Results 31 to 40 of about 256,437 (324)
Markov random topic fields [PDF]
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers on - ACL-IJCNLP '09, 2009 Most approaches to topic modeling assume an independence between documents that is frequently violated. We present an topic model that makes use of one or more user-specified graphs describing relationships between documents. These graph are encoded in the form of a Markov random field over topics and serve to encourage related documents to have ...
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An Efficient Gaussian Filter Based on Gaussian Symmetric Markov Random Field
IEEE Access, 2022 This article presents a new image denoising algorithm that uses Gaussian Symmetric Markov random fields based on maximum a posteriori estimation. First, an image denoising model based on Gaussian Symmetric Markov random fields is built, and the image ...
Fusong Xiong +3 more
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The Annals of Applied Probability, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kunsch, Hans +2 more
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Hierarchical non‐parametric Markov random field for image segmentation
IET Computer Vision, 2017 Markov random fields (MRFs) are prominent in modelling image to handle image processing problems. However, they confront the bottleneck of model selection in further improving the performance.
Xiangrong Wang, Jieyu Zhao
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Suggested Algorithm for Images Segmentation by Using Markov Random Field [PDF]
المجلة العراقية للعلوم الاحصائية, 2010 In this research, Markov Random Fields Models have been used in images processing, included algorithm suggested for image segmentation tha depends on the triple mixture normal distribution.
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Hidden Markov Random Field for Multi-Agent Optimal Decision in Top-Coal Caving
IEEE Access, 2020 Applying model-based learning for the optimal decision of the multi-agent system is not trivial due to the expensive price or even the impossibility of obtaining the ground truth for training the model of the complex environment.
Yi Yang +5 more
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Deep Gaussian Markov Random Fields
CoRR, 2020 Gaussian Markov random fields (GMRFs) are probabilistic graphical models widely used in spatial statistics and related fields to model dependencies over spatial structures. We establish a formal connection between GMRFs and convolutional neural networks (CNNs).
Per Sidén, Fredrik Lindsten
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Sensing capacity for Markov random fields [PDF]
Proceedings. International Symposium on Information Theory, 2005. ISIT 2005., 2005 This paper computes the sensing capacity of a sensor network, with sensors of limited range, sensing a two-dimensional Markov random field, by modeling the sensing operation as an encoder. Sensor observations are dependent across sensors, and the sensor network output across different states of the environment is neither identically nor independently ...
Yaron Rachlin +2 more
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Multiscale Representations of Markov Random Fields [PDF]
IEEE Transactions on Signal Processing, 1992 Summary: Recently, a framework for multiscale stochastic modeling was introduced based on coarse-to-fine scale-recursive dynamics defined on trees. This model class has some attractive characteristics which lead to extremely efficient, statistically optimal signal and image processing algorithms. We show that this model class is also quite rich.
Mark R. Luettgen +3 more
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SAR-based change detection using hypothesis testing and Markov random field modelling [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step
W. Cao, S. Martinis
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