Most recent works in optical flow extraction focus on the accuracy and neglect the time
complexity. However, in real-life visual applications, such as tracking, activity detection and …

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 …

Differential methods belong to the most widely used techniques for optic flow computation in
image sequences. They can be classified into local methods such as the Lucas–Kanade …

We develop a framework for learning generic, expressive image priors that capture the
statistics of natural scenes and can be used for a variety of machine vision tasks. The …

This paper presents a new variational framework for detecting and tracking multiple moving
objects in image sequences. Motion detection is performed using a statistical framework for …

" This book is concerned with a probabilistic approach for image analysis, mostly from the
Bayesian point of view, and the important Markov chain Monte Carlo methods commonly …

We present a global optimization approach to optical flow estimation. The approach
optimizes a classical optical flow objective over the full space of map**s between discrete …

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 …

In this paper, we suggest a variational model for optic flow computation based on non-
linearised and higher order constancy assumptions. Besides the common grey value …

This paper describes a method to estimate parametric motion models. Motivations for the
use of such models are, on one hand, their efficiency, which has been demonstrated in …