@InProceedings{8081195,
  author = {J. Giovannelli and C. Vacar},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Deconvolution-segmentation for textured images},
  year = {2017},
  pages = {191-195},
  abstract = {The paper tackles the problem of joint deconvolution and segmentation specifically for textured images. The images are composed of patches of textures that belong to a set of K possible classes. Each class of image is described by a Gaussian random field and the classes are modelled by a Potts field. The method relies on a hierarchical model and a Bayesian strategy to jointly estimate the labels, the textured images as well as the hyperparameters. An important point is that the parameter of the Potts field is also estimated. The estimators are designed in an optimal manner (marginal posterior maximizer for the labels and posterior mean for the other unknowns). They are computed based on a convergent procedure, from samples of the posterior obtained through an MCMC algorithm (Gibbs sampler including Perturbation-Optimization). A first numerical evaluation provides encouraging results despite the strong difficulty of the problem.},
  keywords = {Bayes methods;deconvolution;estimation theory;image segmentation;image texture;Markov processes;Monte Carlo methods;optimisation;Gaussian random field;Potts field;textured images;image deconvolution;image segmentation;joint deconvolution-segmentation;marginal posterior maximizer;labels maximizers;MCMC algorithm;Gibbs sampling;K-possible class;Perturbation Optimization;Image segmentation;Bayes methods;Europe;Signal processing;Signal processing algorithms;Markov processes;Optimization;Deconvolution;segmentation;texture;Bayes;Potts;sampling;optimization},
  doi = {10.23919/EUSIPCO.2017.8081195},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570343213.pdf},
}

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The method relies on a hierarchical model and a Bayesian strategy to jointly estimate the labels, the textured images as well as the hyperparameters. An important point is that the parameter of the Potts field is also estimated. The estimators are designed in an optimal manner (marginal posterior maximizer for the labels and posterior mean for the other unknowns). They are computed based on a convergent procedure, from samples of the posterior obtained through an MCMC algorithm (Gibbs sampler including Perturbation-Optimization). A first numerical evaluation provides encouraging results despite the strong difficulty of the problem.","keywords":"Bayes methods;deconvolution;estimation theory;image segmentation;image texture;Markov processes;Monte Carlo methods;optimisation;Gaussian random field;Potts field;textured images;image deconvolution;image segmentation;joint deconvolution-segmentation;marginal posterior maximizer;labels maximizers;MCMC algorithm;Gibbs sampling;K-possible class;Perturbation Optimization;Image segmentation;Bayes methods;Europe;Signal processing;Signal processing algorithms;Markov processes;Optimization;Deconvolution;segmentation;texture;Bayes;Potts;sampling;optimization","doi":"10.23919/EUSIPCO.2017.8081195","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570343213.pdf","bibtex":"@InProceedings{8081195,\n author = {J. Giovannelli and C. Vacar},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Deconvolution-segmentation for textured images},\n year = {2017},\n pages = {191-195},\n abstract = {The paper tackles the problem of joint deconvolution and segmentation specifically for textured images. The images are composed of patches of textures that belong to a set of K possible classes. Each class of image is described by a Gaussian random field and the classes are modelled by a Potts field. The method relies on a hierarchical model and a Bayesian strategy to jointly estimate the labels, the textured images as well as the hyperparameters. An important point is that the parameter of the Potts field is also estimated. The estimators are designed in an optimal manner (marginal posterior maximizer for the labels and posterior mean for the other unknowns). They are computed based on a convergent procedure, from samples of the posterior obtained through an MCMC algorithm (Gibbs sampler including Perturbation-Optimization). A first numerical evaluation provides encouraging results despite the strong difficulty of the problem.},\n keywords = {Bayes methods;deconvolution;estimation theory;image segmentation;image texture;Markov processes;Monte Carlo methods;optimisation;Gaussian random field;Potts field;textured images;image deconvolution;image segmentation;joint deconvolution-segmentation;marginal posterior maximizer;labels maximizers;MCMC algorithm;Gibbs sampling;K-possible class;Perturbation Optimization;Image segmentation;Bayes methods;Europe;Signal processing;Signal processing algorithms;Markov processes;Optimization;Deconvolution;segmentation;texture;Bayes;Potts;sampling;optimization},\n doi = {10.23919/EUSIPCO.2017.8081195},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570343213.pdf},\n}\n\n","author_short":["Giovannelli, J.","Vacar, C."],"key":"8081195","id":"8081195","bibbaseid":"giovannelli-vacar-deconvolutionsegmentationfortexturedimages-2017","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570343213.pdf"},"keyword":["Bayes methods;deconvolution;estimation theory;image segmentation;image texture;Markov processes;Monte Carlo methods;optimisation;Gaussian random field;Potts field;textured images;image deconvolution;image segmentation;joint deconvolution-segmentation;marginal posterior maximizer;labels maximizers;MCMC algorithm;Gibbs sampling;K-possible class;Perturbation Optimization;Image segmentation;Bayes methods;Europe;Signal processing;Signal processing algorithms;Markov processes;Optimization;Deconvolution;segmentation;texture;Bayes;Potts;sampling;optimization"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","creationDate":"2021-02-13T16:38:25.513Z","downloads":0,"keywords":["bayes methods;deconvolution;estimation theory;image segmentation;image texture;markov processes;monte carlo methods;optimisation;gaussian random field;potts field;textured images;image deconvolution;image segmentation;joint deconvolution-segmentation;marginal posterior maximizer;labels maximizers;mcmc algorithm;gibbs sampling;k-possible class;perturbation optimization;image segmentation;bayes methods;europe;signal processing;signal processing algorithms;markov processes;optimization;deconvolution;segmentation;texture;bayes;potts;sampling;optimization"],"search_terms":["deconvolution","segmentation","textured","images","giovannelli","vacar"],"title":"Deconvolution-segmentation for textured images","year":2017,"dataSources":["2MNbFYjMYTD6z7ExY","uP2aT6Qs8sfZJ6s8b"]}