Abstract Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate anatomical
objects of interest for analysis is essential in almost any computer-aided diagnosis system or …

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 …

Among the most exciting advances in early vision has been the development of efficient
energy minimization algorithms for pixel-labeling tasks such as depth or texture …

This paper proposes a novel framework for labelling problems which is able to combine
multiple segmentations in a principled manner. Our method is based on higher order …

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for
future study. This volume demonstrates the power of the Markov random field (MRF) in …

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in
computer vision and image understanding, with respect to the modeling, the inference and …

Second-order priors on the smoothness of 3D surfaces are a better model of typical scenes
than first-order priors. However, stereo reconstruction using global inference algorithms …

The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple
discrete or continuous labels remains an open question. In this paper, we demonstrate one …

The problems of dense stereo reconstruction and object class segmentation can both be
formulated as Random Field labeling problems, in which every pixel in the image is …