Pattern Theory

by David Mumford, Agnès Desolneux

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.

Genres: Mathematics, Science

428 pages, Hardcover

First published August 15, 2010

About the author

David Mumford is a Professor Emeritus of Mathematics at Brown University.

(David B. Mumford)

Reviews

[Adriano · Rating 3 out of 5]

Mumford's professed ”guru” on patterns was Ulf Grenander, and for a generalizable abstract treatment I prefer the the first half of another book, also called *Pattern Theory*, by the latter. Mumford has computer vision as his canonical field of application of theory, but his book did not help to explain why the Deep Learning algorithms are so successful in image classification. One key idea is probabilistic structure over graphs, and for this, I recommend the Grenander book instead.

[Chris · Rating 4 out of 5]

The only good machine learning book.

Unfortunately, machine learning.