In this paper, we study the use of optical flow as a characteristic for tracking. We analyze the behavior of three flowbased observation models for particle filter algorithms, and...
The paper introduces a system model called the probabilistic asynchronous model which characterises the context in which many practical and the Internet-based applications are bui...
Hidden Markov Models (HMM) are probabilistic graphical models for interdependent classification. In this paper we experiment with different ways of combining the components of an ...
We propose an approach to learning the semantics of images which allows us to automatically annotate an image with keywords and to retrieve images based on text queries. We do thi...
We present a Bayesian approach to color constancy which utilizes a nonGaussian probabilistic model of the image formation process. The parameters of this model are estimated direc...
Charles R. Rosenberg, Thomas P. Minka, Alok Ladsar...