Shannon's sampling theory and its variants provide effective solutions to the problem of reconstructing a signal from its samples in some "shift-invariant" space, wh...
Sathish Ramani, Dimitri Van De Ville, Thierry Blu,...
This paper describes a method to obtain a closed surface that approximates a general 3D data point set with non-uniform density. Aside from the positions of the initial data point...
A novel framework for spatially estimating unknown image data is presented. Common applications include inpainting, concealment of transmission errors, prediction in video coding,...
Haricharan Lakshman, Patrick Ndjiki-Nya, Martin K&...
Sparse representation for machine learning has been exploited in past years. Several sparse representation based classification algorithms have been developed for some application...
—A challenge in providing scientific data services to a broad user base is to also provide the metadata services and tools the user base needs to correctly interpret and trust t...
Stephan Zednik, Peter Fox, Deborah L. McGuinness, ...