Pattern Theory
The Stochastic Analysis of Real-World Signals, Second Edition
2nd Edition
Published on 1. January 2021
Book
Hardback
410 pages
978-1-138-05396-0 (ISBN)
Description
Like its widely praised, best-selling predecessor, Pattern Theory: The Stochastic Analysis of Real-World Signals, Second Edition 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.
New to theSecond Edition:
* A new chapter discussing Convolutional Neural Networks (CNN's) including the hierarchical structure of, and learning, with CNN's
*Additional topics, including flexible templates in medical applications, Gaussian models for texture synthesis, exponential models and their use.
New to theSecond Edition:
* A new chapter discussing Convolutional Neural Networks (CNN's) including the hierarchical structure of, and learning, with CNN's
*Additional topics, including flexible templates in medical applications, Gaussian models for texture synthesis, exponential models and their use.
More details
Edition
2nd edition
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-138-05396-0 (9781138053960)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition
Book
08/2010
1st Edition
A K Peters
€133.70
Shipment within 3-4 weeks
Persons
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 andmodels 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.
Yann Gousseau is a professor at Telecom-ParisTech, Paris. His interests lie in the mathematical modeling of natural images, scaling laws and images, image indexing and contentbased retrieval and, texture analysis and synthesis.
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.
Yann Gousseau is a professor at Telecom-ParisTech, Paris. His interests lie in the mathematical modeling of natural images, scaling laws and images, image indexing and contentbased retrieval and, texture analysis and synthesis.
Content
What Is Pattern Theory?. English Text and Markov Chains. Music and Piecewise Gaussian Models. Character Recognition and Syntactic Grouping. Image Texture, Segmentation and Gibbs Models. Faces and Flexible Templates. Natural Scenes and their Multiscale Analysis. Neural Nets for Learning and Synthesis. Bibliography