Results 11  20
of
2,280
Robust Higher Order Potentials for Enforcing Label Consistency
, 2009
"... This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation ..."
Abstract

Cited by 259 (34 self)
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This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation
Efficient Simulation from the Multivariate Normal and Studentt Distributions Subject to Linear Constraints and the Evaluation of Constraint Probabilities
, 1991
"... The construction and implementation of a Gibbs sampler for efficient simulation from the truncated multivariate normal and Studentt distributions is described. It is shown how the accuracy and convergence of integrals based on the Gibbs sample may be constructed, and how an estimate of the probabil ..."
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Cited by 211 (10 self)
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The construction and implementation of a Gibbs sampler for efficient simulation from the truncated multivariate normal and Studentt distributions is described. It is shown how the accuracy and convergence of integrals based on the Gibbs sample may be constructed, and how an estimate
Discriminative learning of Markov random fields for segmentation of 3d scan data
 In Proc. of the Conf. on Computer Vision and Pattern Recognition (CVPR
, 2005
"... We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov Random Fields (MRFs) which support efficient graphcut inference. The MRF models incorporate a large set of diverse features and enforce the preference that a ..."
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Cited by 156 (3 self)
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We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov Random Fields (MRFs) which support efficient graphcut inference. The MRF models incorporate a large set of diverse features and enforce the preference
Integer linear programming inference for conditional random fields
 Proc. of ICML
, 2005
"... Inference in Conditional Random Fields and Hidden Markov Models is done using the Viterbi algorithm, an efficient dynamic programming algorithm. In many cases, general (nonlocal and nonsequential) constraints may exist over the output sequence, but cannot be incorporated and exploited in a natu ..."
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Cited by 90 (18 self)
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Inference in Conditional Random Fields and Hidden Markov Models is done using the Viterbi algorithm, an efficient dynamic programming algorithm. In many cases, general (nonlocal and nonsequential) constraints may exist over the output sequence, but cannot be incorporated and exploited in a nat
Bayesian inference for generalized additive mixed models based on markov random field priors
 C
, 2001
"... Summary. Most regression problems in practice require ¯exible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in lo ..."
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Cited by 112 (26 self)
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Summary. Most regression problems in practice require ¯exible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation
Graph Cut based Inference with Cooccurrence Statistics
"... Abstract. Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is computationally tractable. In this paper we consider a class of global potentials defined over all variables in the CRF. We show how they can be readily ..."
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Cited by 100 (13 self)
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Abstract. Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is computationally tractable. In this paper we consider a class of global potentials defined over all variables in the CRF. We show how they can
Diversitybased Inference of Finite Automata
 JOURNAL OF ACM
, 1994
"... We present new procedures for inferring the structure of a finitestate automaton (FSA) from its input \ output behavior, using access to the automaton to perform experiments. Our procedures use a new representation for finite automata, based on the notion of equivalence between tesfs. We call the ..."
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Cited by 84 (2 self)
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We present new procedures for inferring the structure of a finitestate automaton (FSA) from its input \ output behavior, using access to the automaton to perform experiments. Our procedures use a new representation for finite automata, based on the notion of equivalence between tesfs. We call
Comparison of Graph Cuts with Belief Propagation for Stereo, Using Identical MRF Parameters
 In ICCV
, 2003
"... Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRFbased approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use ..."
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Cited by 172 (0 self)
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Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRFbased approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use
Estimating Causal Effects from Large Data Sets Using Propensity
 Scores,”Annals of Internal Medicine
, 1997
"... The aim of many analyses of large databases is to draw causal inferences about the effects of actions, treatments, or interventions. Examples include the effects of various options available to a physician for treating a particular patient, the relative efficacies of various health care providers, ..."
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Cited by 177 (5 self)
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, and the consequences of implementing a new national health care policy. A complication of using large databases to achieve such aims is that their data are almost always observational rather than experimental. That is, the data in most large data sets are not based on the results of carefully conducted randomized
Supervised Random Walks: Predicting and Recommending Links in Social Networks
"... Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Althoug ..."
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Cited by 147 (3 self)
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. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from
Results 11  20
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2,280