Table of Contents

1: Introduction
2: The Bayes paradigm, estimation and information measures
3: Probabilistic directed acyclic graphs and their entropies
4: Markov random fields on undirected graphs
5: Gaussian random fields on undirected graphs
6: The canonical representations of general pattern theory
7: Matrix group actions transforming patterns
8: Manifolds, active modes, and deformable templates
9: Second order and Gaussian fields
10: Metrics spaces for the matrix groups
11: Metrics spaces for the infinite dimensional diffeomorphisms
12: Metrics on photometric and geometric deformable templates
13: Estimation bounds for automated object recognition
14: Estimation on metric spaces with photometric variation
15: Information bounds for automated object recognition
16: Computational anatomy: shape, growth and atrophy comparison via diffeomorphisms
17: Computational anatomy: hypothesis testing on disease
18: Markov processes and random sampling
19: Jump diffusion inference in complex scenes

About the Author

Ulf Grenander is the L. Herbert Ballou University Professor at Brown University. He is a member of the Royal Swedish Academy of Science and an honorary fellow of the Royal Statistical Society in London
Michael Miller is the Professor of Electrical and Computer Engineering, Director of the Center for Imaging Science, and Professor of Biomedical Engineering at Johns Hopkins University, Baltimore. He completed his Ph.D. in Biomedical Engineering at The Johns Hopkins University in 1983.

Reviews

"Patterns Theory: From Representations to Inference" provides a comprehensive and accessible overview of the modern challenges in signal, data and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision.
*L'enseignement Mathematique*