Overview

Pattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis of new signals. This book treats the mathematical tools, the models themselves, and the computational algorithms for applying statistics to analyze six representative classes of signals of increasing complexity. The book covers patterns in text, sound, and images. Discussions of images include recognizing characters, textures, nature scenes, and human faces. The text includes online access to the materials (data, code, etc.) needed for the exercises.

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9781568815794

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Reviews

Pattern Theory covers six classic attempts at modeling signals from the human and natural world: natural language (written), music, character recognition, texture modeling, face recognition, and natural scenes. These applications, appealing to students and researchers alike, include fourteen 'crash courses' giving all the needed basics, exercises, and numerical simulations. ... a complete pedagogic tool at master or first-year graduate level. I endorse the publication of Pattern Theory, and will actually use it and recommend it to other researchers. --Jean-Michel Morel, CMLA This book is fascinating. It develops a statistic approach to finding the patterns in the signals generated by the world. The style is lucid. I'm reminded of Mumford's exposition of Theta functions and Abelian varieties in his Tata lectures. The exposition is thorough. The authors provide the necessary mathematical tools allowing scientists to pursue an exciting subject. I've been running a seminar at MIT entitled 'New Opportunities for the Interactions of Mathematics and Other Disciplines' because I'm convinced that mathematics will move in surprising new directions. Pattern Theory, a decade's effort, is a prime example. --I. M. Singer, Institute Professor, MIT What singles out this outstanding book is an extremely original approach ... The authors are leaders in signal and image processing and this book is based on their innovative research work. The overall organization of the book is marvelous. It is a crescendo. The authors do not have any methodological prejudice. Reading this book is like entering David Mumford's office and beginning a friendly and informal scientific discussion with Agnes and David. That is a good approximation to paradise. --Yves Meyer, Membre de l'Institut, Foreign Honorary Member of the American Academy of Arts and Sciences

Author Information

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). Agnes Desolneux is a researcher at CNRS/Universite 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.

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