The stereo matching problem takes two images captured by nearby cameras and attempts to recover quantitative disparity information. Most of the existing stereo matching algorithms find it difficult to estimate disparity in the occlusion, discontinuities and textureless regions in the images. In the last few decades, a number of stereo matching methods have been proposed to overcome some of these problems. In the same line of thought, the authors propose a new feature-based stereo matching method, which consists of four basic steps -feature-based stereo correspondence, two-pass cost aggregation, disparity computation using winner-takes-all selection and finally, the disparity refinement. In the proposed method, local features of Gabor wavelet in spatial domain are used for matching cost computation and subsequently a cost aggregation step is implemented by combined use of the Kuwahara filter and the median filter. Experimental results on the Middlebury benchmark database shows that the proposed method outperforms many existing local stereo matching methods.

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In many computer vision applications, identification of moving objects is a critical task. It involves classification of a pixel into either foreground or background. Background subtraction is a common approach used to achieve such classifications to remove background from the current frame. Background subtraction/modeling is extremely difficult due to illumination variations and the presence of shadow and/or occlusion. Single camera-based setup cannot perfectly handle all these problems. To overcome some of these problems, multiple camera-based backgrounds modeling system is proposed to extract multi-view objects. In this paper, homography and codebook-based approaches are utilized to detect the moving objects. Subsequently, a new heuristics is proposed which is quite robust to sudden lighting changes. The proposed method is also robust to shadows. Experimental results show that the proposed foreground segmentation method gives better performance compared to the single camera-based counterparts and other conventional approaches.

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.