Belief propagation

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Belief propagation is a message passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields. (Wikipedia.org)






Conferences related to Belief propagation

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2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE 23rd International Conference on Information Fusion (FUSION)

The International Conference on Information Fusion is the premier forum for interchange of the latest research in data and information fusion, and its impacts on our society. The conference brings together researchers and practitioners from academia and industry to report on the latest scientific and technical advances.


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


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Periodicals related to Belief propagation

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


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Most published Xplore authors for Belief propagation

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Xplore Articles related to Belief propagation

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Hardware-Efficient Belief Propagation

IEEE Transactions on Circuits and Systems for Video Technology, 2011

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. In this paper, we propose two techniques to address these ...


Overlapped decoding for a class of quasi-cyclic LDPC codes

IEEE Workshop onSignal Processing Systems, 2004. SIPS 2004., 2004

In low-density parity-check (LDPC) code decoding with the iterative sum- product algorithm (SPA), due to the randomness of the parity-check matrix, H, the overlapping of the check node processing unit (CNU) and variable node processing unit (VNU) in the same clock cycle is difficult. The paper demonstrates that overlapped decoding can be exploited as long as the LDPC matrix is ...


Asymptotic bit error probability of LDPC codes for the binary erasure channel with finite number of iterations

2008 IEEE International Symposium on Information Theory, 2008

We consider communication over the binary erasure channel (BEC) using low- density parity-check (LDPC) code and belief propagation (BP) decoding. Furthermore, a gap between the bit error probability after finite number of iterations for finite block length n and that for infinite block length is asymptotically alpha/n, where alpha denotes a speci..c constant determined by a degree distribution, a number ...


Comments on successive relaxation for decoding of LDPC codes

IEEE Transactions on Communications, 2009

The application of successive relaxation (SR) to the fixed-point problem associated with the iterative decoding of low-density parity-check (LDPC) codes was proposed by Hemati et al.. The simulation results presented by Hemati et al. for the SR version of belief propagation (BP) in the likelihood ratio (LR) domain and that of min-sum (MS) in the log-likelihood ratio (LLR) domain are ...


Dynamic background discrimination with belief propagation

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2004

A probabilistic graphical model is proposed for the complex video foreground and background discrimination. The model learns the temporal and the spatial correlation from the video input data. The inference of the graphical model is achieved with the generalized belief propagation algorithm. Experiments have shown that the proposed method is able to model the dynamic backgrounds containing swaying trees, bushes ...


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Educational Resources on Belief propagation

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IEEE-USA E-Books

  • Hardware-Efficient Belief Propagation

    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. In this paper, we propose two techniques to address these issues. The first technique is a new message passing scheme named tile-based BP that reduces the memory and bandwidth to a fraction of the ordinary BP algorithms without performance degradation by splitting the MRF into many tiles and only storing the messages across the neighboring tiles. The tile-wise processing also enables data reuse and pipeline, resulting in efficient hardware implementation. The second technique is an <i>O</i>(<i>L</i>) fast message construction algorithm that exploits the properties of robust functions for parallelization. We apply these two techniques to a very large-scale integration circuit for stereo matching that generates high-resolution disparity maps in near real-time. We also implement the proposed schemes on graphics processing unit (GPU) which is four-time faster than standard BP on GPU.

  • Overlapped decoding for a class of quasi-cyclic LDPC codes

    In low-density parity-check (LDPC) code decoding with the iterative sum- product algorithm (SPA), due to the randomness of the parity-check matrix, H, the overlapping of the check node processing unit (CNU) and variable node processing unit (VNU) in the same clock cycle is difficult. The paper demonstrates that overlapped decoding can be exploited as long as the LDPC matrix is composed of identity matrices and their cyclic-shifted matrices, i.e., the parity-check matrix, H, belongs to a class of quasi-cyclic LDPC codes. It is shown that the number of clock cycles required for decoding can be reduced by 50% when overlapped decoding is applied to a (3,6)-regular LDPC code decoder.

  • Asymptotic bit error probability of LDPC codes for the binary erasure channel with finite number of iterations

    We consider communication over the binary erasure channel (BEC) using low- density parity-check (LDPC) code and belief propagation (BP) decoding. Furthermore, a gap between the bit error probability after finite number of iterations for finite block length n and that for infinite block length is asymptotically alpha/n, where alpha denotes a speci..c constant determined by a degree distribution, a number of iterations and erasure probability. Our main result is to derive an ef..cient algorithm for calculating alpha for regular ensembles.

  • Comments on successive relaxation for decoding of LDPC codes

    The application of successive relaxation (SR) to the fixed-point problem associated with the iterative decoding of low-density parity-check (LDPC) codes was proposed by Hemati et al.. The simulation results presented by Hemati et al. for the SR version of belief propagation (BP) in the likelihood ratio (LR) domain and that of min-sum (MS) in the log-likelihood ratio (LLR) domain are based on the assumption of all-zero codeword transmission. This assumption however results in erroneous error rates when SR is applied in the LR domain. Here, we correct the simulation results reported by Hemati et al. for SR-BP in the LR domain. Furthermore, we investigate the performance of SR- BP and SR-MS in the LLR and LR domains, respectively. The results for a binary input additive white Gaussian noise (BIAWGN) channel show that for both BP and MS, the application of SR in the two domains of LR and LLR results in different error correcting performance. In particular, for the tested codes, it is shown that among the four algorithms, SR-MS-LLR has the best performance. It outperforms standard MS and BP by up to about 0.6 dB and 0.3 dB, respectively, offering an attractive solution in terms of performance/complexity tradeoff.

  • Dynamic background discrimination with belief propagation

    A probabilistic graphical model is proposed for the complex video foreground and background discrimination. The model learns the temporal and the spatial correlation from the video input data. The inference of the graphical model is achieved with the generalized belief propagation algorithm. Experiments have shown that the proposed method is able to model the dynamic backgrounds containing swaying trees, bushes and moving ocean waves. The final segmentation results are very promising.

  • A 30fps stereo matching processor based on belief propagation with disparity-parallel PE array architecture

    In this paper, we propose a real-time stereo matching processor based on the belief propagation algorithm. Computationally complex message construction is accelerated by a disparity-parallel PE array architecture, which calculates messages for all disparity levels (1-32) in parallel. A tile-based belief propagation approach reduces the on-chip memory requirements by 95.4% compared to the previous works. In addition, a two-level on-chip buffer and memory access pipelining enable high PE utilization of 89%. As a result, the message construction rate of the PEs is increased by 6.45x compared to previous works. The fabricated processor in a 0.18um CMOS process achieves 30 fps performance for QVGA (320×240) video inputs at 200 MHz operating frequency.

  • Turbo decoding of product codes using adaptive belief propagation

    The adaptive belief propagation (ABP) algorithm was recently proposed by Jiang and Narayanan for the soft decoding of Reed-Solomon (RS) codes. In this paper, simplified versions of this algorithm are investigated for the turbo decoding of product codes. The complexity of the turbo-oriented adaptive belief propagation (TAB) algorithm is significantly reduced by moving the matrix adaptation step outside of the belief propagation iteration loop. A reduced- complexity version of the TAB algorithm that offers a trade-off between performance and complexity is also proposed. Simulation results for the turbo decoding of product codes show that belief propagation based on adaptive parity check matrices is a practical alternative to the currently very popular Chase-Pyndiah algorithm.

  • Lowering the Error Floor of LDPC Codes by a Two-stage Hybrid Decoding Algorithm

    In this paper, a hybrid decoding scheme is proposed to lower the error floor of low-density parity-check codes. With the observation that some error bits' LLR values oscillate throughout iterative decoding procedure, a "feedback BP" (FBP) decoding algorithm is presented as second-stage decoding cell to reduce the phenomena of oscillations. The hybrid decoding scheme, which consists LLR- BP decoding algorithm and FBP decoding algorithm, detects errors in the codewords obtained by using the parity check equations of LDPC codes. Simulation results show that the new decoding scheme exhibits a lower error floor than that of belief propagation decoding algorithm in the moderate and high SNR region.

  • Inferring BP Priority Order Using 5D Tensor Voting for Inpainting-Based Macroblock Prediction

    Summary form only given. In this paper, we propose an optimized in painting- based macro block(MB) prediction mode (IP-mode) in the state-of-the-art H.264/AVC video compression engine, and belief propagation (BP) is applied to achieve the global spatio-temporal consistency between the predicted content and the co-located known region. To decrease the computing complexity of the iterative BP algorithm, we explore structure and motion features by tensor votes projected from the decoded regions, to assign the priority of message scheduling and prune the intolerable labels. No side information is need to be coded into the bit stream, while the structure and motion information is estimated from the decoded region at decoder side. Compared with the existing prediction modes in H.264/AVC, the proposed IP-mode only encode the macro block header and residual data, where the residual is lighter in homogeneous texture regions by the optimized BP algorithm with label pruning. Experiments validate that the proposed video compression scheme can achieve a better R-D performance, and the computing complexity is largely reduced through the inference of structure and motion features.

  • Performance analysis of finite-length LDPC codes

    In this paper, we are concerned with the finite-length analysis of low-density parity-check (LDPC) codes when used over additive white Gaussian noise (AWGN) channels. We present the iterative decoding algorithm of LDPC codes, and we also discuss the encoding methods and the bipartite graph representation of LDPC codes ensemble. Finite-length LDPC code performances are investigated, and the influence of cycle is pointed out. Simulation results show that irregular LDPC codes with variable and check node degree distribution optimized for infinite-length codes are not optimal for finite-length LDPC codes, and the error patterns of both regular and irregular codes are investigated as an effort to improve the performance and design methodology of finite-length LDPC codes.



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