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. Editor(s): Descombes, Xavier. Num Pages: 356 pages, Illustrations. BIC Classification: TJ. Category: (P) Professional & Vocational. Dimension: 235 x 163 x 25. Weight in Grams: 684. . 2011. . . . .

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Bibliographic Details

Title: Stochastic Geometry for Image Analysis
Publisher: ISTE Ltd and John Wiley & Sons Inc
Publication Date: 2011
Language: English
ISBN 10: 1848212402
ISBN 13: 9781848212404
Binding: Hardcover
Condition: New

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Synopsis

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.

About the Author

Xavier Descombes is currently research director at INRIA (French research institute dedicated to digital science and technology). His research interests include Markov random fields, stochastic geometry and stochastic modeling in image processing.

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