Real scenes are full of specularities (highlights and reflections), and yet most vision algorithms ignore them. In order to capture the appearance of realistic scenes, we need to ...
Rahul Swaminathan, Sing Bing Kang, Richard Szelisk...
In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF's). These three MRF's model a smooth fie...
Human activity can be described as a sequence of 3D body postures. The traditional approach to recognition and 3D reconstruction of human activity has been to track motion in 3D, m...
We consider dynamic scenes consisting of moving points whose motion is constrained to happen in one of a pencil of planes. This is for example the case when rigid objects move ind...
Estimating the parameters of a pencil of lines is addressed. A statistical model for the measurements is developed, from which the Cramer Rao lower bound is determined. An estimato...
What does it mean for a deforming object to be "moving" (see Fig. 1)? How can we separate the overall motion (a finite-dimensional group action) from the more general de...
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 ...