Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can...
We optimize automultiscopic displays built by stacking a pair of modified LCD panels. To date, such dual-stacked LCDs have used heuristic parallax barriers for view-dependent imag...
Douglas Lanman, Matthew Hirsch, Yunhee Kim, Ramesh...
—This paper aims to develop a novel framework to systematically trade-off computational complexity with output distortion in linear multimedia transforms, in an optimal manner. T...
We consider the problem of extracting the source signals from an under-determined convolutive mixture assuming known mixing filters. State-of-the-art methods operate in the time-fr...
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...