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A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures

Christophoros Nikou, Aristidis C Likas, Nikolaos P Galatsanos
2010 IEEE Transactions on Image Processing  
This model exploits the Dirichlet compound multinomial (DCM) probability density to model the mixing proportions (i.e., the probabilities of class labels) and a Gauss-Markov random field (MRF) on the Dirichlet  ...  Index Terms-Bayesian model, Dirichlet compound multinomial distribution, Gauss-Markov random field prior, Gaussian mixture, image segmentation, spatially varying finite mixture model.  ...  Furthermore, spatial smoothness is imposed by assuming a Gauss-Markov random field (GMRF) prior for the parameters of the Dirichlet.  ... 
doi:10.1109/tip.2010.2047903 pmid:20378472 fatcat:gid2luccnfeghof7ln42j54kee

Active self-calibration of robotic eyes and hand-eye relationships with model identification

Guo-Qing Wei, K. Arbter, G. Hirzinger
1998 IEEE Transactions on Robotics and Automation  
The approach is fully autonomous, in that no initial guesses of the unknown parameters are to be provided from the outside by humans for the solution of a set of nonlinear equations.  ...  Through tracking a set of world points of unknown coordinates during robot motion, the internal parameters of the cameras (including distortions), the mounting parameters as well as the coordinates of  ...  Acknowledgement The authors are very grateful of the anonymous reviewers for their valuable comments.  ... 
doi:10.1109/70.660864 fatcat:wydrfvpnvrcetdiuqapo55xbuy

Adaptive restoration of speckled SAR images

J.M.B. Dias, T.A.M. Silva, J.M.N. Leitao
1998 IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174)  
a compound Gauss-Markov random field which enforces smoothness on homogeneous regions while preserving discontinuities between neighboring regions.  ...  The approach is Bayesian: the observed image is assumed to be a realization of a random field built upon the physical mechanism of image generation; the backscattering coefficient image is modelled by  ...  • the set of backscattering coefficients associated to the image pixels is assumed to be piecewise smooth, and modelled as a random field with a compound Gauss-Markov random field (CGMRF) prior [5] ,  ... 
doi:10.1109/igarss.1998.702784 fatcat:7ly6yd44knglvl7624m232grg4

Likelihood-based and Bayesian methods for Tweedie compound Poisson linear mixed models

Yanwei Zhang
2012 Statistics and computing  
In addition, we derive the corresponding Markov Chain Monte Carlo algorithm for a Bayesian formulation of the mixed model.  ...  This paper presents several likelihood-based inferential methods for the Tweedie compound Poisson mixed model that enable estimation of the variance function from the data.  ...  Markov Chain Monte Carlo. To implement the MCMC procedure, we formulate the model in a Bayesian setting. In particular, prior distributions for all parameters must be specified.  ... 
doi:10.1007/s11222-012-9343-7 fatcat:nzuolqu2ajgorptzknkn6zyrti

Identifying and modeling motion primitives for the hydromedusaeSarsia tubulosaandAequorea victoria

Isaac Sledge, Michael Krieg, Doug Lipinski, Kamran Mohseni
2015 Bioinspiration & Biomimetics  
By adopting a nonparametric, Bayesian formalism for learning and simplifying these pattern generators, we arrive at a purely data-driven model to automatically identify breakpoints in the movement sequences  ...  We apply this model to swimming sequences from two hydromedusa.  ...  Acknowledgments The work of the authors was funded by the US Office of Naval Research.  ... 
doi:10.1088/1748-3190/10/6/066001 pmid:26495992 fatcat:6ute5vnw2bg55ldhhau4fbffq4

SMART: the stochastic model checking analyzer for reliability and timing

G. Ciardo, A.S. Miner
2004 First International Conference on the Quantitative Evaluation of Systems, 2004. QEST 2004. Proceedings.  
In addition, certain classes of non-Markov models can be solved numerically. For more details, see G. Ciardo et. al., "Logical and stochastic modeling with SMART", in Proc.  ...  algorithms for the analysis of discrete-state systems.  ...  Models interact: a measure computed in a model can be an input parameter for another model.  ... 
doi:10.1109/qest.2004.1348056 dblp:conf/qest/CiardoM04 fatcat:2vhi7qag7bemnojgak6yzma73m

Bayesian system identification of dynamical systems using highly informative training data

P.L. Green, E.J. Cross, K. Worden
2015 Mechanical systems and signal processing  
To that end, using concepts from information theory, expressions are derived which allow one to approximate the effect that a set of training data will have on parameter uncertainty as well as the plausibility  ...  The usefulness of this concept is then demonstrated through the system identification of several dynamical systems using both physics-based and emulator models.  ...  Acknowledgments This paper was funded by an EPSRC fellowship and the EPSRC Programme Grant 'Engineering Nonlinearity' EP/K003836/1.  ... 
doi:10.1016/j.ymssp.2014.10.003 fatcat:l6r5j7e5ujaslajcthbxlxjwri

A Model-based Tightly Coupled Architecture for Low-Cost Unmanned Aerial Vehicles for Real-Time Applications

Hery A. Mwenegoha, Terry Moore, James Pinchin, Mark Jabbal
2020 IEEE Access  
ACKNOWLEDGMENT The authors would like to thank UoN colleagues for their invaluable support. We would also like to thank the Hucknall MAC administration for coordinating the flight tests.  ...  The INNOVATIVE programme is partially funded by the Marie Curie Initial Training Networks (ITN) action (project number 665468) and partially by the Institute for Aerospace Technology (IAT) at the University  ...  A second-order Gauss-Markov process is used to model the receiver clock bias and drift.  ... 
doi:10.1109/access.2020.3038530 fatcat:gggocdrk5befjcvegniylbqz3i

Model-based despeckling and information extraction from SAR images

M. Walessa, M. Datcu
2000 IEEE Transactions on Geoscience and Remote Sensing  
The proposed approach uses a Gauss Markov random field (GMRF) model for textured areas and allows an adaptive neighborhood system for edge preservation between uniform areas.  ...  Additionally, the estimated model parameters can be used for further image interpretation methods.  ...  ACKNOWLEDGMENT The authors would like to thank F. Faille for her help and for her suggestions concerning the manuscript.  ... 
doi:10.1109/36.868883 fatcat:4ssfvgznkrelllhkuwotqdn5me

Accelerating Markov Chain Monte Carlo with Active Subspaces

Paul G. Constantine, Carson Kent, Tan Bui-Thanh
2016 SIAM Journal on Scientific Computing  
The Markov chain Monte Carlo (MCMC) method is the computational workhorse for Bayesian inverse problems.  ...  Active subspaces are part of an emerging set of tools for subspace-based dimension reduction.  ...  We thank Luis Tenorio and Aaron Porter at Colorado School of Mines and Youssef Marzouk and Tiangang Cui at MIT for their helpful comments.  ... 
doi:10.1137/15m1042127 fatcat:oawtevrhtbblxcopc336c3ucwm

Segmentation And Recognition Of Tabla Strokes

Parag Chordia
2005 Zenodo  
ACKNOWLEDGEMENTS My sincere thanks to Olivier Gillet and Gael Richard for providing their data to me.  ...  Multivariate Gaussian (mv gauss) The feature vector was modeled as being drawn from a mv gauss distribution.  ...  By performing language modeling, using a hidden Markov model (hmm), they were able to attain a 93.6% recognition rate.  ... 
doi:10.5281/zenodo.1416000 fatcat:tnpnv2wlbbceffonf5jzipnbau

Bias modeling and estimation for GMTI applications

K. Kastella, B. Yeary, T. Zadra, R. Brouillard, E. Frangione
2000 Proceedings of the Third International Conference on Information Fusion  
This paper describes an approach to sensor bias modeling and estimation for ground target tracking applications using multiple airborne Ground Moving Target Indicator (GMTI) radar sensors.  ...  For airborne sensors, slowly varying platform location, heading and velocity errors lead to time-dependent measurement biases.  ...  This work was supported by the Defense Advance Projects Agency (DARPA) under the Affordable Moving Surface Target Engagement (AMSTE) program.  ... 
doi:10.1109/ific.2000.862677 fatcat:6qwvbxoienagndavcqu62rkmce

Importance Sampling Kalman Filter for Image Estimation

G. R. K. S. Subrahmanyam, A. N. Rajagopalan, R. Aravind
2007 IEEE Signal Processing Letters  
Index Terms-Discontinuity adaptive prior, image estimation, importance sampling, Kalman filter, Markov random fields, non-Gaussian image modelling, state space models.  ...  A generalized methodology is proposed for specifying state-dynamics using the conditional density of the state given its neighbors, without explicitly defining the state equation.  ...  In [8] , a compound Gauss Markov random field (GMRF) model is proposed for the image and its maximum a posteriori probability (MAP) estimate is obtained by simulated annealing.  ... 
doi:10.1109/lsp.2006.891345 fatcat:6ilg5rbauvbuvgdz3ytmzwuuw4

Divide and Conquer: Recursive Likelihood Function Integration for Hidden Markov Models with Continuous Latent Variables

Gregor Reich
2017 Social Science Research Network  
This paper develops a method to efficiently estimate hidden Markov models with continuous latent variables using maximum likelihood estimation.  ...  the algorithm's ability to recover the parameters in an extensive Monte Carlo study with simulated datasets.  ...  hidden Markov models (Section 3.4).  ... 
doi:10.2139/ssrn.2794884 fatcat:anfarwnhbbaffa2hakioqdfjsq

A Multi-Objective Mathematical Model, a Markov Chains and a Deep Learning approaches for mobility prediction to reduce Energy Consumption and Delay in Mobile Wireless Sensor Networks

German A. Montoya, Carlos Lozano-Garzon, Yezid Donoso
2021 IEEE Access  
For this reason, we propose a multiobjective mathematical optimization model for finding the optimal communication path between a source node and a sink (base station) considering hard scenarios where  ...  The performance of our prediction algorithms (Markov Chains and Deep Learning approaches) is evaluated against the mathematical model to determine how good it is.  ...  parameters of the mathematical model.  ... 
doi:10.1109/access.2021.3124737 fatcat:mbrg37d7i5fxzgmyitdgx26gl4
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