We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
In this paper we present a fast method for computing acoustic likelihoods that makes use of a Graphics Processing Unit (GPU). After enabling the GPU acceleration the main processo...
We present a multiscale unsupervised segmenter for automatic detection of potentially cancerous regions of interest containing fibroglandular tissue in digital screening mammogra...
This paper proposes an automatic foreground segmentation system based on Gaussian mixture models and dynamic graph cut algorithm. An adaptive perpixel background model is develope...
Foreground segmentation is one of the most challenging problems in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combine...