Optimization techniques based on graph cuts have become a standard tool for many vision
applications. These techniques allow to minimize efficiently certain energy functions …

Optimization is a powerful paradigm for expressing and solving problems in a wide range of
areas, and has been successfully applied to many vision problems. Discrete optimization …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …

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 …

Many tasks in computer vision involve assigning a label (such as disparity) to every pixel. A
common constraint is that the labels should vary smoothly almost everywhere while …

Several new algorithms for visual correspondence based on graph cuts have recently been
developed. While these methods give very strong results in practice, they do not handle …

In typical classification tasks, we seek a function which assigns a label to a single object.
Kernel-based approaches, such as support vector machines (SVMs), which maximize the …

Phase unwrapping is the inference of absolute phase from modulo-2pi phase. This paper
introduces a new energy minimization framework for phase unwrapping. The considered …

Water/fat separation is a classical problem for in vivo proton MRI. Although many methods
have been proposed to address this problem, robust water/fat separation remains a …