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Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction

In computer vision field, one of most active topics in research is estimation of depth map which is Stereo Matching (SM) process and it’s also known as Stereo Vision Disparity Map (SVDM). The real challenge in SM is to get high accuracy of disparity map. Matching cost computation produces high noise...

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Main Author: Abd Razak, Siti Safwana
Format: Thesis
Language:English
English
Published: 2019
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utem.edu.my/id/eprint/24618/1/Development%20Of%20Stereo-Matching%20Algorithm%20Based%20On%20Adaptive%20Weighted%20Prediction.pdf
http://eprints.utem.edu.my/id/eprint/24618/2/Development%20Of%20Stereo-Matching%20Algorithm%20Based%20On%20Adaptive%20Weighted%20Prediction.pdf
http://eprints.utem.edu.my/id/eprint/24618/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117088
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spelling my.utem.eprints.246182021-10-05T11:21:07Z http://eprints.utem.edu.my/id/eprint/24618/ Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction Abd Razak, Siti Safwana T Technology (General) TA Engineering (General). Civil engineering (General) In computer vision field, one of most active topics in research is estimation of depth map which is Stereo Matching (SM) process and it’s also known as Stereo Vision Disparity Map (SVDM). The real challenge in SM is to get high accuracy of disparity map. Matching cost computation produces high noise of disparity map and input images contains low texture area and repetitive pattern also leads to high error on development of disparity map. Other than that, wrong information of each pixel of disparity map which can affect the accuracy of SVDM. Therefore, to overcome the causes of effected accuracy, new Stereo Matching Algorithm (SMA) based on Adaptive Weighted Bilateral Filter (AWBF) was introduced together with characterize the SMA based on quantitative and qualitative measurements and produced SMA performance were evaluate using standard taxonomy of SM. This thesis proposes an algorithm to handle the limitations. Firstly, pre-processing used Sobel Filter was added at initial step to compensate photometric distortion of input images. Then for matching cost, the proposed SMA combine one of matching cost method with some threshold adjustment to reduces the radiometric distortions. The Sum of Absolute Different (SAD) is the matching cost’s method and some threshold adjustment were used in this thesis where the SAD’s window size of 11 and threshold value of 0.8 was selected based on the experimental results. Secondly, to overcome low texture area and repetitive pattern limitation, this thesis present AWBF at cost aggregation stage where AWBF’s radius or window size of 19, spatial adjustment of 17 and disparity similarity value of 0.3 was selected. This process is introduced to preserve and improve the object boundaries. Finally, this thesis present Outlier Detection, window size of 5 for Median Filter and fill-in invalid disparity to utilize the last stage of SMA to handle the accuracy of disparity map in regions of occluded, repetitive and low texture. The experimental result on the proposed algorithm is able to reduce 17.4% of weighted average error for all and 9.62% of weighted average error for nonocc (nonoccluded) compared to others Stereo Matching Algorithm without the proposed framework. This framework experimental result was also compared with other methods which located in standard benchmarking dataset from the Middlebury. New SMA was developed in this thesis based on AWBF. The weighted average error of disparity map in both all and nonocc attribute are reduced based on the quantitative and qualitative measurements. Comparison of this framework continue in some of the state-of-the-arts algorithm in the literature and the results is outperformed based on the proposed algorithm experimental result. 2019 Thesis NonPeerReviewed text en http://eprints.utem.edu.my/id/eprint/24618/1/Development%20Of%20Stereo-Matching%20Algorithm%20Based%20On%20Adaptive%20Weighted%20Prediction.pdf text en http://eprints.utem.edu.my/id/eprint/24618/2/Development%20Of%20Stereo-Matching%20Algorithm%20Based%20On%20Adaptive%20Weighted%20Prediction.pdf Abd Razak, Siti Safwana (2019) Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction. Masters thesis, Universiti Teknikal Malaysia Melaka. https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117088
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
Abd Razak, Siti Safwana
Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction
description In computer vision field, one of most active topics in research is estimation of depth map which is Stereo Matching (SM) process and it’s also known as Stereo Vision Disparity Map (SVDM). The real challenge in SM is to get high accuracy of disparity map. Matching cost computation produces high noise of disparity map and input images contains low texture area and repetitive pattern also leads to high error on development of disparity map. Other than that, wrong information of each pixel of disparity map which can affect the accuracy of SVDM. Therefore, to overcome the causes of effected accuracy, new Stereo Matching Algorithm (SMA) based on Adaptive Weighted Bilateral Filter (AWBF) was introduced together with characterize the SMA based on quantitative and qualitative measurements and produced SMA performance were evaluate using standard taxonomy of SM. This thesis proposes an algorithm to handle the limitations. Firstly, pre-processing used Sobel Filter was added at initial step to compensate photometric distortion of input images. Then for matching cost, the proposed SMA combine one of matching cost method with some threshold adjustment to reduces the radiometric distortions. The Sum of Absolute Different (SAD) is the matching cost’s method and some threshold adjustment were used in this thesis where the SAD’s window size of 11 and threshold value of 0.8 was selected based on the experimental results. Secondly, to overcome low texture area and repetitive pattern limitation, this thesis present AWBF at cost aggregation stage where AWBF’s radius or window size of 19, spatial adjustment of 17 and disparity similarity value of 0.3 was selected. This process is introduced to preserve and improve the object boundaries. Finally, this thesis present Outlier Detection, window size of 5 for Median Filter and fill-in invalid disparity to utilize the last stage of SMA to handle the accuracy of disparity map in regions of occluded, repetitive and low texture. The experimental result on the proposed algorithm is able to reduce 17.4% of weighted average error for all and 9.62% of weighted average error for nonocc (nonoccluded) compared to others Stereo Matching Algorithm without the proposed framework. This framework experimental result was also compared with other methods which located in standard benchmarking dataset from the Middlebury. New SMA was developed in this thesis based on AWBF. The weighted average error of disparity map in both all and nonocc attribute are reduced based on the quantitative and qualitative measurements. Comparison of this framework continue in some of the state-of-the-arts algorithm in the literature and the results is outperformed based on the proposed algorithm experimental result.
format Thesis
author Abd Razak, Siti Safwana
author_facet Abd Razak, Siti Safwana
author_sort Abd Razak, Siti Safwana
title Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction
title_short Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction
title_full Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction
title_fullStr Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction
title_full_unstemmed Development Of Stereo-Matching Algorithm Based On Adaptive Weighted Prediction
title_sort development of stereo-matching algorithm based on adaptive weighted prediction
publishDate 2019
url http://eprints.utem.edu.my/id/eprint/24618/1/Development%20Of%20Stereo-Matching%20Algorithm%20Based%20On%20Adaptive%20Weighted%20Prediction.pdf
http://eprints.utem.edu.my/id/eprint/24618/2/Development%20Of%20Stereo-Matching%20Algorithm%20Based%20On%20Adaptive%20Weighted%20Prediction.pdf
http://eprints.utem.edu.my/id/eprint/24618/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117088
_version_ 1713203447200743424
score 13.211869

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