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List of contents
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
About the author
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).
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.
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.
Summary
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.
