Results 131 to 140 of about 157,133 (176)

Hardware-efficient belief propagation

2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009
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 it difficult to parallelize the computation.
null Chia-Kai Liang, null Chao-Chung Cheng, null Yen-Chieh Lai, null Liang-Gee Chen, Homer H Chen   +4 more
openaire   +1 more source

Motion Estimation via Belief Propagation

14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007
We present a probabilistic model for motion estimation in which motion characteristics are inferred on the basis of a finite mixture of motion models. The model is graphically represented in the form of a pairwise Markov Random Field network upon which a Loopy Belief Propagation algorithm is exploited to perform inference.
Giuseppe Boccignone, Angelo Marcelli, Paolo Napoletano, FERRARO, Mario   +3 more
openaire   +4 more sources

Convex combination belief propagation

Applied Mathematics and Computation, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Grim, Anna, Felzenszwalb, Pedro
openaire   +1 more source

Stereo Matching Using Belief Propagation

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion.
null Jian Sun, null Nan-Ning Zheng, null Heung-Yeung Shum   +2 more
openaire   +1 more source

Iterative decoding beyond belief propagation

2010 Information Theory and Applications Workshop (ITA), 2010
At the heart of modern coding theory lies the fact that low-density parity-check (LDPC) codes can be efficiently decoded by belief propagation (BP). The BP is an inference algorithm which operates on a graphical model of a code, and lends itself to low-complexity and high-speed implementations, making it the algorithm of choice in many applications. It
Planjery, Shiva, Chilappagari, Shashi Kiran, Vasic, Bane, Declercq, David, Danjean, Ludovic   +4 more
openaire   +1 more source

Regularized Gaussian belief propagation

Statistics and Computing, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Francois Kamper, Johan A. du Preez, Sarel J. Steel, Stephan Wagner   +3 more
openaire   +1 more source

Dithered Belief Propagation Decoding

IEEE Transactions on Communications, 2012
We introduce two dithered belief propagation decoding algorithms to lower the error floor with a minimal hardware overhead. One of the algorithms can additionally improve the decoding performance in the waterfall region using a large iteration limit but with a negligible increase in the average time complexity.
Francois Leduc-Primeau, Saied Hemati, Shie Mannor, Warren J. Gross   +3 more
openaire   +2 more sources

Shuffled belief propagation decoding

Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002., 2003
In this paper, we propose a shuffled version of the belief propagation (BP) algorithm for the decoding of low-density parity-check (LDPC) codes. We show that when the Tanner graph of the code is acyclic and connected, the proposed scheme is optimal in the sense of MAP decoding and converges faster (or at least no slower) than the standard BP algorithm.
null Juntan Zhang, M. Fossorier
openaire   +1 more source

Grid-based belief propagation

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2017
This paper considers the problem of decentralized, cooperative, and dynamic self-localization in wireless sensor networks. In particular, we are interested in a restrictive but very realistic scenario where few anchors are deployed and each anchor whose location is priori known may only communicate with very few agents (e.g.
Yang Song, Chong Xiao Wang, Wee Peng Tay, Choi Look Law   +3 more
openaire   +1 more source

Noise predictive belief propagation

IEEE International Conference on Communications, 2005. ICC 2005. 2005, 2005
We introduce iterative noise whitening for belief propagation (BP) based channel detectors over intersymbol interference (ISI) channels with correlated noise. Called noise predictive belief propagation (NPBP), the new scheme iteratively whitens the noise samples by modifying the edge probability computation of the BP algorithm.
M.N. Kaynak, T.M. Duman, E.M. Kurtas
openaire   +1 more source