Journal ArticleDOI
Multiband segmentation based on a hierarchical Markov model
Christophe Collet,Fionn Murtagh +1 more
TLDR
A new multiscale Markov segmentation model for multiband images is developed using quadtree multiple resolution analysis of a multiband image, which uses both inter- and intra-scale spatial Markov statistical dependencies.About:
This article is published in Pattern Recognition.The article was published on 2004-12-01. It has received 36 citations till now. The article focuses on the topics: Markov random field & Markov model.read more
Citations
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Journal ArticleDOI
Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs
TL;DR: The experiments that were performed on a bitemporal TerraSAR-X StripMap data set from South West England during and after a large-scale flooding in 2007 confirm the effectiveness of the proposed change detection method and show an increased classification accuracy of the hybrid MRF model in comparison to the sole application of the HMAP estimation.
Journal ArticleDOI
Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model.
TL;DR: A novel automated system for the segmentation of oncological PET data aiming at providing an accurate quantitative analysis tool is proposed and shows promise and can successfully segment patient lesions.
Journal ArticleDOI
A Hierarchical Spatio-Temporal Markov Model for Improved Flood Mapping Using Multi-Temporal X-Band SAR Data
Sandro Martinis,André Twele +1 more
TL;DR: Experiments performed on a bi-temporal TerraSAR-X ScanSAR data-set from the Caprivi region of Namibia during flooding in 2009 and 2010 confirm the effectiveness of integrating hierarchical as well as spatio-tem temporal context into the labeling process, and of adapting the models to irregular graph structures.
Journal ArticleDOI
Background-Source Separation in astronomical images with Bayesian probability theory (I): the method
TL;DR: A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described in this article, where Bayesian probability theory is applied to gain insight into the coexistence of backgrounds and sources through a two-component mixture model.
Journal ArticleDOI
Bayesian inference for multiband image segmentation via model-based cluster trees
TL;DR: A new methodology for doing model-based cluster trees is proposed, which bases inference on finite mixture models estimated by maximum likelihood using the EM algorithm, and automatically chooses the number of clusters by Bayesian model selection, approximated using BIC, the Bayesian Information Criterion.
References
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Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more