Stochastic geometry for image analysis

Xavier Descombes 1, 2
1 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
2 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous applications, covering remote sensing images, biological and medical imaging, are detailed. This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling
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https://hal.inria.fr/hal-00793677
Contributor : Xavier Descombes <>
Submitted on : Friday, February 22, 2013 - 5:38:14 PM
Last modification on : Monday, November 5, 2018 - 3:52:02 PM

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Xavier Descombes. Stochastic geometry for image analysis. Xavier Descombes. France. Wiley-ISTE, pp.384, 2011, 978-1-84821-240-4. ⟨hal-00793677⟩

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