Image analysis, random fields and dynamic Monte Carlo methods : a mathematical introduction / Gerhard Winkler / Applications of mathematics ; 27

Winkler, Gerhard:

ISBN 10: 3540570691 / ISBN 13: 9783540570691

Editore: Berlin ; Heidelberg ; New York : Springer, 1995

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XIV, 324 S. : Ill., graph. Darst. ; 25 cm Like new! Unread book with minimum traces of storge. 9783540570691 Sprache: Englisch Gewicht in Gramm: 620.

Codice articolo 200206

Dati bibliografici

• Titolo: Image analysis, random fields and dynamic Monte Carlo methods : a mathematical introduction / Gerhard Winkler / Applications of mathematics ; 27
• Editore: Berlin ; Heidelberg ; New York : Springer
• Data di pubblicazione: 1995
• Lingua: Inglese
• ISBN 10: 3540570691
• ISBN 13: 9783540570691
• Rilegatura: Hardcover
• Edizione: 1st ed. 1995. 2nd printing.
• Cataloghi dei venditori:
  • Mathematik

Descrizione articolo

Riassunto

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.

Dalla quarta di copertina

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 elemenatry: 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.