A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM

Object Recognition has been a field of interest to many researchers. In fact, it has been referred to as the most important problem in machine or computer vision. Researchers have developed many algorithms to solve the problem of object recognition that are machine vision motivated. On the other han...

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Main Authors: Hamada , R. H. Al-Absi, Azween, Abdullah
Format: Thesis
Published: 2010
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Online Access:http://eprints.utp.edu.my/2738/1/HamadaCOPY1Latest-19-08.pdf
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spelling my-utp-ep.27382017-01-19T08:24:42Z A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM 2010 Hamada , R. H. Al-Absi Azween, Abdullah QA75 Electronic computers. Computer science Object Recognition has been a field of interest to many researchers. In fact, it has been referred to as the most important problem in machine or computer vision. Researchers have developed many algorithms to solve the problem of object recognition that are machine vision motivated. On the other hand, biology has motivated researchers to study the visual system of humans and animals such as monkeys and map it into a computational model. Some of these models are based on the feed-forward mechanism of information communication in cortex where the information is communicated between the different visual areas from the lower areas to the top areas in a feed-forward manner; however, the performance of these models has been affected much by the increase of clutter in the scene as well as occlusion. Another mechanism of information processing in the cortex is called the feedback mechanism, where the information from the top areas in the visual system is communicated to the lower areas in a feedback manner; this mechanism has also been mapped into computational models. All these models which are based on the feed-forward or feedback mechanisms have shown promising results. However, during the testing of these models, there have been some issues that affect their performance such as occlusion that prevents objects from being visible. In addition, scenes that contain high amounts of clutter in them, where there are so many objects, have also affected the performance of these models. In fact, the performance has been reported to drop to 74% when systems that are based on these models are subjected to one or both of the issues mentioned above. The human visual system, naturally, utilizes both feed-forward and feedback mechanisms in the operation of perceiving the surrounding environment. Both feed-forward and feedback mechanisms are integrated in a way that makes the visual system of the human outperforms any state-of-the-art system. In this research, a proposed model of object recognition based on the integration concept of the feed-forward and feedback mechanisms in the human visual system is presented. 2010 Thesis http://eprints.utp.edu.my/2738/ http://eprints.utp.edu.my/2738/1/HamadaCOPY1Latest-19-08.pdf application/pdf masters UNIVERSITI TEKNOLOGI PETRONAS Department of computer and Information Sciences
institution Universiti Teknologi PETRONAS
collection UTP Institutional Repository
topic QA75 Electronic computers
Computer science
spellingShingle QA75 Electronic computers
Computer science
Hamada , R. H. Al-Absi
Azween, Abdullah
A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM
description Object Recognition has been a field of interest to many researchers. In fact, it has been referred to as the most important problem in machine or computer vision. Researchers have developed many algorithms to solve the problem of object recognition that are machine vision motivated. On the other hand, biology has motivated researchers to study the visual system of humans and animals such as monkeys and map it into a computational model. Some of these models are based on the feed-forward mechanism of information communication in cortex where the information is communicated between the different visual areas from the lower areas to the top areas in a feed-forward manner; however, the performance of these models has been affected much by the increase of clutter in the scene as well as occlusion. Another mechanism of information processing in the cortex is called the feedback mechanism, where the information from the top areas in the visual system is communicated to the lower areas in a feedback manner; this mechanism has also been mapped into computational models. All these models which are based on the feed-forward or feedback mechanisms have shown promising results. However, during the testing of these models, there have been some issues that affect their performance such as occlusion that prevents objects from being visible. In addition, scenes that contain high amounts of clutter in them, where there are so many objects, have also affected the performance of these models. In fact, the performance has been reported to drop to 74% when systems that are based on these models are subjected to one or both of the issues mentioned above. The human visual system, naturally, utilizes both feed-forward and feedback mechanisms in the operation of perceiving the surrounding environment. Both feed-forward and feedback mechanisms are integrated in a way that makes the visual system of the human outperforms any state-of-the-art system. In this research, a proposed model of object recognition based on the integration concept of the feed-forward and feedback mechanisms in the human visual system is presented.
format Thesis
qualification_level Master's degree
author Hamada , R. H. Al-Absi
Azween, Abdullah
author_facet Hamada , R. H. Al-Absi
Azween, Abdullah
author_sort Hamada , R. H. Al-Absi
title A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM
title_short A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM
title_full A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM
title_fullStr A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM
title_full_unstemmed A BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM
title_sort biologically inspired object recognition system
granting_institution UNIVERSITI TEKNOLOGI PETRONAS
granting_department Department of computer and Information Sciences
publishDate 2010
url http://eprints.utp.edu.my/2738/1/HamadaCOPY1Latest-19-08.pdf
_version_ 1747837929983574016