Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

Stereo matching is one of the most active research areas in computer vision. While a large
number of algorithms for stereo correspondence have been developed, relatively little work …

In this paper, we formulate the stereo matching problem as a Markov network and solve it
using Bayesian belief propagation. The stereo Markov network consists of three coupled …

We develop a fast and accurate variable window approach. The two main ideas for
achieving accuracy are choosing a useful range of window sizes/shapes for evaluation and …

In the last decades several cost aggregation methods aimed at improving the robustness of
stereo correspondence within local and global algorithms have been proposed. Given the …

In this paper, we propose a learning-based depth estimation framework suitable for both
densely and sparsely sampled light fields. The proposed framework consists of three …

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 …

High-performance real-time stereo vision system is crucial to various stereo vision
applications, such as robotics, autonomous vehicles, multiview video coding, freeview TV …

The image motion blurring process is generally modelled as the convolution of a blur kernel
with a latent image. Therefore, the estimation of the blur kernel is essentially important for …

In this paper, a new window-based method for stereo matching is proposed. Differing with
the existing local approaches, our algorithm divides the matching process into two steps …