We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned f...
In subband image and video compression, the issue of dyadic spatial scalability with a downsizing ratio of 2:1 has already been widely investigated. In some application scenarios,...
We consider an interactive browsing environment, with greedy optimization of a current view, conditioned on the availability of previously transmitted information for other (possi...
Pietro Zanuttigh, Nicola Brusco, David Taubman, Gu...
In this paper, we revisit the manifold assumption which has been widely adopted in the learning-based image superresolution. The assumption states that point-pairs from the high-r...
This article presents a new adaptive framework for locally parallel texture modeling. Oscillating patterns are modeled with functionals that constrain the local Fourier decompositi...