Depth from stereo is an important research field in computer vision due to the wide range of
its applications. In this work, we present a stereo matching algorithm based on belief …

Depth information can be obtained using stereo matching algorithms, which compute the
horizontal displacement (disparity) of the corresponding points and convert to depth …

We propose a novel formulation of stereo matching that considers each pixel as a feature
vector. Under this view, matching two or more images can be cast as matching point clouds …

In this paper, we formulate the stereo matching problem as a Markov network consisting of
three coupled Markov random fields (MRF's). These three MRF's model a smooth field for …

This paper presents a new global matching framework for stereo computation. In this
framework, the second view is first predicted from the reference view using the depth …

Typically, local methods for stereo matching are fast but have relatively low degree of
accuracy while global ones, though costly, achieve a higher degree of accuracy in retrieving …

Discovering depth from stereopsis is difficult because the quality of un-cooled sensors is not
sufficient for generating dense depth maps. We show how to produce sparse disparity maps …

The depth of a scene is very important in computer vision. One way to get the depth map is
by stereo vision. But because of the noise, textureless area of the scene and the occlusion …

Disparity estimation in stereoscopic vision is a vital step for the extraction of depth
information from stereo images. This paper presents the hardware implementation of a …

With the rapid growth in infrared sensor technology and its drastic cost reduction, the
potential of application of these imaging technologies in computer vision systems has …