Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the train...
Volume rendering is a flexible technique for visualizing dense 3D volumetric datasets. A central element of volume rendering is the conversion between data values and observable q...
Aaron E. Lefohn, Charles D. Hansen, Emil Praun, Jo...
Weather visualization is a difficult problem because it comprises volumetric multi-field data and traditional surface-based approaches obscure details of the complex three-dimen...
Kirk Riley, David S. Ebert, Charles D. Hansen, Jas...
Based on the observation that it is relatively easier for users to generate several good transfer functions (TFs) for different features of volumetric data, we propose TF fusing, ...
Yingcai Wu, Huamin Qu, Hong Zhou, Ming-Yuen Cha...
Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires mode...