Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
In this paper we propose a framework for gradient descent
image alignment in the Fourier domain. Specifically,
we propose an extension to the classical Lucas & Kanade
(LK) a...
Existing supercomputers have hundreds of thousands of processor cores, and future systems may have hundreds of millions. Developers need detailed performance measurements to tune ...
Todd Gamblin, Bronis R. de Supinski, Martin Schulz...
Regret minimization has proven to be a very powerful tool in both computational learning theory and online algorithms. Regret minimization algorithms can guarantee, for a single de...
We introduce a new concept for accelerating realistic image synthesis algorithms. At the core of this procedure is a novel physical error metric that correctly predicts the percep...
Mahesh Ramasubramanian, Sumanta N. Pattanaik, Dona...