Literatura académica sobre el tema "MRF, Markov Random Fields"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "MRF, Markov Random Fields".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "MRF, Markov Random Fields"
Zhipeng, Jiang y Huang Chengwei. "High-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition". Cybernetics and Information Technologies 15, n.º 4 (1 de noviembre de 2015): 50–57. http://dx.doi.org/10.1515/cait-2015-0054.
Texto completoCai, Kuntai, Xiaoyu Lei, Jianxin Wei y Xiaokui Xiao. "Data synthesis via differentially private markov random fields". Proceedings of the VLDB Endowment 14, n.º 11 (julio de 2021): 2190–202. http://dx.doi.org/10.14778/3476249.3476272.
Texto completoLee, Sang Heon, Adel Malallah, Akhil Datta-Gupta y David Higdon. "Multiscale Data Integration Using Markov Random Fields". SPE Reservoir Evaluation & Engineering 5, n.º 01 (1 de febrero de 2002): 68–78. http://dx.doi.org/10.2118/76905-pa.
Texto completoYang, Xiangyu, Xuezhi Yang, Chunju Zhang y Jun Wang. "SAR Image Classification Using Markov Random Fields with Deep Learning". Remote Sensing 15, n.º 3 (20 de enero de 2023): 617. http://dx.doi.org/10.3390/rs15030617.
Texto completoJin, Di, Ziyang Liu, Weihao Li, Dongxiao He y Weixiong Zhang. "Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 152–59. http://dx.doi.org/10.1609/aaai.v33i01.3301152.
Texto completoSmii, Boubaker. "Markov random fields model and applications to image processing". AIMS Mathematics 7, n.º 3 (2022): 4459–71. http://dx.doi.org/10.3934/math.2022248.
Texto completoKurella, Pushpak. "Convolutional Neural Networks Grid Search Optimizer Based Brain Tumor Detection". International Transactions on Electrical Engineering and Computer Science 2, n.º 4 (30 de diciembre de 2023): 183–90. http://dx.doi.org/10.62760/iteecs.2.4.2023.68.
Texto completoShi, Haoran, Lixin Ji, Shuxin Liu, Kai Wang y Xinxin Hu. "Collusive anomalies detection based on collaborative markov random field". Intelligent Data Analysis 26, n.º 6 (12 de noviembre de 2022): 1469–85. http://dx.doi.org/10.3233/ida-216287.
Texto completoKinge, Sanjaykumar, B. Sheela Rani y Mukul Sutaone. "Restored texture segmentation using Markov random fields". Mathematical Biosciences and Engineering 20, n.º 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.
Texto completoQi, Anna, Lihua Yang y Chao Huang. "Convergence of Markovian stochastic approximation for Markov random fields with hidden variables". Stochastics and Dynamics 20, n.º 05 (18 de noviembre de 2019): 2050029. http://dx.doi.org/10.1142/s021949372050029x.
Texto completoTesis sobre el tema "MRF, Markov Random Fields"
Samuel, Kegan. "Gradient based MRF learning for image restoration and segmentation". Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5480.
Texto completoPh.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
Kato, Jien, Toyohide Watanabe, Sébastien Joga, Liu Ying, Hiroyuki Hase, ジェーン 加藤 y 豊英 渡邉. "An HMM/MRF-based stochastic framework for robust vehicle tracking". IEEE, 2004. http://hdl.handle.net/2237/6743.
Texto completoKarci, Mehmet Haydar. "Higher Order Levelable Mrf Energy Minimization Via Graph Cuts". Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609408/index.pdf.
Texto completoGasnier, Nicolas. "Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT002.
Texto completoSpaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it
Besbes, Ahmed. "Image segmentation using MRFs and statistical shape modeling". Phd thesis, Ecole Centrale Paris, 2010. http://tel.archives-ouvertes.fr/tel-00594246.
Texto completoKale, Hikmet Emre. "Segmentation Of Human Facial Muscles On Ct And Mri Data Using Level Set And Bayesian Methods". Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613352/index.pdf.
Texto completoWang, Siying. "Segmentation of magnetic resonance images for assessing neonatal brain maturation". Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:96db1546-16c1-4e37-9fd2-6431b385b516.
Texto completoStien, Marita. "Sequential Markov random fields and Markov mesh random fields for modelling of geological structures". Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9326.
Texto completoWe have been given a two-dimensional image of a geological structure. This structure is used to construct a three-dimensional statistical model, to be used as prior knowledge in the analysis of seismic data. We consider two classes of discrete lattice models for which efficient simulation is possible; sequential Markov random field (sMRF) and Markov mesh random field (MMRF). We first explore models from these two classes in two dimensions, using the maximum likelihood estimator (MLE). The results indicate that a larger neighbourhood should be considered for all the models. We also develop a second estimator, which is designed to match the model with the observation with respect to a set of specified functions. This estimator is only considered for the sMRF model, since that model proved to be flexible enough to give satisfying results. Due to time limitation of this thesis, we could not wait for the optimization of the estimator to converge. Thus, we can not evaluate this estimator. Finally, we extract useful information from the two-dimensional models and specify a sMRF model in three dimensions. Parameter estimation for this model needs approximative techniques, since we only have given observations in two dimensions. Such techniques have not been investigated in this report, however, we have adjusted the parameters manually and observed that the model is very flexible and might give very satisfying results.
Austad, Haakon Michael. "Approximations of Binary Markov Random Fields". Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14922.
Texto completoDrouin, Simon. "Digital rotoscoping using Markov random fields". Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32535.
Texto completoCe mémoire présente un modèle statistique ainsi que son implantation dans un programme de rotoscopie qui peut être utilisé pour la production de films d'animation. Le problème de la segmentation assistée de scènes video contenant un avant-plan et un arrière-plan distincts, un sous-ensemble du problème plus général que constitue la rotoscopie, est utilisé pour analyser les propriétés du modèle statistique et de son implantation. Le modèle statistique utilisé est construit à partir d'un découpage de paires d'images d'entraînement composées d'un cadre de la séquence video à segmenter et d'une image binaire qui défini la segmentation associée. La segmentation de chaque cadre de la sequence est obtenue en collant, pour chaque portion d'image, la portion d'image la plus similaire de l'ensemble d'entraînement. Un mécanisme inspiré de la "propagation de conviction"(belief propagation) est utilisé pour assurer la cohérence entre les portions de l'image de sortie qui sont voisines. L'algorithme est appliqué à plusieurs niveaux d'échelle afin de considérer la dépendance statistique de plus longue portée qui existe entre les pixels d'une image. Une métrique est définie pour mesurer la performance de la segmentation automatique. Les résultats de la segmentation sont analysés à l'aide d'une série de séquences vidéo qui ont préalablement été segmentées manuellement. Une nouvelle technique est également présentée pour permettre au logiciel de segmentation de choisir automatiquement l'ensemble d'entraînement optimal. Une segmentation grossière est d'abord obtenue en ulitisant le plus petit ensemble d'entraînement possible (1 cadre)
Libros sobre el tema "MRF, Markov Random Fields"
Snell, J. Laurie (James Laurie), 1925-2011, ed. Markov random fields and their applications. [Providence]: AMS, 2003.
Buscar texto completoRama, Chellappa y Jain Anil K. 1948-, eds. Markov random fields: Theory and application. Boston: Academic Press, 1993.
Buscar texto completoMarkov random fields for vision and image processing. Cambridge, Mass: MIT Press, 2011.
Buscar texto completoAndrew, Blake, Pushmeet Kohli y Carsten Rother. Markov random fields for vision and image processing. Cambridge, Mass: MIT Press, 2011.
Buscar texto completoLi, S. Z. Markov random field modeling in computer vision. New York: Springer-Verlag, 1995.
Buscar texto completoXu, Jinbo, Sheng Wang y Jianzhu Ma. Protein Homology Detection Through Alignment of Markov Random Fields. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14914-1.
Texto completoImage textures and Gibbs random fields. Dordrecht: Kluwer Academic Publishers, 1999.
Buscar texto completoSui ji huan jing zhong de Ma'erkefu guo cheng: Markov processes in random environments = Suiji huanjingzhong de Maerkefu guocheng. Beijing Shi: Gao deng jiao yu chu ban she, 2011.
Buscar texto completoWinkler, Gerhard. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6.
Texto completo1937-, Greenwood P. E., ed. Markov fields over countable partially ordered sets: Extrema and splitting. Providence, R.I: American Mathematical Society, 1994.
Buscar texto completoCapítulos de libros sobre el tema "MRF, Markov Random Fields"
Shekhar, Shashi y Hui Xiong. "Markov Random Field (MRF)". En Encyclopedia of GIS, 637. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_758.
Texto completoLi, S. Z. "MRF Parameter Estimation". En Markov Random Field Modeling in Computer Vision, 131–56. Tokyo: Springer Japan, 1995. http://dx.doi.org/10.1007/978-4-431-66933-3_6.
Texto completoWang, Zifu y Matthew B. Blaschko. "MRF-UNets: Searching UNet with Markov Random Fields". En Machine Learning and Knowledge Discovery in Databases, 599–614. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_36.
Texto completoLi, S. Z. "Low Level MRF Models". En Markov Random Field Modeling in Computer Vision, 37–61. Tokyo: Springer Japan, 1995. http://dx.doi.org/10.1007/978-4-431-66933-3_2.
Texto completoLi, S. Z. "High Level MRF Models". En Markov Random Field Modeling in Computer Vision, 101–30. Tokyo: Springer Japan, 1995. http://dx.doi.org/10.1007/978-4-431-66933-3_5.
Texto completoNakamura, Rodrigo, Daniel Osaku, Alexandre Levada, Fabio Cappabianco, Alexandre Falcão y Joao Papa. "OPF-MRF: Optimum-Path Forest and Markov Random Fields for Contextual-Based Image Classification". En Computer Analysis of Images and Patterns, 233–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40246-3_29.
Texto completoSucar, Luis Enrique. "Markov Random Fields". En Probabilistic Graphical Models, 83–99. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_6.
Texto completoMitchell, H. B. "Markov Random Fields". En Image Fusion, 205–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11216-4_17.
Texto completoFieguth, Paul. "Markov Random Fields". En Statistical Image Processing and Multidimensional Modeling, 179–214. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7294-1_6.
Texto completoGuttorp, Peter. "Markov random fields". En Stochastic Modeling of Scientific Data, 189–226. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4449-8_4.
Texto completoActas de conferencias sobre el tema "MRF, Markov Random Fields"
Grover, Ishaan, Matthew Huggins, Cynthia Breazeal y Hae Won Park. "MRF-Chat: Improving Dialogue with Markov Random Fields". En Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.403.
Texto completoWu, Chi-hsin y Peter C. Doerschuk. "Markov random fields as a priori information for image restoration". En Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc2.
Texto completoGuo, Jinnian, Xinyu Wu, Tian Cao, Shiqi Yu y Yangsheng Xu. "Crowd density estimation via Markov Random Field (MRF)". En 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5554998.
Texto completoKusuma, T. y S. Jagannathn. "Review on Markov Random Field (Mrf) in Video Surveillance". En Third International Conference on Current Trends in Engineering Science and Technology ICCTEST-2017. Grenze Scientific Society, 2017. http://dx.doi.org/10.21647/icctest/2017/49071.
Texto completoZhang, Yue y Arti Ramesh. "Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/838.
Texto completoDong, Yiqi, Dongxiao He, Xiaobao Wang, Yawen Li, Xiaowen Su y Di Jin. "A Generalized Deep Markov Random Fields Framework for Fake News Detection". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/529.
Texto completoLei, Tianhu y Jayaram K. Udupa. "A new look at Markov random field (MRF) model-based MR image analysis". En Medical Imaging, editado por J. Michael Fitzpatrick y Joseph M. Reinhardt. SPIE, 2005. http://dx.doi.org/10.1117/12.596251.
Texto completoHe, Dongxiao, Wenze Song, Di Jin, Zhiyong Feng y Yuxiao Huang. "An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/794.
Texto completoSamy, Roger A. y Daniel Duclos. "Pyramidal Markov random field (MRF) models for optical flow estimation applied to target detection". En SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, editado por Nagaraj Nandhakumar. SPIE, 1994. http://dx.doi.org/10.1117/12.179033.
Texto completoLin, Jiawei y Sei-Ichiro Kamata. "Using Markov Random Field (MRF) Hypergraph Transformer Method for Visual Question Answering (VQA) Application". En 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2023. http://dx.doi.org/10.1109/prai59366.2023.10332038.
Texto completoInformes sobre el tema "MRF, Markov Random Fields"
Luettgen, M. R., W. C. Karl, A. S. Willsky y R. R. Tenney. Multiscale Representations of Markov Random Fields. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1992. http://dx.doi.org/10.21236/ada459389.
Texto completoLuettgen, Mark R., William C. Karl, Alan S. Willsky y Robert R. Tenney. Multiscale Representations of Markov Random Fields. Fort Belvoir, VA: Defense Technical Information Center, junio de 1993. http://dx.doi.org/10.21236/ada459967.
Texto completoCevher, Volkan, Chinmay Hegde, Marco F. Duarte y Richard G. Baraniuk. Sparse Signal Recovery Using Markov Random Fields. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 2009. http://dx.doi.org/10.21236/ada520187.
Texto completoMitter, Sanjoy K. Markov Random Fields, Stochastic Quantization and Image Analysis. Fort Belvoir, VA: Defense Technical Information Center, enero de 1990. http://dx.doi.org/10.21236/ada459566.
Texto completoAnandkumar, Animashree, Lang Tong y Ananthram Swami. Detection of Gauss-Markov Random Fields with Nearest-Neighbor Dependency. Fort Belvoir, VA: Defense Technical Information Center, enero de 2010. http://dx.doi.org/10.21236/ada536158.
Texto completoAdler, Robert J. y R. Epstein. A Central Limit Theorem for Markov Paths and Some Properties of Gaussian Random Fields. Fort Belvoir, VA: Defense Technical Information Center, febrero de 1986. http://dx.doi.org/10.21236/ada170258.
Texto completo