IEEE Organizations related to Bayes Methods

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Conferences related to Bayes Methods

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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 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


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 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.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


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Periodicals related to Bayes Methods

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Most published Xplore authors for Bayes Methods

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Xplore Articles related to Bayes Methods

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Bayesian deconvolution of cyclostationary processes based on point processes

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

In this paper we address the problem of the Bayesian de-convolution of a widely spread class of processes, filtered point processes, whose underlying point process is a self-excited point process. In order to achieve this de- convolution, we perform powerful stochastic algorithm, the Markov chains Monte Carlo (MCMC), which despite their power have not been yet widely used in signal ...


Region-based image annotation using color and texture cues

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

We present algorithms for automatic image annotation and retrieval based on pixel-based color, and block- or region-based texture features. Region formation has been accomplished by utilizing Gibbs random fields or morphological based operations. Color, and texture indexing may be knowledge- based (using appropriate training sets) or by example. The algorithms are designed to: i) offer the user a wide range ...


A statistical solar flare forecast method

Space Weather, 2005

A Bayesian approach to solar flare prediction has been developed which uses only the event statistics of flares already observed. The method is simple and objective and makes few ad hoc assumptions. It is argued that this approach should be used to provide a baseline prediction for certain space weather purposes, upon which other methods, incorporating additional information, can improve. ...


Joint interpolation, motion and parameter estimation for image sequences with missing data

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

This paper presents a new scheme for interpolation of missing data in image sequences, an important problem in many areas including archived motion picture film and digital video. A unified framework for image data modelling and motion estimation is adopted which is based on 3-dimensional autoregressive (3DAR) models with motion correction. A fully Bayesian methodology is implemented using the Gibbs ...


Learning Techniques for Context Diagnosis and Prediction in Cognitive Communications

Cognitive Communications: Distributed Artificial Intelligence (DAI), Regulatory Policy and Economics, Implementation, None

This chapter contains sections titled:IntroductionPredictionFuture ProblemsConclusionsReferences


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Educational Resources on Bayes Methods

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IEEE-USA E-Books

  • Bayesian deconvolution of cyclostationary processes based on point processes

    In this paper we address the problem of the Bayesian de-convolution of a widely spread class of processes, filtered point processes, whose underlying point process is a self-excited point process. In order to achieve this de- convolution, we perform powerful stochastic algorithm, the Markov chains Monte Carlo (MCMC), which despite their power have not been yet widely used in signal processing.

  • Region-based image annotation using color and texture cues

    We present algorithms for automatic image annotation and retrieval based on pixel-based color, and block- or region-based texture features. Region formation has been accomplished by utilizing Gibbs random fields or morphological based operations. Color, and texture indexing may be knowledge- based (using appropriate training sets) or by example. The algorithms are designed to: i) offer the user a wide range of options and flexibilities in order to enhance the outcome of the search and retrieval operations, and ii) provide a compromise between accuracy and computational complexity.

  • A statistical solar flare forecast method

    A Bayesian approach to solar flare prediction has been developed which uses only the event statistics of flares already observed. The method is simple and objective and makes few ad hoc assumptions. It is argued that this approach should be used to provide a baseline prediction for certain space weather purposes, upon which other methods, incorporating additional information, can improve. A practical implementation of the method for whole-Sun prediction of Geostationary Observational Environment Satellite (GOES) events is described in detail and is demonstrated for 4 November 2003, the day of the largest recorded GOES flare. A test of the method is described on the basis of the historical record of GOES events (1975–2003), and a detailed comparison is made with U.S. National Oceanic and Atmospheric Administration (NOAA) predictions for 1987–2003. Although the NOAA forecasts incorporate a variety of other information, the present method outperforms the NOAA method in predicting mean numbers of event days for both M-X and X events. Skill scores and other measures show that the present method is slightly less accurate at predicting M-X events than the NOAA method but substantially more accurate at predicting X events, which are important contributors to space weather.

  • Joint interpolation, motion and parameter estimation for image sequences with missing data

    This paper presents a new scheme for interpolation of missing data in image sequences, an important problem in many areas including archived motion picture film and digital video. A unified framework for image data modelling and motion estimation is adopted which is based on 3-dimensional autoregressive (3DAR) models with motion correction. A fully Bayesian methodology is implemented using the Gibbs Sampler, a method which allows for joint estimation with respect to all of the unknowns, including the motion field.

  • Learning Techniques for Context Diagnosis and Prediction in Cognitive Communications

    This chapter contains sections titled:IntroductionPredictionFuture ProblemsConclusionsReferences

  • Optimum Receiver

    This chapter provides information on how to detect a transmitted signal correctly in a receiver. The decision theory of wireless communication systems is developed to minimize the probability of error. The chapter introduces several decision theories and focuses on optimum receiver. The decision theory uses a priori information, a posterior information, and likelihood. The Bayesian decision rule is used when there is a priori information and likelihood. The chapter considers a simple system model with the AWGN channel and provides an illustration for the signal detector of a matched filter receiver. Finally, the chapter discusses coherent and noncoherent detection. The matched filter receiver can be used for the coherent detection of modulation schemes. The coherent detection needs expensive and complex carrier recovery circuit but has good performance of detection. The noncoherent detection does not require expensive and complex carrier recovery circuit but has poor performance of detection.

  • Unified image/bitstream analysis for the robust decoding of compressed video sequences for wireless networks

    This paper is concerned with the problem of error detection and correction of MPEG-4 video streams transmitted over lossy networks. The problem is first defined and some relevant detail of the MPEG-4 syntax is presented. The difficulties encountered in articulating this problem within a unified Bayesian framework are explored and two separate frameworks for dealing with error detection and error correction are then presented, along with some results. The paper concludes with some comments on the techniques used and pointers to future plans.

  • Improving Biochemical Named Entity Recognition Using PSO Classifier Selection and Bayesian Combination Methods

    Named Entity Recognition (NER) is a basic step for large number of consequent text mining tasks in the biochemical domain. Increasing the performance of such recognition systems is of high importance and always poses a challenge. In this study, a new community based decision making system is proposed which aims at increasing the efficiency of NER systems in the chemical/ drug name context. Particle Swarm Optimization (PSO) algorithm is chosen as the expert selection strategy along with the Bayesian combination method to merge the outputs of the selected classifiers as well as evaluate the fitness of the selected candidates. The proposed system performs in two steps. The first step focuses on creating various numbers of baseline classifiers for NER with different features sets using the Conditional Random Fields (CRFs). The second step involves the selection and efficient combination of the classifiers using PSO and Bayesisan combination. Two comprehensive corpora from BioCreative events, namely ChemDNER and CEMP, are used for the experiments conducted. Results show that the ensemble of classifiers selected by means of the proposed approach perform better than the single best classifier as well as ensembles formed using other popular selection/combination strategies for both corpora. Furthermore, the proposed method outperforms the best performing system at the Biocreative IV ChemDNER track by achieving an F-score of 87.95 percent.

  • Mobile Tracking in Mixed Line‐of‐Sight/Non‐Line‐of‐Sight Conditions: Algorithms and Theoretical Lower Bound

    This chapter investigates the problem of mobile tracking in mixed line‐of‐sight (LOS)/non‐line‐of‐sight (NLOS) conditions. It reviews the state‐of‐the‐art methods in this field. The chapter considers the problem in the Bayesian estimation framework and focus on two types of Bayesian filters: the Gaussian mixture filter (GMF) and the particle filter (PF). In the GMF section, the approximation property and the convergence results are summarized. Then, the modified extended Kalman filter (EKF) banks method, as one specific GMF, is described. In the PF section, generic PF is first introduced, and a more effective PF, approximated Rao‐Blackwellized particle filtering (ARBPF), is discussed in detail. The chapter closes with a discussion on the computation of a posterior Cramer‐Rao lower bound (CRLB) for this kind of mobile tracking problem. Simulation results are provided to compare the performance of the filtering algorithms and the posterior CRLB.

  • Machine Learning Applied to Cognitive Communications

    This chapter contains sections titled:IntroductionState of the ArtLearning TechniquesAdvantages and Disadvantages of Applying Machine Learning to Cognitive Radio NetworksConclusionsAcknowledgementReferences



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