Identifying background (context) information in scientific articles can help scholars understand major contributions in their research area more easily. In this paper, we propose ...
In this paper, we describe a new system for converting a user's freehand sketch of a tree into a full 3D model that is both complex and realistic-looking. Our system does thi...
Xuejin Chen, Boris Neubert, Ying-Qing Xu, Oliver D...
This paper presents an optimisation technique to automatically select a set of control parameters for a Markov Random Field. The method is based on the Reactive Tabu Search strate...
Umberto Castellani, Andrea Fusiello, Riccardo Gher...
This paper describes how a visual system can automatically define features of interest from the observation of a large enough number of natural images. The principle complements t...
In this paper, we propose an unsupervised segmentation algorithm for extracting moving objects/regions from compressed video using Markov Random Field (MRF) classification. First,...
We consider the problem of image deconvolution. We foccus on a Bayesian approach which consists of maximizing an energy obtained by a Markov Random Field modeling. MRFs are classi...
We relate the problem of finding the best application of a Synchronous ContextFree Grammar (SCFG) rule during parsing to a Markov Random Field. This representation allows us to u...
The paper deals with the design and implementation of a stereo algorithm. Disparity map is formulated as a Markov Random Field with a new smoothness constraint depending not only ...
Nello Balossino, Maurizio Lucenteforte, Luca Piova...
Abstract –In this paper, a new problem, consensus estimation, is formulated, whose setting is complementary to the well-known CEO problem. In particular, a set of nodes are emplo...
We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selectio...
Jesse Butterfield, Odest Chadwicke Jenkins, Brian ...