Literatura científica selecionada sobre o tema "MRF, Markov Random Fields"
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Artigos de revistas sobre o assunto "MRF, Markov Random Fields"
Zhipeng, Jiang, e 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 novembro de 2015): 50–57. http://dx.doi.org/10.1515/cait-2015-0054.
Texto completo da fonteCai, Kuntai, Xiaoyu Lei, Jianxin Wei e Xiaokui Xiao. "Data synthesis via differentially private markov random fields". Proceedings of the VLDB Endowment 14, n.º 11 (julho de 2021): 2190–202. http://dx.doi.org/10.14778/3476249.3476272.
Texto completo da fonteLee, Sang Heon, Adel Malallah, Akhil Datta-Gupta e David Higdon. "Multiscale Data Integration Using Markov Random Fields". SPE Reservoir Evaluation & Engineering 5, n.º 01 (1 de fevereiro de 2002): 68–78. http://dx.doi.org/10.2118/76905-pa.
Texto completo da fonteYang, Xiangyu, Xuezhi Yang, Chunju Zhang e Jun Wang. "SAR Image Classification Using Markov Random Fields with Deep Learning". Remote Sensing 15, n.º 3 (20 de janeiro de 2023): 617. http://dx.doi.org/10.3390/rs15030617.
Texto completo da fonteJin, Di, Ziyang Liu, Weihao Li, Dongxiao He e 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 julho de 2019): 152–59. http://dx.doi.org/10.1609/aaai.v33i01.3301152.
Texto completo da fonteSmii, 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 completo da fonteKurella, 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 dezembro de 2023): 183–90. http://dx.doi.org/10.62760/iteecs.2.4.2023.68.
Texto completo da fonteShi, Haoran, Lixin Ji, Shuxin Liu, Kai Wang e Xinxin Hu. "Collusive anomalies detection based on collaborative markov random field". Intelligent Data Analysis 26, n.º 6 (12 de novembro de 2022): 1469–85. http://dx.doi.org/10.3233/ida-216287.
Texto completo da fonteKinge, Sanjaykumar, B. Sheela Rani e 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 completo da fonteQi, Anna, Lihua Yang e Chao Huang. "Convergence of Markovian stochastic approximation for Markov random fields with hidden variables". Stochastics and Dynamics 20, n.º 05 (18 de novembro de 2019): 2050029. http://dx.doi.org/10.1142/s021949372050029x.
Texto completo da fonteTeses / dissertações sobre o assunto "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 completo da fontePh.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 e 豊英 渡邉. "An HMM/MRF-based stochastic framework for robust vehicle tracking". IEEE, 2004. http://hdl.handle.net/2237/6743.
Texto completo da fonteKarci, 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 completo da fonteGasnier, 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 completo da fonteSpaceborne 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 completo da fonteKale, 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 completo da fonteWang, 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 completo da fonteStien, 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 completo da fonteWe 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 completo da fonteDrouin, 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 completo da fonteCe 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)
Livros sobre o assunto "MRF, Markov Random Fields"
Snell, J. Laurie (James Laurie), 1925-2011, ed. Markov random fields and their applications. [Providence]: AMS, 2003.
Encontre o texto completo da fonteRama, Chellappa, e Jain Anil K. 1948-, eds. Markov random fields: Theory and application. Boston: Academic Press, 1993.
Encontre o texto completo da fonteAndrew, Blake. Markov random fields for vision and image processing. Cambridge, Mass: MIT Press, 2011.
Encontre o texto completo da fonteAndrew, Blake, Pushmeet Kohli e Carsten Rother. Markov random fields for vision and image processing. Cambridge, Mass: MIT Press, 2011.
Encontre o texto completo da fonteLi, S. Z. Markov random field modeling in computer vision. New York: Springer-Verlag, 1995.
Encontre o texto completo da fonteXu, Jinbo, Sheng Wang e 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 completo da fonteImage textures and Gibbs random fields. Dordrecht: Kluwer Academic Publishers, 1999.
Encontre o texto completo da fonteHu, Dihe. Sui ji huan jing zhong de Ma'erkefu guo cheng =: Markov processes in random environments = Suiji huanjingzhong de Maerkefu guocheng. 8a ed. Beijing Shi: Gao deng jiao yu chu ban she, 2011.
Encontre o texto completo da fonteWinkler, 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 completo da fonteEvstigneev, I. V. Markov fields over countable partially ordered sets: Extrema and splitting. Providence, R.I: American Mathematical Society, 1994.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "MRF, Markov Random Fields"
Shekhar, Shashi, e Hui Xiong. "Markov Random Field (MRF)". In Encyclopedia of GIS, 637. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_758.
Texto completo da fonteLi, S. Z. "MRF Parameter Estimation". In 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 completo da fonteWang, Zifu, e Matthew B. Blaschko. "MRF-UNets: Searching UNet with Markov Random Fields". In 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 completo da fonteLi, S. Z. "Low Level MRF Models". In 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 completo da fonteLi, S. Z. "High Level MRF Models". In 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 completo da fonteNakamura, Rodrigo, Daniel Osaku, Alexandre Levada, Fabio Cappabianco, Alexandre Falcão e Joao Papa. "OPF-MRF: Optimum-Path Forest and Markov Random Fields for Contextual-Based Image Classification". In 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 completo da fonteSucar, Luis Enrique. "Markov Random Fields". In Probabilistic Graphical Models, 83–99. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_6.
Texto completo da fonteMitchell, H. B. "Markov Random Fields". In Image Fusion, 205–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11216-4_17.
Texto completo da fonteFieguth, Paul. "Markov Random Fields". In 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 completo da fonteGuttorp, Peter. "Markov random fields". In 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 completo da fonteTrabalhos de conferências sobre o assunto "MRF, Markov Random Fields"
Grover, Ishaan, Matthew Huggins, Cynthia Breazeal e Hae Won Park. "MRF-Chat: Improving Dialogue with Markov Random Fields". In 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 completo da fonteWu, Chi-hsin, e Peter C. Doerschuk. "Markov random fields as a priori information for image restoration". In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc2.
Texto completo da fonteGuo, Jinnian, Xinyu Wu, Tian Cao, Shiqi Yu e Yangsheng Xu. "Crowd density estimation via Markov Random Field (MRF)". In 2010 8th World Congress on Intelligent Control and Automation (WCICA 2010). IEEE, 2010. http://dx.doi.org/10.1109/wcica.2010.5554998.
Texto completo da fonteKusuma, T., e S. Jagannathn. "Review on Markov Random Field (Mrf) in Video Surveillance". In 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 completo da fonteZhang, Yue, e Arti Ramesh. "Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields". In 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 completo da fonteDong, Yiqi, Dongxiao He, Xiaobao Wang, Yawen Li, Xiaowen Su e Di Jin. "A Generalized Deep Markov Random Fields Framework for Fake News Detection". In 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 completo da fonteLei, Tianhu, e Jayaram K. Udupa. "A new look at Markov random field (MRF) model-based MR image analysis". In Medical Imaging, editado por J. Michael Fitzpatrick e Joseph M. Reinhardt. SPIE, 2005. http://dx.doi.org/10.1117/12.596251.
Texto completo da fonteHe, Dongxiao, Wenze Song, Di Jin, Zhiyong Feng e Yuxiao Huang. "An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph". In 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 completo da fonteSamy, Roger A., e Daniel Duclos. "Pyramidal Markov random field (MRF) models for optical flow estimation applied to target detection". In 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 completo da fonteLin, Jiawei, e Sei-Ichiro Kamata. "Using Markov Random Field (MRF) Hypergraph Transformer Method for Visual Question Answering (VQA) Application". In 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2023. http://dx.doi.org/10.1109/prai59366.2023.10332038.
Texto completo da fonteRelatórios de organizações sobre o assunto "MRF, Markov Random Fields"
Luettgen, M. R., W. C. Karl, A. S. Willsky e R. R. Tenney. Multiscale Representations of Markov Random Fields. Fort Belvoir, VA: Defense Technical Information Center, setembro de 1992. http://dx.doi.org/10.21236/ada459389.
Texto completo da fonteLuettgen, Mark R., William C. Karl, Alan S. Willsky e Robert R. Tenney. Multiscale Representations of Markov Random Fields. Fort Belvoir, VA: Defense Technical Information Center, junho de 1993. http://dx.doi.org/10.21236/ada459967.
Texto completo da fonteCevher, Volkan, Chinmay Hegde, Marco F. Duarte e Richard G. Baraniuk. Sparse Signal Recovery Using Markov Random Fields. Fort Belvoir, VA: Defense Technical Information Center, dezembro de 2009. http://dx.doi.org/10.21236/ada520187.
Texto completo da fonteMitter, Sanjoy K. Markov Random Fields, Stochastic Quantization and Image Analysis. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 1990. http://dx.doi.org/10.21236/ada459566.
Texto completo da fonteAnandkumar, Animashree, Lang Tong e Ananthram Swami. Detection of Gauss-Markov Random Fields with Nearest-Neighbor Dependency. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 2010. http://dx.doi.org/10.21236/ada536158.
Texto completo da fonteAdler, Robert J., e R. Epstein. A Central Limit Theorem for Markov Paths and Some Properties of Gaussian Random Fields. Fort Belvoir, VA: Defense Technical Information Center, fevereiro de 1986. http://dx.doi.org/10.21236/ada170258.
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