Models for computer vision are commonly defined either wrt low-level concepts such as
pixels that are to be grouped, or wrt high-level concepts such as semantic objects that are to …

For the segmentation of moving objects in videos, the analysis of long-term point trajectories
has been very popular recently. In this paper, we formulate the segmentation of a video …

Clustering is one type of unsupervised learning where the goal is to partition the set of
objects into groups called clusters. Faced to the difficulty to design a general purpose …

Motion segmentation is an essential task in artificial intelligence and computer vision.
However, scene motion in real-world intelligent systems usually integrates multiple types of …

Unsupervised deep learning for optical flow computation has achieved promising results.
Most existing deep-net based methods rely on image brightness consistency and local …

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be
successful in both motion trajectory segmentation and multi-target tracking scenarios. Both …

We propose a novel Gaussian kernel based integration model (GKIM) for anomalous entities
detection and localization in pedestrian flows. The GKIM integrates spatio-temporal features …

Consider a video sequence captured by a single camera observing a complex dynamic
scene containing an unknown mixture of multiple moving and possibly deforming objects. In …

Motion segmentation is a challenging problem that seeks to identify independent motions in
two or several input images. This paper introduces the first algorithm for motion …

Many real-world video sequences cannot be conveniently categorized as general or
degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or …