Results 31 to 40 of about 22,384 (303)
3D CLASSIFICATION OF CROSSROADS FROM MULTIPLE AERIAL IMAGES USING MARKOV RANDOM FIELDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Markov Random Fields (MRF) approach to this problem, a probabilistic model that can be used to consider ...
S. Kosov +4 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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Concentration Inequalities for Functions of Gibbs Fields with Application to Diffraction and Random Gibbs Measures [PDF]
, 2002 We derive useful general concentration inequalities for functions of Gibbs fields in the uniqueness regime. We also consider expectations of random Gibbs measures that depend on an additional disorder field, and prove concentration w.r.t.
Külske, Christof +2 more
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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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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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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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Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble
Energies, 2021 Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant ...
Sanjoy Das +2 more
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Concentration inequalities for random fields via coupling [PDF]
, 2007 We present a new and simple approach to concentration inequalities in the context of dependent random processes and random fields. Our method is based on coupling and does not use information inequalities.
Chazottes, J. R. +8 more
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Steerable random fields for image restoralion [PDF]
, 2022 S.377-387Markov random fields (MRFs) are used to perform spatial (or spatiotemporal) regularization by imposing prior knowledge on the types of admissible images, depth maps, flow fields, and so on.
Roth, Stefan, Black, Michael
core
Bayesian 2D Deconvolution: A Model for Diffuse Ultrasound Scattering [PDF]
Modeling, Identification and Control, 2001 Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle, and significantly reduces the diagnostic value of the images. In order to remove both blur and speckle we
Oddvar Husby +4 more
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