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28.10.2024 um 19:30 Uhr
Pattern Theory
The Stochastic Analysis of Real-World Signals
von Agnes Desolneux, David Mumford
Verlag: Taylor & Francis Ltd
Taschenbuch
ISBN: 978-1-032-92005-4
Erscheint am 14.10.2024
Sprache: Englisch
Format: 229 mm [H] x 152 mm [B]
Umfang: 428 Seiten

Preis: 59,50 €
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Klappentext
Biografische Anmerkung
Inhaltsverzeichnis

Pattern theory is a field in applied mathematics that analyzes all types of signals that the world presents to us. For each chapter, the authors present a real-world problem - a class of signals that we want to model. Then they present the basic mathematical tools needed before moving into the model. Then the model is compared with the original



David Mumford is a professor emeritus of applied mathematics at Brown University. His contributions to mathematics fundamentally changed algebraic geometry, including his development of geometric invariant theory and his study of the moduli space of curves. In addition, Dr. Mumford's work in computer vision and pattern theory introduced new mathematical tools and models from analysis and differential geometry. He has been the recipient of many prestigious awards, including U.S. National Medal of Science (2010), the Wolf Foundation Prize in Mathematics (2008), the Steele Prize for Mathematical Exposition (2007), the Shaw Prize in Mathematical Sciences (2006), a MacArthur Foundation Fellowship (1987-1992), and the Fields Medal (1974).

Agnès Desolneux is a researcher at CNRS/Université Paris Descartes. A former student of David Mumford's, she earned her Ph.D. in applied mathematics from CMLA, ENS Cachan. Dr. Desolneux's research interests include statistical image analysis, Gestalt theory, mathematical modeling of visual perception, and medical imaging.



Preface
Notation
What Is Pattern Theory?
English Text and Markov Chains
Music and Piece wise Gaussian Models
Character Recognition and Syntactic Grouping
Image Texture, Segmentation and Gibbs Models
Faces and Flexible Templates
Natural Scenes and their Multiscale Analysis
Bibliography
Index