Long-duration tracking of general targets is quite challenging for computer vision, because in practice target may undergo large uncertainties in its visual appearance and the unc...
Discriminant Analysis (DA) methods have demonstrated their utility in countless applications in computer vision and other areas of research ? especially in the C class classificat...
We introduce a theoretical framework and practical algorithms for replacing time-coded structured light patterns with viewpoint codes, in the form of additional camera locations. ...
Mark Young, Erik Beeson, James Davis, Szymon Rusin...
In this paper we propose a new segmentation algorithm which combines patch-based information with edge cues under a probabilistic framework. We use a mixture of multiple Gaussians...
This paper describes an online learning based method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. Our fire detection method...
A framework for the regularized estimation of nonuniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, th...
In this paper, we focus on the design of Markov Chain Monte Carlo techniques in a statistical registration framework based on finite element basis (FE). Due to the use of FE basis...
Fully automatic 3D modeling from a catadioptric image sequence has rarely been addressed until now, although this is a long-standing problem for perspective images. All previous c...
We present a computer vision system for robust object tracking in 3D by combining evidence from multiple calibrated cameras. This kernel-based 3D tracker is automatically bootstra...
Ambrish Tyagi, Mark A. Keck, James W. Davis, Geras...
Outdoor face recognition is among the most challenging problems for face recognition. In this paper, we develop a discriminant mutual subspace learning algorithm for indoor and ou...