Space constrained optimization problems arise in a multitude of important applications such as data warehouses and pervasive computing. A typical instance of such problems is to s...
Themistoklis Palpanas, Nick Koudas, Alberto O. Men...
We observe that existing methods for failure-tolerance are inefficient in their use of time, storage and computational resources. We aim to harness the power of idle desktop compu...
Multi-resolution techniques have been used in a wide range of vision applications. Unfortunately, the costly operation of building a proper pyramid strongly reduces its value as a...
Markov Random Fields (MRFs) are ubiquitous in lowlevel computer vision. In this paper, we propose a new approach to the optimization of multi-labeled MRFs. Similarly to -expansion...
Many methods for 3D reconstruction in computer vision rely on probability models, for example, Bayesian reasoning. Here we introduce a probability model of surface visibilities in ...