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580
Stereo matching using belief propagation
, 2003
"... In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, ..."
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Cited by 350 (4 self)
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In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity
Improved prediction of signal peptides  SignalP 3.0
 J. MOL. BIOL.
, 2004
"... We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cle ..."
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Cited by 654 (7 self)
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We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea
Being Bayesian about network structure
 Machine Learning
, 2000
"... Abstract. In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent dependency structures using Bayesian network models. To analyze a given data set, Bayesian model sel ..."
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Cited by 299 (3 self)
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probability of all models that contain it. In this paper, we propose a new approach for this task. We first show how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables. This allows us to compute, for a given order, both
Dense photometric stereo: A markov random field approach
, 2006
"... We address the problem of robust normal reconstruction by dense photometric stereo, in the presence of complex geometry, shadows, highlight, transparencies, variable attenuation in light intensities, and inaccurate estimation in light directions. The input is a dense set of noisy photometric images, ..."
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Cited by 13 (3 self)
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, conveniently captured by using a very simple setup consisting of a digital video camera, a reflective mirror sphere, and a handheld spotlight. We formulate the dense photometric stereo problem as a Markov network, and investigate two important inference algorithms for Markov Random Fields (MRFs) – graph cuts
Manhattanworld Stereo
"... Multiview stereo (MVS) algorithms now produce reconstructions that rival laser range scanner accuracy. However, stereo algorithms require textured surfaces, and therefore work poorly for many architectural scenes (e.g., building interiors with textureless, painted walls). This paper presents a nove ..."
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Cited by 73 (7 self)
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Multiview stereo (MVS) algorithms now produce reconstructions that rival laser range scanner accuracy. However, stereo algorithms require textured surfaces, and therefore work poorly for many architectural scenes (e.g., building interiors with textureless, painted walls). This paper presents a
Hybrid markov logic networks.
 In Proceedings of the TwentyThird AAAI Conference on Artificial Intelligence (AAAI),
, 2008
"... Abstract Markov logic networks (MLNs) combine firstorder logic and Markov networks, allowing us to handle the complexity and uncertainty of realworld problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most realworld applications also contai ..."
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Cited by 44 (1 self)
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Abstract Markov logic networks (MLNs) combine firstorder logic and Markov networks, allowing us to handle the complexity and uncertainty of realworld problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most realworld applications also
Bottomup learning of Markov logic network structure
 In Proceedings of the TwentyFourth International Conference on Machine Learning
, 2007
"... Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes firstorder logic and Markov networks. The current stateoftheart algorithm for learning MLN structure follows a topdown paradigm where many potential candidate structures a ..."
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Cited by 68 (7 self)
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Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes firstorder logic and Markov networks. The current stateoftheart algorithm for learning MLN structure follows a topdown paradigm where many potential candidate structures
Link prediction in relational data
 in Neural Information Processing Systems
, 2003
"... Many realworld domains are relational in nature, consisting of a set of objects related to each other in complex ways. This paper focuses on predicting the existence and the type of links between entities in such domains. We apply the relational Markov network framework of Taskar et al. to define a ..."
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Cited by 156 (1 self)
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Many realworld domains are relational in nature, consisting of a set of objects related to each other in complex ways. This paper focuses on predicting the existence and the type of links between entities in such domains. We apply the relational Markov network framework of Taskar et al. to define
Piecewise Planar Stereo for Imagebased Rendering
"... We present a novel multiview stereo method designed for imagebased rendering that generates piecewise planar depth maps from an unordered collection of photographs. First a discrete set of 3D plane candidates are computed based on a sparse point cloud of the scene (recovered by structure from moti ..."
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Cited by 54 (7 self)
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motion) and sparse 3D line segments reconstructed from multiple views. Next, evidence is accumulated for each plane using 3D point and line incidence and photoconsistency cues. Finally, a piecewise planar depth map is recovered for each image by solving a multilabel Markov Random Field (MRF
Relaxing symmetric multiple windows stereo using markov random fields
 IN M.FIGUREIDO, J.ZERUBIA. (ED.), ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, NO. 2124 IN LECTURE NOTES IN COMPUTER SCIENCE
, 2001
"... This paper introduces RSMW, a new algorithm for stereo matching.The main aspect is the introduction of a Markov Random Field (MRF) model in the Symmetric Multiple Windows (SMW) stereo algorithm in order to obtain a nondeterministic relaxation.The SMW algorithm is an adaptive, multiple window sche ..."
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Cited by 7 (5 self)
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This paper introduces RSMW, a new algorithm for stereo matching.The main aspect is the introduction of a Markov Random Field (MRF) model in the Symmetric Multiple Windows (SMW) stereo algorithm in order to obtain a nondeterministic relaxation.The SMW algorithm is an adaptive, multiple window
Results 1  10
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580