Abstract--We present a versatile framework for tractable computation of approximate variances in large-scale Gaussian Markov random field estimation problems. In addition to its ef...
Dmitry M. Malioutov, Jason K. Johnson, Myung Jin C...
Geometric constraint solving is a key issue in CAD/CAM. Since Owen’s seminal paper, solvers typically use graph based decomposition methods. However, these methods become diffi...
The Web graph is a giant social network whose properties have been measured and modeled extensively in recent years. Most such studies concentrate on the graph structure alone, an...
Many models of wireless sensor networks (WSNs) assume a perfect synchronization along the graph of such network as a simplifying assumption. In our contribution we base our invest...
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...