Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
A distributed memory parallel version of the group average Hierarchical Agglomerative Clustering algorithm is proposed to enable scaling the document clustering problem to large c...
Rebecca Cathey, Eric C. Jensen, Steven M. Beitzel,...
—Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multivariate data. Most visualization algorithms aim to find a projec...
This paper presents a top-down approach to 3D data analysis by fitting a Morphable Model to scans of faces. In a unified framework, the algorithm optimizes shape, texture, pose an...
Fully automatic, completely reliable segmentation in medical images is an unrealistic expectation with today's technology. However, many automatic segmentation algorithms may ...