Asymmetric occlusion detection using linear regression and weight-based filling for stereo disparity map estimation

Stereo matching computes the disparity information from stereo image pairs. A number of stereo matching methods have been proposed to estimate a fine disparity map. However, objects present in the images are occluded on account of different camera viewpoints in a stereo vision setup, and hence it is quite difficult to get a fine disparity map. The methods which use disparity map information of two cameras (symmetric approach) to detect occluded pixels are computationally more complex. The authors approach entails to detect the occluded pixels only by using single disparity map information (asymmetric approach). The behaviour of reference and target pixels are analysed, and it is observed that the target matching pixels almost follow a linear pattern with respect to the reference image pixels. Hence, it is approximated by a linear regression model, and subsequently this model is used to detect the occluded pixels in the authors' method. Finally, a fine disparity map is obtained by incorporating a novel occlusion filling method. Experimental results show that the proposed occlusion detection method gives almost similar performance as that of the methods which use two disparity maps for detection. For occlusion filling, the authors utilise support weights from both the stereo images, and hence their method can give better performance.