Sequential random sampling (`Markov Chain Monte-Carlo') is a popular strategy for many vision problems involving multimodal distributions over high-dimensional parameter spac...
Getting trapped in suboptimal local minima is a perennial problem in model based vision, especially in applications like monocular human body tracking where complex nonlinear para...
This paper shows how the output of a number of detection and tracking algorithms can be fused to achieve robust tracking of people in an indoor environment. The new tracking system...
Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an ...
We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple lowresolution video sequences of the same dynamic scene. ...
We address the problem of face recognition from a large set of images obtained over time - a task arising in many surveillance and authentication applications. A set or a sequence ...
Gregory Shakhnarovich, John W. Fisher III, Trevor ...
We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the d...
This paper examines issues arising in applying a previously developed edit-distance shock graph matching technique to indexing into large shape databases. This approach compares th...
Thomas B. Sebastian, Philip N. Klein, Benjamin B. ...
There has been considerable success in automated reconstruction for image sequences where small baseline algorithms can be used to establish matches across a number of images. In c...
We analyze the problem of recovering the shape of a mirror surface. We generalize the results of [1], where the special case of planar and spherical mirror surfaces was considered,...