Motion analysis is one of the most fundamental and challenging problems in the field of
computer vision, which can be widely applied in many areas, such as autonomous driving …

It has been recently shown that a convolutional neural network can learn optical flow
estimation with unsuper-vised learning. However, the performance of the unsuper-vised …

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 …

Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate
that motion will be exploited most effectively, if it is regarded over larger time windows …

This paper describes a method for scene reconstruction of complex, detailed environments
from 3D light fields. Densely sampled light fields in the order of 109 light rays allow us to …

Dense motion estimations obtained from optical flow techniques play a significant role in
many image processing and computer vision tasks. Remarkable progress has been made in …

Natural image statistics indicate that we should use nonconvex norms for most
regularization tasks in image processing and computer vision. Still, they are rarely used in …

Occlusion relations inform the partition of the image domain into``objects''but are difficult to
determine from a single image or short-baseline video. We show how long-term occlusion …

The segmentation of transparent objects can be very useful in computer vision applications.
However, because they borrow texture from their background and have a similar …

With the aim to improve accuracy of stereo confidence measures, we apply the random
decision forest framework to a large set of diverse stereo confidence measures. Learning …