Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm

Stereo Vision Disparity Map (SVDM) estimation is one of the active research topics in computer vision. To improve the accuracy of SVDM is difficult and challenging. The accuracy is affected by the regions of edge discontinuities, occluded, repetitive pattern and low texture. Therefore, this thesi...

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Main Author:	Hamzah, Rostam Affendi
Format:	Thesis
Language:	English
Published:	2017
Subjects:	T Technology
Online Access:	http://eprints.usm.my/45789/1/Improvement%20Of%20Local-Based%20Stereo%20Vision%20Disparity%20Map%20Estimation%20Algorithm.pdf
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spelling	my-usm-ep.457892021-11-17T03:42:15Z Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm 2017-06 Hamzah, Rostam Affendi T Technology TA401-492 Materials of engineering and construction. Mechanics of materials Stereo Vision Disparity Map (SVDM) estimation is one of the active research topics in computer vision. To improve the accuracy of SVDM is difficult and challenging. The accuracy is affected by the regions of edge discontinuities, occluded, repetitive pattern and low texture. Therefore, this thesis proposes an algorithm to handle more efficiently these challenges. Firstly, the proposed SVDM algorithm combines three matching cost features based on per pixel differences. The combination of Absolute Differences (AD) and Gradi- ent Matching (GM) features reduces the radiometric distortions. Then, both differences are combined with Census Transform (CN) feature to reduce the effect of illumination vari- ations. Secondly, this thesis also presents a new method of edge discontinuities handling which is known as iterative Guided Filter (iGF). This method is introduced to preserve and improve the object boundaries. Finally, the fill-in invalid disparity, undirected graph segmentation and plane fitting processes are utilized at the last stage in order to recover the occluded, repetitive and low texture regions on the SVDM. Based on the experimental results of standard benchmarking dataset from the Middlebury, the proposed algorithm is able to reduce 17.17% and 18.11% of all and nonocc errors, respectively, as compared to the algorithm without the proposed framework. Moreover, the proposed framework outperformed some of the state-of-the-arts algorithms in the literature. 2017-06 Thesis http://eprints.usm.my/45789/ http://eprints.usm.my/45789/1/Improvement%20Of%20Local-Based%20Stereo%20Vision%20Disparity%20Map%20Estimation%20Algorithm.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Bahan & Sumber Mineral
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology T Technology
spellingShingle	T Technology T Technology Hamzah, Rostam Affendi Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm
description	Stereo Vision Disparity Map (SVDM) estimation is one of the active research topics in computer vision. To improve the accuracy of SVDM is difficult and challenging. The accuracy is affected by the regions of edge discontinuities, occluded, repetitive pattern and low texture. Therefore, this thesis proposes an algorithm to handle more efficiently these challenges. Firstly, the proposed SVDM algorithm combines three matching cost features based on per pixel differences. The combination of Absolute Differences (AD) and Gradi- ent Matching (GM) features reduces the radiometric distortions. Then, both differences are combined with Census Transform (CN) feature to reduce the effect of illumination vari- ations. Secondly, this thesis also presents a new method of edge discontinuities handling which is known as iterative Guided Filter (iGF). This method is introduced to preserve and improve the object boundaries. Finally, the fill-in invalid disparity, undirected graph segmentation and plane fitting processes are utilized at the last stage in order to recover the occluded, repetitive and low texture regions on the SVDM. Based on the experimental results of standard benchmarking dataset from the Middlebury, the proposed algorithm is able to reduce 17.17% and 18.11% of all and nonocc errors, respectively, as compared to the algorithm without the proposed framework. Moreover, the proposed framework outperformed some of the state-of-the-arts algorithms in the literature.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Hamzah, Rostam Affendi
author_facet	Hamzah, Rostam Affendi
author_sort	Hamzah, Rostam Affendi
title	Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm
title_short	Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm
title_full	Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm
title_fullStr	Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm
title_full_unstemmed	Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm
title_sort	improvement of local-based stereo vision disparity map estimation algorithm
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Bahan & Sumber Mineral
publishDate	2017
url	http://eprints.usm.my/45789/1/Improvement%20Of%20Local-Based%20Stereo%20Vision%20Disparity%20Map%20Estimation%20Algorithm.pdf
_version_	1747821566612209664

Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm

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