In this paper, we give a probabilistic model for automatic change detection on airborne images taken with moving cameras. To ensure robustness, we adopt an unsupervised coarse mat...
Csaba Benedek, Tamas Sziranyi, Zoltan Kato, and Jo...
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...
To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilist...
Hanna Pasula, Luke S. Zettlemoyer, Leslie Pack Kae...
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fracti...