3D Intrinsic scene characteristics extraction framework for a single image

Three-Dimensional (3D) shape reconstruction is an important area of computer vision research because it has numerous potential applications from entertainment production to industrial inspection and clinical analysis. Existing 3D Intrinsic Scene Characteristics (3D-ISCs) extraction methods for a sin...

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Main Author:	Akbar, Habibullah
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	T Technology (General) TR Photography
Online Access:	http://eprints.utem.edu.my/id/eprint/18570/1/3D%20Intrinsic%20Scene%20Characteristics%20Extraction%20Framework%20For%20A%20Single%20Image%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18570/2/3D%20Intrinsic%20scene%20characteristics%20extraction%20framework%20for%20a%20single%20image.pdf
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id	my-utem-ep.18570
record_format	uketd_dc
spelling	my-utem-ep.185702022-05-19T12:45:24Z 3D Intrinsic scene characteristics extraction framework for a single image 2016 Akbar, Habibullah T Technology (General) TR Photography Three-Dimensional (3D) shape reconstruction is an important area of computer vision research because it has numerous potential applications from entertainment production to industrial inspection and clinical analysis. Existing 3D Intrinsic Scene Characteristics (3D-ISCs) extraction methods for a single image have focused solely on estimating diffuse characteristics, i.e. 3D shape, illumination, and reflectance models, of an object. As a result, they have neglected the specular characteristic, the shiny areas of a glossy surface. In reality, many real-world objects emit both specular and diffuse reflections, and thus the specular component may decrease the performance of the 3D-ISCs methods. This study has developed a framework to extract all of these characteristics. The framework combines a Specular Removal (SR) method and a Shape, Illumination, and Reflectance From Shading (SIRFS) method under a Bidirectional Reflectance Distribution Function (BRDF) model. Since the previous SR methods suffered from hue-saturation ambiguity, they are not suitable for this framework. To solve this problem, two SR methods were developed, evaluated, and compared with the standard SR methods. The proposed SR methods are referred as Chaotic Segmentation (CS) and Sparse Coding (SC) methods. To combine the SR and SIRFS methods, two BRDF models were also developed, evaluated, and compared. These models are referred as Modified Dichromatic Reflectance (MDR) and Modified Blinn-Phong (MBP) models. The performances of the proposed SR methods and the BRDF models for extracting 3D-ISCs were evaluated based on public datasets. The results showed that the SC method was more satisfactory compared to the CS and the benchmark method (iterative method). The accuracies of the diffuse and specular characteristics were improved by 7.6% and 53.5% respectively. Moreover, the combination of SC method and MDR model was capable of outperforming the SIRFS method. The computational speed was 19.2% faster. Meanwhile, the average accuracies of depth, surface normal, illumination, shading, and reflectance were improved by 11.4%, 6.5%, 50.5%, 35.2%, and 5.1% respectively. This study indicates that the specular reflection is an important aspect of 3D reconstruction from a single image. The proposed framework has also made considerable improvements in terms of accuracy and computational time of extracting 3D-ISCs. UTeM 2016 Thesis http://eprints.utem.edu.my/id/eprint/18570/ http://eprints.utem.edu.my/id/eprint/18570/1/3D%20Intrinsic%20Scene%20Characteristics%20Extraction%20Framework%20For%20A%20Single%20Image%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/18570/2/3D%20Intrinsic%20scene%20characteristics%20extraction%20framework%20for%20a%20single%20image.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=100420 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Herman, Nanna Suryana
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Herman, Nanna Suryana
topic	T Technology (General) TR Photography
spellingShingle	T Technology (General) TR Photography Akbar, Habibullah 3D Intrinsic scene characteristics extraction framework for a single image
description	Three-Dimensional (3D) shape reconstruction is an important area of computer vision research because it has numerous potential applications from entertainment production to industrial inspection and clinical analysis. Existing 3D Intrinsic Scene Characteristics (3D-ISCs) extraction methods for a single image have focused solely on estimating diffuse characteristics, i.e. 3D shape, illumination, and reflectance models, of an object. As a result, they have neglected the specular characteristic, the shiny areas of a glossy surface. In reality, many real-world objects emit both specular and diffuse reflections, and thus the specular component may decrease the performance of the 3D-ISCs methods. This study has developed a framework to extract all of these characteristics. The framework combines a Specular Removal (SR) method and a Shape, Illumination, and Reflectance From Shading (SIRFS) method under a Bidirectional Reflectance Distribution Function (BRDF) model. Since the previous SR methods suffered from hue-saturation ambiguity, they are not suitable for this framework. To solve this problem, two SR methods were developed, evaluated, and compared with the standard SR methods. The proposed SR methods are referred as Chaotic Segmentation (CS) and Sparse Coding (SC) methods. To combine the SR and SIRFS methods, two BRDF models were also developed, evaluated, and compared. These models are referred as Modified Dichromatic Reflectance (MDR) and Modified Blinn-Phong (MBP) models. The performances of the proposed SR methods and the BRDF models for extracting 3D-ISCs were evaluated based on public datasets. The results showed that the SC method was more satisfactory compared to the CS and the benchmark method (iterative method). The accuracies of the diffuse and specular characteristics were improved by 7.6% and 53.5% respectively. Moreover, the combination of SC method and MDR model was capable of outperforming the SIRFS method. The computational speed was 19.2% faster. Meanwhile, the average accuracies of depth, surface normal, illumination, shading, and reflectance were improved by 11.4%, 6.5%, 50.5%, 35.2%, and 5.1% respectively. This study indicates that the specular reflection is an important aspect of 3D reconstruction from a single image. The proposed framework has also made considerable improvements in terms of accuracy and computational time of extracting 3D-ISCs.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Akbar, Habibullah
author_facet	Akbar, Habibullah
author_sort	Akbar, Habibullah
title	3D Intrinsic scene characteristics extraction framework for a single image
title_short	3D Intrinsic scene characteristics extraction framework for a single image
title_full	3D Intrinsic scene characteristics extraction framework for a single image
title_fullStr	3D Intrinsic scene characteristics extraction framework for a single image
title_full_unstemmed	3D Intrinsic scene characteristics extraction framework for a single image
title_sort	3d intrinsic scene characteristics extraction framework for a single image
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/18570/1/3D%20Intrinsic%20Scene%20Characteristics%20Extraction%20Framework%20For%20A%20Single%20Image%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18570/2/3D%20Intrinsic%20scene%20characteristics%20extraction%20framework%20for%20a%20single%20image.pdf
_version_	1747833937698226176

3D Intrinsic scene characteristics extraction framework for a single image

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