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Conditioned Simulations of Random Velocity Fields
Mathematical Geosciences, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Geraets, David +2 more
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Model fusion of conditional random fields
2007 IEEE International Conference on Systems, Man and Cybernetics, 2007This paper introduces two model fusion methods on a series of sub-models of Conditional Random Fields (CRFs): majority voting and feature fusion. The former performs on the results of each participant without any consideration about the underlying details of each sub-model, and the latter takes place on feature level to produce modified feature weights
Lu Li +3 more
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Infinite Latent Conditional Random Fields
2013 IEEE International Conference on Computer Vision Workshops, 2013In this paper, we present Infinite Latent Conditional Random Fields (ILCRFs) that model the data through a mixture of CRFs generated from Dirichlet processes. Each CRF represents one possible explanation of the data. In addition to visible nodes and edges that exist in classic CRFs, it generatively models the distribution of different CRF structures ...
Yun Jiang, Ashutosh Saxena
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Staging tissues with conditional random fields
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011We present a framework for identifying disease states by classifying cells in the pathological regions of tissues into different categories. We use conditional random fields (CRF) to incorporate characteristics of cells and their spatial distributions.
Jagath C. Rajapakse, Song Liu
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Learning Conditional Random Fields for Stereo
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose heuristic restrictions or priors on disparities, for example by modulating local smoothness costs with intensity gradients. In this paper we seek to replace such heuristics with explicit probabilistic models of disparities and ...
Daniel Scharstein, Chris Pal
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Distributed training for Conditional Random Fields
Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010), 2010This paper proposes a novel distributed training method of Conditional Random Fields (CRFs) by utilizing the clusters built from commodity computers. The method employs Message Passing Interface (MPI) to deal with large-scale data in two steps. Firstly, the entire training data is divided into several small pieces, each of which can be handled by one ...
Xiaojun Lin 0002 +3 more
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Learning Flexible Features for Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increase in model complexity. In this paper, we present a model that is capable of learning such structures using a random field of parameterized features.
Liam Stewart +2 more
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Neural conditional random fields
2010We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks.
Do, Trinh Minh Tri, Artières, Thierry
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Margin Losses for Training Conditional Random Fields
Journal of Mathematical Imaging and Vision, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ehsan Ahmadi, Zohreh Azimifar
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Multi-Illuminant Estimation With Conditional Random Fields
IEEE Transactions on Image Processing, 2014Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over
Shida Beigpour +3 more
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