique. It is also found that the performance of F AM neural network is influenced by the types of normalization technique utilized. The Improved Linear Scaling (ILS) normalization technique generated the highest classification rate by the F AM neural network when compared to Unit Range (UR) and Improved Unit Range (IUR). It is further found that GAM neural network is a better insect classification technique when compared to F AM neural network producing the classification accuracy up to 99.58% whereby the classification accuracy of FAM neural network is 82%. Universiti Malaysia Perlis (UniMAP) 2006 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/63439 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63439/1/Page%201-24.pdf 121a0bfc1daad15756275ccf176f4b27 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63439/2/Full%20text.pdf 0241bafffb6da638c365260a0f5ea69d http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63439/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Neural networks (Computer science) Cognition in animals Object recognition Moment invariant School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Assoc. Prof. Dr. Puteh Saad
topic	Neural networks (Computer science) Cognition in animals Object recognition Moment invariant
spellingShingle	Neural networks (Computer science) Cognition in animals Object recognition Moment invariant Shahrul Nizam, Yaakob Classification of binary insect images using fuzzy and gaussian artmap neural networks
description	Object recognition and classification is an essential routine in our daily lives. Our eyes act as a camera capturing the image of particular object and sending it to the brain to be recognized. Thus, the eye vision system inspires researchers to create machine vision systems. As a significant part of the machine vision system, this research focused on two (2) important phases of the system; feature extraction and classification. As for the feature extraction six (6) different types of moment invariant techniques namely Geometric moment invariant (GMI), United moment invariant (UMI), Zernike moment invariant (ZMI), Legendre moment invariant (LMI), Tchebichefmoment invariant (I'MI) and Krawtchouk moment invariant (KMI) are used to extract the global shape features of the binary insect images. These features are then channeled to the Fuzzy ARTMAP (FAM) and Gaussian ARTMAP( GAM )neural network to be classified and recognized. In the GAM neural network, a gamma threshold is proposed to find the optimal value for gamma parameter acting as the initial value for a Gaussian distribution in the training phase. It is found that KMI is the best technique for features extraction of the global shape information of the insect images as compared to GMI, UM!, ZM!, LMI and TMI. The finding is based on the lowest value of Total Min Absolute Error (I'PMAE) (0.03%-1.01). The training and testing method for both neural networks is based on 4- folds cross validation technique. It is also found that the performance of F AM neural network is influenced by the types of normalization technique utilized. The Improved Linear Scaling (ILS) normalization technique generated the highest classification rate by the F AM neural network when compared to Unit Range (UR) and Improved Unit Range (IUR). It is further found that GAM neural network is a better insect classification technique when compared to F AM neural network producing the classification accuracy up to 99.58% whereby the classification accuracy of FAM neural network is 82%.
format	Thesis
author	Shahrul Nizam, Yaakob
author_facet	Shahrul Nizam, Yaakob
author_sort	Shahrul Nizam, Yaakob
title	Classification of binary insect images using fuzzy and gaussian artmap neural networks
title_short	Classification of binary insect images using fuzzy and gaussian artmap neural networks
title_full	Classification of binary insect images using fuzzy and gaussian artmap neural networks
title_fullStr	Classification of binary insect images using fuzzy and gaussian artmap neural networks
title_full_unstemmed	Classification of binary insect images using fuzzy and gaussian artmap neural networks
title_sort	classification of binary insect images using fuzzy and gaussian artmap neural networks
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63439/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63439/2/Full%20text.pdf
_version_	1747836857943588864

Classification of binary insect images using fuzzy and gaussian artmap neural networks

Similar Items