Results 1 - 10 of 11,359

Efficient belief propagation for early vision

by Pedro F. Felzenszwalb, Daniel P. Huttenlocher - In CVPR , 2004
"... Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical u ..."
Abstract - Cited by 515 (8 self) - Add to MetaCart
Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical

Hardware-efficient belief propagation

by Chia-kai Liang, Student Member, Chao-chung Cheng, Yen-chieh Lai, Liang-gee Chen, Homer H. Chen - in Proc. CVPR , 2009
"... Abstract—Loopy belief propagation (BP) is an effective solution for assigning labels to the nodes of a graphical model such as the Markov random field (MRF), but it requires high memory, bandwidth, and computational costs. Furthermore, the iterative, pixel-wise, and sequential operations of BP make ..."
Abstract - Cited by 14 (1 self) - Add to MetaCart
Abstract—Loopy belief propagation (BP) is an effective solution for assigning labels to the nodes of a graphical model such as the Markov random field (MRF), but it requires high memory, bandwidth, and computational costs. Furthermore, the iterative, pixel-wise, and sequential operations of BP make

Efficient Belief Propagation for Early Vision

by Pedro Felzenszwalb And, Pedro F. Felzenszwalb, Daniel P. Huttenlocher - In CVPR , 2004
"... Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical u ..."
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Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph cuts and belief propagation yield accurate results, but despite recent advances are often still too slow for practical

EFFICIENT BELIEF PROPAGATION FOR IMAGE RESTORATION 1 CMPS 290C Project Efficient Belief Propagation for Image Restoration

by Qi Zhao
"... Abstract—The Markov Random Field (MRF) theory provides a consistent way for modeling context dependent entities such as image pixels. Trying to solve the image restoration problem in the MRF framework is an optimization problem that is NP hard, and approximation techniques like the belief propagatio ..."
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propagation methods are proposed. The problem of the belief propagation is its inefficiency. In this project, I implement the efficient belief propagation method proposed by Felzenszwalb and Huttenlocher, applying it to additive noise removal and image inpainting. Further, other methods for additive noise

Efficient Belief Propagation for Utility Maximization and Repeated Inference

by Aniruddh Nath, Pedro Domingos - PROCEEDINGS OF THE TWENTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-10) , 2010
"... Many problems require repeated inference on probabilistic graphical models, with different values for evidence variables or other changes. Examples of such problems include utility maximization, MAP inference, online and interactive inference, parameter and structure learning, and dynamic inference. ..."
Abstract - Cited by 14 (0 self) - Add to MetaCart
. Since small changes to the evidence typically only affect a small region of the network, repeatedly performing inference from scratch can be massively redundant. In this paper, we propose expanding frontier belief propagation (EFBP), an efficient approximate algorithm for probabilistic inference

Facility Locations Revisited: An Efficient Belief Propagation Approach

by Wenye Li, Linli Xu, Dale Schuurmans
"... Abstract — This paper studies the fixed-charge facility location problem—an important problem in logistics and operations research that has wide application to many areas of commerce and industry. The problem is to locate a small number of facilities among nodes in a network to provide good service ..."
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to the client nodes while confining the total construction cost. The problem is NP-hard, but of sufficient importance to warrant developing practical heuristics. To handle large instances, we propose an algorithm based on recent advances in belief propagation and graphical models. In particular, we adapt a form

Efficient belief propagation with learned higher-order Markov random fields

by Xiangyang Lan, Stefan Roth, Daniel Huttenlocher, Michael J. Black - IN ECCV (2 , 2006
"... Belief propagation (BP) has become widely used for low-level vision problems and various inference techniques have been proposed for loopy graphs. These methods typically rely on ad hoc spatial priors such as the Potts model. In this paper we investigate the use of learned models of image structure, ..."
Abstract - Cited by 81 (6 self) - Add to MetaCart
Belief propagation (BP) has become widely used for low-level vision problems and various inference techniques have been proposed for loopy graphs. These methods typically rely on ad hoc spatial priors such as the Potts model. In this paper we investigate the use of learned models of image structure

Efficient belief propagation for vision using linear constraint nodes

by Brian Potetz - in CVPR , 2007
"... Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, and has been successfully applied to several important computer vision problems. However, pairwise interactions are often insufficient to capture the full statistics of the problem. Higher-order intera ..."
Abstract - Cited by 39 (7 self) - Add to MetaCart
Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, and has been successfully applied to several important computer vision problems. However, pairwise interactions are often insufficient to capture the full statistics of the problem. Higher

Efficient Belief Propagation for Higher-Order Cliques Using Linear Constraint Nodes

by Brian Potetz, Tai Sing Lee - COMPUTER VISION AND IMAGE UNDERSTANDING , 2008
"... ..."
Abstract - Cited by 23 (0 self) - Add to MetaCart
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Efficient Belief Propagation for Higher Order Cliques Using Linear Constraint Nodes

by Brian Potetz, Tai Sing Lee , 2008
"... Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, and has been successfully applied to several important computer vision problems. However, pairwise interactions are often insufficient to capture the full statistics of the problem. Higher-order intera ..."
Abstract - Cited by 8 (2 self) - Add to MetaCart
and also to the use of guaranteed-convergent forms of belief propagation. To illustrate these techniques, we perform efficient inference in graphical models where the spatial prior of natural images is captured by 2×2 cliques. This approach shows significant improvement over the commonly used pairwise
Results 1 - 10 of 11,359