 | Biomedical Image Analysis | | |
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| | Surfaces and Segmentation | | | |  | 4D Deformable Models with Temporal Constraints: Application to 4D Cardiac Image Segmentation, J. Montagnat and H. Delingette, 2005 | | A Genetic Algorithm for the Topology Correction of Cortical Surfaces, Florent Ségonne, Eric Grimson, and Bruce Fischl, 2005 | | Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images, Walter Sun et al., 2005 | | Topology Preserving Tissue Classification with Fast Marching and Topology Templates, Pierre-Louis Bazin and Dzung L. Pham, 2005 |
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| | | | Tensor Computing | | | | | A Riemannian Framework for Tensor Computing, Xavier Pennec, Pierre Fillard, and Nicholas Ayache, 2005 | | Extrapolation of Sparse Tensor Fields: Application to the Modeling of Brain Variability, Pierre Fillard, Vincent Arsigny, Xavier Pennec, Paul Thompson, and Nicholas Ayache., 2005 |
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| Computer Vision and Machine Learning | | |
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| | Graphical Models and Bayesian Networks | | | | | Convergent Tree-reweighted Message Passing for Energy Minimization, Vladimir Kolmogorov, 2005 | | Digital Tapestry, Carsten Rother et al., 2005 | | On the Optimality of Tree-reweighted Max-product Message Passing, Vladimir Kolmogorov et al., 2005 | | Nonparametric Belief Propagation, Erik B. Sudderth et al., 2003 | | A Brief Introduction to Graphical Models and Bayesian Networks, Kevin Murphy, 1998 | | Markov Random Fields with Efficient Approximations, Yuri Boykov et al., 1998 | | Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, S. Geman and D. Geman, 1984 | | Nonparametric Belief Propagation, Erik Sudderth et al. | | Sequential Monte Carlo Methods Particle Filtering, Nando de Freitas et al. |
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| | | | Learning Algorithms | | | | | Bayesian image super-resolution, Michael E. Tipping et al., 2003 | | Fast Marginal Likelihood Maximisation for Sparse Bayesian Models, Michael E. Tipping, 2003 | | Information Theory, Inference, and Learning Algorithms, David J.C. MacKay, 2003 | | Sparse Bayesian Learning and the Relevance Vector Machine, Michael E. Tipping, 2001 | | Bayesian Methods for Neural Networks: Theory and Applications, David J.C. MacKay, 2000 |
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| | | | Object Detection and Tracking | | | | | Pictorial Structures for Object Recognition, Daniel Huttenlocher et al., 2005 | | Sparse Bayesian Learning for Efficient Visual Tracking, Oliver Williams et al., 2005 | | Feature-Centric Evaluation for Efficient Cascaded Object Detection, Henry W. Schneiderman, 2004 | | Gibbs Likelihoods for Bayesian Tracking, Stefan Roth et al., 2004 | | Learning a Restricted Bayesian Network for Object Detection, Henry W. Schneiderman, 2004 | | Tracking Loose-limbed People, Leonid Sigal et al., 2004 | | Fast and Robust Face Finding via Local Context, Hannes Kruppa et al., 2003 | | Object Detection Using the Statistics of Parts, Henry W. Schneiderman et al., 2002 | | Robust Real-time Object Detection, Paul Viola et al., 2001 |
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| Lectures, Speeches and Talks | | |
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| Miscellaneous | | |
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| Signal and Image Processing | | |
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| | Correlation Filtering | | |
| | | | Shape Fitting | | |
| | | | Steerable Filters | | |
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