Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo Markov chain techniques for unsupervised MRF-based image denoising
Pattern Recognition Letters
Binary Tomography for Triplane Cardiography
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
MRF parameter estimation by an accelerated method
Pattern Recognition Letters
Stochastic Computation of Medial Axis in Markov Random Fields
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simulations of spartan random fields
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
Resuming Shapes with Applications
Journal of Mathematical Imaging and Vision
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Delivery Massager: A tool for propagating seismic inversion information into reservoir models
Computers & Geosciences
Inter-Image Statistics for 3D Environment Modeling
International Journal of Computer Vision
Pre-processing Large Spatial Data Sets with Bayesian Methods
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Brief paper: Decentralized coordination of autonomous swarms using parallel Gibbs sampling
Automatica (Journal of IFAC)
Gibbs sampler-based coordination of autonomous swarms
Automatica (Journal of IFAC)
Multi-kernel multi-label learning with max-margin concept network
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Model-based adaptive spatial sampling for occurrence map construction
Statistics and Computing
A graph theoretic approach to simulation and classification
Computational Statistics & Data Analysis
Cluster Differences Unfolding for Two-Way Two-Mode Preference Rating Data
Journal of Classification
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The book is mainly concerned with the mathematical foundations of Bayesian image analysis and its algorithms. This amounts to the study of Markov random fields and dynamic Monte Carlo algorithms like sampling, simulated annealing and stochastic gradient algorithms. The approach is introductory and elementary: given basic concepts from linear algebra and real analysis it is self-contained. No previous knowledge from image analysis is required. Knowledge of elementary probability theory and statistics is certainly beneficial but not absolutely necessary. The necessary background from imaging is sketched and illustrated by a number of concrete applications like restoration, texture segmentation and motion analysis.