Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image

Automated surveillance systems often identify shadows as parts of a moving object which jeopardized subsequent image processing tasks such as object identification and tracking. In this thesis, an improved shadow elimination method for an indoor surveillance system is presented. This developed metho...

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Main Author:	Teo, Kah Ming
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/98289/1/TeoKahMingMSKE2018.pdf
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id	my-utm-ep.98289
record_format	uketd_dc
spelling	my-utm-ep.982892022-12-04T10:11:42Z Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image 2018 Teo, Kah Ming TK Electrical engineering. Electronics Nuclear engineering Automated surveillance systems often identify shadows as parts of a moving object which jeopardized subsequent image processing tasks such as object identification and tracking. In this thesis, an improved shadow elimination method for an indoor surveillance system is presented. This developed method is a fusion of several image processing methods. Firstly, the image is segmented using the Statistical Region Merging algorithm to obtain the segmented potential shadow regions. Next, multiple shadow identification features which include Normalized Cross-Correlation, Local Color Constancy and Hue-Saturation-Value shadow cues are applied on the images to generate feature maps. These feature maps are used for identifying and removing cast shadows according to the segmented regions. The video dataset used is the Autonomous Agents for On-Scene Networked Incident Management which covers both indoor and outdoor video scenes. The benchmarking result indicates that the developed method is on-par with several normally used shadow detection methods. The developed method yields a mean score of 85.17% for the video sequence in which the strongest shadow is present and a mean score of 89.93% for the video having the most complex textured background. This research contributes to the development and improvement of a functioning shadow eliminator method that is able to cope with image noise and various illumination changes. 2018 Thesis http://eprints.utm.my/id/eprint/98289/ http://eprints.utm.my/id/eprint/98289/1/TeoKahMingMSKE2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:144762 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School 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 Teo, Kah Ming Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image
description	Automated surveillance systems often identify shadows as parts of a moving object which jeopardized subsequent image processing tasks such as object identification and tracking. In this thesis, an improved shadow elimination method for an indoor surveillance system is presented. This developed method is a fusion of several image processing methods. Firstly, the image is segmented using the Statistical Region Merging algorithm to obtain the segmented potential shadow regions. Next, multiple shadow identification features which include Normalized Cross-Correlation, Local Color Constancy and Hue-Saturation-Value shadow cues are applied on the images to generate feature maps. These feature maps are used for identifying and removing cast shadows according to the segmented regions. The video dataset used is the Autonomous Agents for On-Scene Networked Incident Management which covers both indoor and outdoor video scenes. The benchmarking result indicates that the developed method is on-par with several normally used shadow detection methods. The developed method yields a mean score of 85.17% for the video sequence in which the strongest shadow is present and a mean score of 89.93% for the video having the most complex textured background. This research contributes to the development and improvement of a functioning shadow eliminator method that is able to cope with image noise and various illumination changes.
format	Thesis
qualification_level	Master's degree
author	Teo, Kah Ming
author_facet	Teo, Kah Ming
author_sort	Teo, Kah Ming
title	Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image
title_short	Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image
title_full	Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image
title_fullStr	Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image
title_full_unstemmed	Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image
title_sort	shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2018
url	http://eprints.utm.my/id/eprint/98289/1/TeoKahMingMSKE2018.pdf
_version_	1776100575538053120

Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image

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