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NIPS
2007
14 years 11 months ago
Loop Series and Bethe Variational Bounds in Attractive Graphical Models
Variational methods are frequently used to approximate or bound the partition or likelihood function of a Markov random field. Methods based on mean field theory are guaranteed ...
Erik B. Sudderth, Martin J. Wainwright, Alan S. Wi...
CVPR
2011
IEEE
14 years 5 months ago
Particle Filter with State Permutations for Solving Image Jigsaw Puzzles
We deal with an image jigsaw puzzle problem, which is defined as reconstructing an image from a set of square and non-overlapping image patches. It is known that a general instan...
Xingwei Yang, Nagesh Adluru, LonginJan Latecki
CVPR
2009
IEEE
2358views Computer Vision» more  CVPR 2009»
16 years 4 months ago
Pictorial Structures Revisited: People Detection and Articulated Pose Estimation
Non-rigid object detection and articulated pose estimation are two related and challenging problems in computer vision. Numerous models have been proposed over the years and oft...
Mykhaylo Andriluka (TU Darmstadt), Stefan Roth (TU...
CVPR
2011
IEEE
14 years 5 months ago
Learning Message-Passing Inference Machines for Structured Prediction
Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models prov...
Stephane Ross, Daniel Munoz, J. Andrew Bagnell
JAIR
2008
113views more  JAIR 2008»
14 years 9 months ago
Graphical Model Inference in Optimal Control of Stochastic Multi-Agent Systems
In this article we consider the issue of optimal control in collaborative multi-agent systems with stochastic dynamics. The agents have a joint task in which they have to reach a ...
Bart van den Broek, Wim Wiegerinck, Bert Kappen