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: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://eprints.usm.my/45789/1/Improvement%20Of%20Local-Based%20Stereo%20Vision%20Disparity%20Map%20Estimation%20Algorithm.pdf |
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| Summary: | 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. |
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