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 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