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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| Format: | Thesis |
| Language: | English |
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2017
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| Online Access: | http://eprints.usm.my/45789/ http://eprints.usm.my/45789/1/Improvement%20Of%20Local-Based%20Stereo%20Vision%20Disparity%20Map%20Estimation%20Algorithm.pdf |
| _version_ | 1848880426624483328 |
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| author | Hamzah, Rostam Affendi |
| author_facet | Hamzah, Rostam Affendi |
| author_sort | Hamzah, Rostam Affendi |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T18:02:56Z |
| format | Thesis |
| id | usm-45789 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:02:56Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-457892021-11-17T03:42:15Z http://eprints.usm.my/45789/ Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm 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 NonPeerReviewed application/pdf en http://eprints.usm.my/45789/1/Improvement%20Of%20Local-Based%20Stereo%20Vision%20Disparity%20Map%20Estimation%20Algorithm.pdf Hamzah, Rostam Affendi (2017) Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | T Technology TA401-492 Materials of engineering and construction. Mechanics of materials Hamzah, Rostam Affendi Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm |
| title | 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_short | Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm |
| title_sort | improvement of local-based stereo vision disparity map estimation algorithm |
| topic | T Technology TA401-492 Materials of engineering and construction. Mechanics of materials |
| url | http://eprints.usm.my/45789/ http://eprints.usm.my/45789/1/Improvement%20Of%20Local-Based%20Stereo%20Vision%20Disparity%20Map%20Estimation%20Algorithm.pdf |