Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov Random Field (MRF) formulation. Here, the challenge arises ...
Darko Zikic, Ben Glocker, Oliver Kutter, Martin Gr...
Online forum discussions are emerging as valuable information repository, where knowledge is accumulated by the interaction among users, leading to multiple threads with structure...
Hongning Wang, Chi Wang, ChengXiang Zhai, Jiawei H...
ded abstract of this work appears in Public Key Cryptography — PKC 2011, ed. R. Gennaro, Springer LNCS 6571 (2011), 1–16. This is the full version. We propose a linearly homom...
—Vector field visualization techniques have evolved very rapidly over the last two decades, however, visualizing vector fields on complex boundary surfaces from computational ...
Zhenmin Peng, Edward Grundy, Robert S. Laramee, Gu...