New descriptor for object detection using an improved ensemble-based technique

Object detection is an essential process for further tasks including, but not limited to, object and event detection, object tracking, object recognition, video indexing, motion estimation, image restoration, image registration, image retrieval, and reconstruction of 3D scene. In the recent past, in...

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Main Author:	Ahsan, Amin Mohamed
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/54786/1/AminMohamedAhsanPFC2015.pdf
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id	my-utm-ep.54786
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spelling	my-utm-ep.547862020-11-07T01:10:54Z New descriptor for object detection using an improved ensemble-based technique 2015-05 Ahsan, Amin Mohamed QA75 Electronic computers. Computer science Object detection is an essential process for further tasks including, but not limited to, object and event detection, object tracking, object recognition, video indexing, motion estimation, image restoration, image registration, image retrieval, and reconstruction of 3D scene. In the recent past, interest point detectors and their descriptors, as local features, have received a great interest in computer vision areas and technologies. These types of features have shown their robustness against different types of deformation due to geometric transformation, photometric transformation and other disturbances. Therefore, they are more accurate and stable than the global ones. Among all interest point detectors and descriptors, the Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) are considered as the most common methods that receive interest from researchers in terms of usage and development; but, getting more accurate, invariant and fast descriptor is still needed. Matching technique is often used to recognize the object based on such features; however, it is not proper for some applications such as searching for an isolated object and it is difficult to be used in object category recognition or to recognize the part-based object. Therefore, learning-based technique, that has been proven to be an effective method in object detection, can be used to overcome the previously mentioned challenges. However, the object required to be detected usually represents a small ratio compared to non-object that causes an imbalanced data problem. The aim of this study is to design and develop an effective model for object detection that is faster, more accurate and it can manage aforementioned challenges. To achieve this goal first, new fast and an accurate descriptor is introduced based on interest points; second, an effective classification method, that mitigates the effect of imbalanced data, is designed based on developed ensemble classifiers; third, an updating scheme of interest point detector is presented to speed up the object detection system. Results show that the proposed features are faster and more invariant than the most common interest-point-based features. The developed technique based on ensemble classifiers produces notable results in terms of accuracy and False Positive rate compared to the traditional one. The speed of object detection system has increased by 30% in average based on the proposed scheme. 2015-05 Thesis http://eprints.utm.my/id/eprint/54786/ http://eprints.utm.my/id/eprint/54786/1/AminMohamedAhsanPFC2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:95962 phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ahsan, Amin Mohamed New descriptor for object detection using an improved ensemble-based technique
description	Object detection is an essential process for further tasks including, but not limited to, object and event detection, object tracking, object recognition, video indexing, motion estimation, image restoration, image registration, image retrieval, and reconstruction of 3D scene. In the recent past, interest point detectors and their descriptors, as local features, have received a great interest in computer vision areas and technologies. These types of features have shown their robustness against different types of deformation due to geometric transformation, photometric transformation and other disturbances. Therefore, they are more accurate and stable than the global ones. Among all interest point detectors and descriptors, the Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) are considered as the most common methods that receive interest from researchers in terms of usage and development; but, getting more accurate, invariant and fast descriptor is still needed. Matching technique is often used to recognize the object based on such features; however, it is not proper for some applications such as searching for an isolated object and it is difficult to be used in object category recognition or to recognize the part-based object. Therefore, learning-based technique, that has been proven to be an effective method in object detection, can be used to overcome the previously mentioned challenges. However, the object required to be detected usually represents a small ratio compared to non-object that causes an imbalanced data problem. The aim of this study is to design and develop an effective model for object detection that is faster, more accurate and it can manage aforementioned challenges. To achieve this goal first, new fast and an accurate descriptor is introduced based on interest points; second, an effective classification method, that mitigates the effect of imbalanced data, is designed based on developed ensemble classifiers; third, an updating scheme of interest point detector is presented to speed up the object detection system. Results show that the proposed features are faster and more invariant than the most common interest-point-based features. The developed technique based on ensemble classifiers produces notable results in terms of accuracy and False Positive rate compared to the traditional one. The speed of object detection system has increased by 30% in average based on the proposed scheme.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ahsan, Amin Mohamed
author_facet	Ahsan, Amin Mohamed
author_sort	Ahsan, Amin Mohamed
title	New descriptor for object detection using an improved ensemble-based technique
title_short	New descriptor for object detection using an improved ensemble-based technique
title_full	New descriptor for object detection using an improved ensemble-based technique
title_fullStr	New descriptor for object detection using an improved ensemble-based technique
title_full_unstemmed	New descriptor for object detection using an improved ensemble-based technique
title_sort	new descriptor for object detection using an improved ensemble-based technique
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2015
url	http://eprints.utm.my/id/eprint/54786/1/AminMohamedAhsanPFC2015.pdf
_version_	1747817726477336576

New descriptor for object detection using an improved ensemble-based technique

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