Academic literature on the topic 'Spatio-temporal random fields'
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Journal articles on the topic "Spatio-temporal random fields"
Descombes, X., F. Kruggel, and D. Y. Von Cramon. "Spatio-temporal fMRI analysis using Markov random fields." IEEE Transactions on Medical Imaging 17, no. 6 (1998): 1028–39. http://dx.doi.org/10.1109/42.746636.
Full textDe Iaco, S., and D. Posa. "Predicting spatio-temporal random fields: Some computational aspects." Computers & Geosciences 41 (April 2012): 12–24. http://dx.doi.org/10.1016/j.cageo.2011.11.014.
Full textPiatkowski, Nico, Sangkyun Lee, and Katharina Morik. "Spatio-temporal random fields: compressible representation and distributed estimation." Machine Learning 93, no. 1 (July 25, 2013): 115–39. http://dx.doi.org/10.1007/s10994-013-5399-7.
Full textIp, Ryan H. L., and W. K. Li. "Matérn cross-covariance functions for bivariate spatio-temporal random fields." Spatial Statistics 17 (August 2016): 22–37. http://dx.doi.org/10.1016/j.spasta.2016.04.004.
Full textSalvaña, Mary Lai O., and Marc G. Genton. "Nonstationary cross-covariance functions for multivariate spatio-temporal random fields." Spatial Statistics 37 (June 2020): 100411. http://dx.doi.org/10.1016/j.spasta.2020.100411.
Full textFontanella, L., L. Ippoliti, R. J. Martin, and S. Trivisonno. "Interpolation of spatial and spatio-temporal Gaussian fields using Gaussian Markov random fields." Advances in Data Analysis and Classification 2, no. 1 (April 2008): 63–79. http://dx.doi.org/10.1007/s11634-008-0019-2.
Full textDas, Sonjoy, Roger Ghanem, and Steven Finette. "Polynomial chaos representation of spatio-temporal random fields from experimental measurements." Journal of Computational Physics 228, no. 23 (December 2009): 8726–51. http://dx.doi.org/10.1016/j.jcp.2009.08.025.
Full textWestern, Luke M., Zhe Sha, Matthew Rigby, Anita L. Ganesan, Alistair J. Manning, Kieran M. Stanley, Simon J. O'Doherty, Dickon Young, and Jonathan Rougier. "Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields." Geoscientific Model Development 13, no. 4 (April 28, 2020): 2095–107. http://dx.doi.org/10.5194/gmd-13-2095-2020.
Full textJadaliha, Mahdi, Jinho Jeong, Yunfei Xu, Jongeun Choi, and Junghoon Kim. "Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields." Sensors 18, no. 9 (August 30, 2018): 2866. http://dx.doi.org/10.3390/s18092866.
Full textGhosh, Debraj, and Anup Suryawanshi. "Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition." Communications in Computational Physics 16, no. 1 (July 2014): 75–95. http://dx.doi.org/10.4208/cicp.201112.191113a.
Full textDissertations / Theses on the topic "Spatio-temporal random fields"
Dai, Luyan. "Topics in objective bayesian methodology and spatio-temporal models." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6084.
Full textThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 4, 2009) Vita. Includes bibliographical references.
Jiang, Huijing. "Statistical computation and inference for functional data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37087.
Full textSánchez, Jiménez Oscar. "On the stochastic response of rotor-blade models with Floquet modal theory : applications to time-dependent reliability of tidal turbine blades." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR39.
Full textThe response of a deterministic rotating mechanical system under stochastic excitation is studied. A mechanical-probabilistic model is developed to combine the relevant characteristics of both aspects of the study: the behavior of this non-standard class of mechanical system and the random properties of correlated stochastic fields describing load processes. The results are applied to a reliability analysis of a reduced order model of a tidal turbine. Semi-analytic and empirical ( in the Monte-Carlo simulation sense) results are obtained, compared and contrasted. The results are framed with respect to the existing literature, and it is found that they provide an innovative treatment of the problem, in terms of the dynamical choices reflected in the model, in the presentation and interpretation of the modal aspects of the system, and in the type of stochastic inputs considered. We develop a dynamical model describing a broad class of mechanical system that models a rotor-blade structure. The model is informed by careful consideration of previous results, with the aim of constructing a reduced model that captures essential characteristics of the vibratory behavior of the structure. Lagrangian formalism is utilized to obtain the equations of motion. The presence of elastic components, which are discretized in a modal way, results in a system of ordinary differential equations with periodic coefficients. The Floquet theory of Linear time-periodic systems is applied on the deterministic dynamical model to synthesize an extension of traditional modal analysis to systems with periodic coefficients. The response of the system is formulated in terms of Floquet exponents and the associated Floquet periodic eigenvectors, from which the Floquet State Transition Matrix can be obtained. Several methods are applied to the modal study of the system, and a new time-frequency approach is proposed based on PGHW wavelets and its associated scalogram. Using an innovative notation to describe probabilistic moments of stochastic quantities, a moment propagation scheme is presented and exploited. The advantages of the treatment, particularly in the case of spatio-temporal stochastic fields, is in its applicability and systematic structure of development. This moment propagation strategy is coupled with a maximum entropy formulation to reconstruct the probability density function of the response and obtain important descriptors of the response, such as the Extreme Value Distribution. The moment propagation technique presented is applied to nonstationary processes. The results from Modal Floquet theory are integrated into this analysis. The problem of crossings of a certain threshold is considered for this type of nonstationary stochastic process. Their response is shown to yield a time-dependent reliability problem, which is resolved using the estimated EVD and then by numerical simulation
Zhuang, Lili. "Bayesian Dynamical Modeling of Count Data." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315949027.
Full textLow, Choy Samantha Jane. "Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour." Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/37098/12/Samantha%20Low%20Choy%20Thesis.pdf.
Full textDeregnaucourt, Thomas. "Prédiction spatio-temporelle de surfaces issues de l'imagerie en utilisant des processus stochastiques." Thesis, Université Clermont Auvergne (2017-2020), 2019. http://www.theses.fr/2019CLFAC088.
Full textThe prediction of a surface is now an important problem due to its use in multiple domains, such as computer vision, the simulation of avatars for cinematography or video games, etc. Since a surface can be static or dynamic, i.e. evolving with time, this problem can be separated in two classes: a spatial prediction problem and a spatio-temporal one. In order to propose a new approach for each of these problems, this thesis works have been separated in two parts.First of all, we have searched to predict a static surface, which is supposed cylindrical, knowing it partially from curves. The proposed approach consisted in deforming a cylinder on the known curves in order to reconstruct the surface of interest. First, a correspondence between known curves and the cylinder is generated with the help of shape analysis tools. Once this step done, an interpolation of the deformation field, which is supposed Gaussian, have been estimated using maximum likelihood and Bayesian inference. This methodology has then been applied to real data from two domains of imaging: medical imaging and infography. The obtained results show that the proposed approach exceeds the existing methods in the literature, with better results using Bayesian inference.In a second hand, we have been interested in the spatio-temporal prediction of dynamic surfaces. The objective was to predict a dynamic surface based on its initial surface. Since the prediction needs to learn on known observations, we first have developed a spatio-temporal surface analysis tool. This analysis is based on shape analysis tools, and allows a better learning. Once this preliminary step done, we have estimated the temporal deformation of the dynamic surface of interest. More precisely, an adaptation, with is usable on the space of surfaces, of usual statistical estimators has been used. Using this estimated deformation on the initial surface, an estimation of the dynamic surface has been created. This process has then been applied for predicting 4D expressions of faces, which allow us to generate visually convincing expressions
Salvaña, Mary Lai O. "Lagrangian Spatio-Temporal Covariance Functions for Multivariate Nonstationary Random Fields." Thesis, 2021. http://hdl.handle.net/10754/669674.
Full textQadir, Ghulam A. "Flexible Covariance Models for Spatio-Temporal and Multivariate Spatial Random Fields." Thesis, 2021. http://hdl.handle.net/10754/669402.
Full textBooks on the topic "Spatio-temporal random fields"
Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
Full textBook chapters on the topic "Spatio-temporal random fields"
Allard, Denis, Xavier Emery, Céline Lacaux, and Christian Lantuéjoul. "Simulation of Stationary Gaussian Random Fields with a Gneiting Spatio-Temporal Covariance." In Springer Proceedings in Earth and Environmental Sciences, 43–49. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_4.
Full textKamijo, Shunsuke, Katsushi Ikeuchi, and Masao Sakauchi. "Segmentations of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model." In Lecture Notes in Computer Science, 298–313. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44745-8_20.
Full textXu, Wanru, Zhenjiang Miao, Jian Zhang, and Yi Tian. "Learning Spatio-Temporal Features for Action Recognition with Modified Hidden Conditional Random Field." In Computer Vision - ECCV 2014 Workshops, 786–801. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16178-5_55.
Full textConference papers on the topic "Spatio-temporal random fields"
Chandra, Siddhartha, Camille Couprie, and Iasonas Kokkinos. "Deep Spatio-Temporal Random Fields for Efficient Video Segmentation." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00929.
Full textRoscher, Ribana, Bernd Uebbing, and Jurgen Kusche. "Spatio-temporal altimeter waveform retracking via sparse representation and conditional random fields." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7325996.
Full textHasani, Behzad, and Mohammad H. Mahoor. "Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields." In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). IEEE, 2017. http://dx.doi.org/10.1109/fg.2017.99.
Full textMitra, Adway. "Identifying coherent anomalies in multi-scale spatio-temporal data using Markov random fields." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258333.
Full textShafiee, M. J., Z. Azimifar, A. Wong, and P. Fieguth. "A Novel Hierarchical Model-Based Frame Rate Up-Conversion via Spatio-temporal Conditional Random Fields." In 2011 IEEE International Symposium on Multimedia (ISM). IEEE, 2011. http://dx.doi.org/10.1109/ism.2011.44.
Full textGeorgiou, Ioannis T. "Pattern Characterization in Acceleration Vector Fields Developed in Complex Beam Structures Subject to an Excitation Protocol by Impulsive Forces." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70504.
Full textMoloney, J. V., P. Ru, R. Indik, S. W. Koch, and E. Wright. "Space-Time Dynamics of Semiconductor Lasers: Many-Body Theory and Phenomenological Models." In Nonlinear Dynamics in Optical Systems. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nldos.1992.thb4.
Full textMadhag, Aqeel, and Jongeun Choi. "Distributed Navigation Strategy of Mobile Sensor Networks With Probabilistic Wireless Communication Links." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9964.
Full textMoloney, J. V., P. Ru, R. Indik, S. W. Koch, and E. Wright. "Many-Body Semiconductor Laser Theory: A comparison with Phenomenological Theories." In Nonlinear Optics. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nlo.1992.we3.
Full textChen, Jia, and Chi-Keung Tang. "Spatio-Temporal Markov Random Field for Video Denoising." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383261.
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