TITLE:
Census and Segmentation-Based Disparity Estimation Algorithm Using Region Merging
AUTHORS:
Viral H. Borisagar, Mukesh A. Zaveri
KEYWORDS:
Stereo Vision, Census Transform, Mean Shift Segmentation, Affine Transform, Region Merging
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.6 No.3,
July
15,
2015
ABSTRACT: Disparity estimation is an ill-posed
problem in computer vision. It is explored comprehensively due to its
usefulness in many areas like 3D scene reconstruction, robot navigation, parts
inspection, virtual reality and image-based rendering. In this paper, we
propose a hybrid disparity generation algorithm which uses census based and
segmentation based approaches. Census transform does not give good results in
textureless areas, but is suitable for highly textured regions. While segment
based stereo matching techniques gives good result in textureless regions.
Coarse disparities obtained from census transform are combined with the region
information extracted by mean shift segmentation method, so that a region
matching can be applied by using affine transformation. Affine transformation
is used to remove noise from each segment. Mean shift segmentation technique
creates more than one segment of same object resulting into non-smoothness
disparity. Region merging is applied to obtain refined smooth disparity map.
Finally, multilateral filtering is applied on the disparity map estimated to
preserve the information and to smooth the disparity map. The proposed
algorithm generates good results compared to the classic census transform. Our
proposed algorithm solves standard problems like occlusions, repetitive
patterns, textureless regions, perspective distortion, specular reflection and
noise. Experiments are performed on middlebury stereo test bed and the results
demonstrate that the proposed algorithm achieves high accuracy, efficiency and
robustness.