Stereo matching is the key technology in stereo vision. Given a pair of rectified images, determines correspondences between images and estimate depth by obtaining disparity corresponding pixels. The current work has shown that estimation from can be formulated as supervised learning task with an end-to-end frame based on convolutional neural networks (CNNs). However, 3D CNN puts great burden m...

This paper presents a stereo matching method which uses multiple stereo pairs with various baselines to obtain precise depth estimates without suffering from ambiguity. In stereo processing, a short baseline means that the estimated depth will be less precise due to narrow triangulation. For more precise depth estimation, a longer baseline is desired. With a longer baseline, however, a larger d...

This thesis proposal addresses the stereoscopic matching. An introduction to the correspondence problem is given at the beginning. Then, State-of-the-Art in the field of binocular stereo matching is presented. Our previous research focusing this problem is described together with its classification among recent stereo matching approaches. Finally, the thesis goals are formulated.

This paper presents a new feature based dense stereo matching algorithm to obtain the dense disparity map via dynamic programming. After extraction of some proper features, we use some matching constraints such as epipolar line, disparity limit, ordering and limit of directional derivative of disparity as well. Also, a coarseto-fine multiresolution strategy is used to decrease the search space ...

This paper presents a new feature based dense stereo matching algorithm to obtain the dense disparity map via dynamic programming. After extraction of some proper features, we use some matching constraints such as epipolar line, disparity limit, ordering and limit of directional derivative of disparity as well. Also, a coarseto-fine multiresolution strategy is used to decrease the search space ...

Finding corresponding edges is considered being the most difficult part of edge-based stereo matching algorithms. Usually, correspondence for a feature point in the first image is obtained by searching in a predefined region of the second image, based on epipolar line and maximum disparity. Reduction of search region can increase performances of the matching process, in the context of execution...

ÐImage matching applications such as tracking and stereo commonly use the sum-of-squared-difference (SSD) measure to determine the best match. However, this measure is sensitive to outliers and is not robust to template variations. Alternative measures have also been proposed that are more robust to these issues. We improve upon these using a probabilistic formulation for image matching in term...

In this paper the notion of stability of a bipartite matching is introduced and studied. It is a strong but overlooked constraint very useful in computational stereo. The resulting algorithms require no prior continuity model, are parameter-free, very fast, and accurate in terms of mismatches. A whole class of stable matchings is presented. It is shown that some of the popular matching algorith...