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...
Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
Local appearance models in the neighborhood of salient image features, together with local and/or global geometric constraints, serve as the basis for several recent and effective...
In this paper, we propose a series of techniques to enhance the computational performance of existing Belief Propagation (BP) based stereo matching that relies on automatic estima...
Shafik Huq, Andreas Koschan, Besma R. Abidi, Mongi...
Consider multiple users searching for a hotel room, based on size, cost, distance to the beach, etc. Users may have variable preferences expressed by different weights on the attri...