Object segmentation in still images using topic modelling method

One of the key components towards achieving high performance automated visual-based object recognition is the quasi-error free object segmentation process. Being an important integral part of many machine vision as well as computer vision systems, a tremendous amount of effort in object segmentation...

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Main Author:	Azmi, Nur ‘Amirah
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/79453/1/Nur%E2%80%98AmirahAzmiMFKE2018.pdf
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id	my-utm-ep.79453
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spelling	my-utm-ep.794532018-10-31T12:39:23Z Object segmentation in still images using topic modelling method 2018 Azmi, Nur ‘Amirah TK Electrical engineering. Electronics Nuclear engineering One of the key components towards achieving high performance automated visual-based object recognition is the quasi-error free object segmentation process. Being an important integral part of many machine vision as well as computer vision systems, a tremendous amount of effort in object segmentation has been proposed in the literature. One of these approaches is the work that implements Probabilistic Graph Modelling (PGM) techniques. PGM is a rich framework for calculating probability and statistics in large given data sets and fields. One of the comprehensive methods in PGM is the Topic Modelling (TM) method introduced in the early 2000. TM has shown to be successful in classifying humongous information related to text and documents and has been implemented in many online search engines. Since image contains huge amount of information (in terms of pixels), segmentation of this information into meaningful region of interest (in this case objects) does fit into the framework of TM. The objectives of this project are to implement and analyze the capability and efficiency of TM in recognizing objects found in stationary images. TM is a process where it uses approximation technique to discover important segment or structure based on object classification. However, to proceed with object classification, object segmentation is firstly executed, making object segmentation as the most important part in the system. Through TM, the classification can be done by grouping the pixels (superpixels) accordingly in order to clearly represent the object of interest. In achieving this goals, Open Computer Vision (OpenCV) library will be fully utilized. It is expected that the proposed method will be able to perform object segmentation with high confident similar to state-of-the-art methods. 2018 Thesis http://eprints.utm.my/id/eprint/79453/ http://eprints.utm.my/id/eprint/79453/1/Nur%E2%80%98AmirahAzmiMFKE2018.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Azmi, Nur ‘Amirah Object segmentation in still images using topic modelling method
description	One of the key components towards achieving high performance automated visual-based object recognition is the quasi-error free object segmentation process. Being an important integral part of many machine vision as well as computer vision systems, a tremendous amount of effort in object segmentation has been proposed in the literature. One of these approaches is the work that implements Probabilistic Graph Modelling (PGM) techniques. PGM is a rich framework for calculating probability and statistics in large given data sets and fields. One of the comprehensive methods in PGM is the Topic Modelling (TM) method introduced in the early 2000. TM has shown to be successful in classifying humongous information related to text and documents and has been implemented in many online search engines. Since image contains huge amount of information (in terms of pixels), segmentation of this information into meaningful region of interest (in this case objects) does fit into the framework of TM. The objectives of this project are to implement and analyze the capability and efficiency of TM in recognizing objects found in stationary images. TM is a process where it uses approximation technique to discover important segment or structure based on object classification. However, to proceed with object classification, object segmentation is firstly executed, making object segmentation as the most important part in the system. Through TM, the classification can be done by grouping the pixels (superpixels) accordingly in order to clearly represent the object of interest. In achieving this goals, Open Computer Vision (OpenCV) library will be fully utilized. It is expected that the proposed method will be able to perform object segmentation with high confident similar to state-of-the-art methods.
format	Thesis
qualification_level	Master's degree
author	Azmi, Nur ‘Amirah
author_facet	Azmi, Nur ‘Amirah
author_sort	Azmi, Nur ‘Amirah
title	Object segmentation in still images using topic modelling method
title_short	Object segmentation in still images using topic modelling method
title_full	Object segmentation in still images using topic modelling method
title_fullStr	Object segmentation in still images using topic modelling method
title_full_unstemmed	Object segmentation in still images using topic modelling method
title_sort	object segmentation in still images using topic modelling method
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2018
url	http://eprints.utm.my/id/eprint/79453/1/Nur%E2%80%98AmirahAzmiMFKE2018.pdf
_version_	1747818229934325760

Object segmentation in still images using topic modelling method

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