Pattern Theory: From Representation to Inference (Oxford Studies in Modern European Culture)

Pattern Theory 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. Aimed at graduate students in biomedical engineering, mathematics, computer science, and electrical engineering with a good background in mathematics and probability, the text includes numerous exercises and an extensive bibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion website.The book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via condition structure. Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn. Chapters 10 and 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in the newly emergent field of Computational Anatomy. Finally, Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.

Pattern Theory: From Representation to Inference

Computers & Technology

Grenander, Ulf

MILLER, MICHAEL

Computer Science

Oxford University Press

The aim of pattern theory is to create mathematical knowledge representations of complex systems, analyze the mathematical properties of the resulting regular structures, and to apply them to practically occurring patterns in nature and the man-made world. Starting from an algebraic formulation of such representations they are studied in terms of their topological, dynamical and probabilistic aspects. Patterns are expressed through their typical behavior as well as through their variability around their typical form. Employing the representations (regular structures) algorithms are derived for the understanding, recognition, and restoration of observed patterns. The algorithms are investigated through computer experiments. The book is intended for statisticians and mathematicians with an interest in image analysis and pattern theory.

General Pattern Theory: A Mathematical Study of Regular Structures (Oxford Mathematical Monographs)

Clarendon Press

Pattern Theory 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. Aimed at graduate students in biomedical engineering, mathematics, computer science, and electrical engineering with a good background in mathematics and probability, the text include numerous exercises and an extensive bibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion website. The book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via condition structure. Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn. Chapters 10 and 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in the newly emergent field of Computational Anatomy. Finally, Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.

This book arose out of a number of different contexts, and numerous persons have contributed to its conception and development. It had its origin in a project initiated jointly with the IBM Cambridge Scien tific Center, particularly with Dr. Rhett Tsao, then of that Center. We are grateful to Mr. Norman Rasmussen, Manager of the IBM Scientific Center Complex, for his initial support. The work is being carried on at Brown University with generous support from the Office of Computing Activities of the National Science Foundation (grants GJ-174 and GJ-7l0); we are grateful to Dr. John Lehmann of this Office for his interest and encouragement. Professors Donald McClure and Richard Vitale of the Division of Applied Mathematics at Brown University contributed greatly to the project and taught courses in its spirit. We are indebted to them and to Dr. Tore Dalenius of the University of Stockholm for helpful criticisms of the manuscript. The final stimulus to the book's completion came from an invLtation to teach a course at the IBM European Systems Research Institute at Geneva. We are grateful to Dr. J.F. Blackburn, Director of the Institute, for his invitation, and to him and his wife Beverley for their hospitality. We are greatly indebted to Mrs. Katrina Avery for her splendid secretarial and editorial work on the manuscript.

A Short Course in Computational Probability and Statistics (Applied Mathematical Sciences)

Science & Math

Freiberger, Walter

Mathematics

Springer

Abstract inference (Wiley series in probability and mathematical statistics)

John Wiley

Grenander's text, a systematic account of the theory of probability for certain sample spaces with algebraic structure, covers semi-groups, groups, linear vector spaces, and algebra. It states and studies fundamental probabilistic problems for these spaces, focusing on concrete results rather than on general and complete answers. An introductory chapter offers historical background on the study of probabilities. Subsequent chapters explore stochastic semi-groups, compact and commutative stochastic groups, stochastic Lie groups, and locally compact stochastic groups. Additional topics include stochastic linear spaces and stochastic algebras. The techniques of Fourier analysis receive particular attention, serving as the main analytical tools in obtaining limit theorems for convolutions of probability distributions. Theoretical developments are accompanied by a number of examples, some of them of considerable scope.

Probabilities on Algebraic Structures (Dover Books on Mathematics)

Dover `

Elements of Pattern Theory (Johns Hopkins Studies in the Mathematical Sciences)

Although pattern and image analysis have their conceptual foundations in the mathematical sciences, it is now applied widely in many other areas of science. It embodies the techniques used to evaluate and compare the patterns, shapes, and other images―natural and artificial―that may range from such electronic forms as CAT scans and satellite images to patterns as ancient as the non-random assembly of letters that comprise a language.
In Patterns Ulf Grenander has written a book both for the student of mathematics and for the specialist who works with patterns in other fields, such as biology and medicine. Grenander begins by examining the nature of patterns and what distinguishes them from randomness. He then provides the mathematical foundation for the concepts and phenomena introduced. Finally, he provides examples and demonstrations of computer algorithms that permit the reader to recreate and explore some of the patterns discussed in the book.
"A dazzling tour de force on patterns. It is a substantial, original contribution by a leader―indeed, originator―in the field, and has the potential for significant impact on the direction of future research. It includes a profoundly insightful treatise on patterns, their nature, and the role of randomness in generating and analyzing them. Examples range from language to weaving to cellular biology. Even non-statisticians and non-scientists reading this material will find their thoughts clarified and provoked."―Alan F. Karr, National Institute of Statistical Sciences

Johns Hopkins University Press

From the Preface to the First Edition (1957): "The purpose of this book is two-fold. It is written in the terminology of the theoretical statistician because one of our objectives is to direct his attention to an approach to time series analysis that is essentially different from most of the techniques used by time series analysts in the past. The second objective is to present a unified treatment of methods that are being used increasingly in the physical sciences and technology. We hope that the book will be of considerable interest to research workers in these fields. Keeping the first objective in mind, we have given a rigorous mathematical discussion of these new topics in time series analysis. The existing literature in time series analysis is characterized with few exceptions by a lack of precision both in conception and in the mathematical treatment of the problems dealt with. To avoid this vagueness, we have devoted more space to rigorous proofs than may appear necessary to some readers, but we believe that a study of the proofs will furnish valuable clues to the practical validity of the results and be an important guide to intuition. We have tried to balance the formal proofs with intuitive remarks and comments on practical applications. While the regularity assumptions we have required in many cases may seem restrictive, appropriately interpreted they give an indication of the range in which the methods are practically valid. We have made such interpretations in the comments accompanying the formal proofs." 
Readers should have knowledge of statistics and basic probability. The second edition was printed with corrections.

Statistical Analysis of Stationary Time Series

Chelsea Pub. Co

From the Preface (1955): The first part of the present exposition is devoted to the theory of Toeplitz forms. The second part deals with applications, in particular to the calculus of probability and mathematical statistics. Neither part claims completeness in any way. Our purpose has been to elucidate the principal ideas of this remarkable chapter of modern analysis and to help the interested student of mathematical statistics to acquire a working knowledge of the subject. The somewhat protracted Chapter 1 explains not only the notation employed but contains also the definition of important auxiliary concepts and the exposition of basic results which will be used later. This arrangement avoids interruptions in the main text ... [It is assumed] that the reader is in possession of the fundamental facts of the theory of functions. ``In chapters 2 and 3 certain topics appear which were treated in the book on orthogonal polynomials by G. Szego. In view of the progress made in this subject since the publication of that book (1939) it was possible to bring some details in an improved setting. The other chapters contain partly old and partly more recent results, some older facts in a new setting, and finally some completely new results. Chapter 1-6 and Chapter 9 have been prepared by Szego, the other chapters by Grenander ... ''

Toeplitz Forms and Their Applications

This monograph reports a thought experiment with a mathematical structure intended to illustrate the workings of a mind. It presents a mathematical theory of human thought based on pattern theory with a graph-based approach to thinking. The method illustrated and produced by extensive computer simulations is related to neural networks. Based mainly on introspection, it is speculative rather than empirical such that it differs radically in attitude from the conventional wisdom of current cognitive science.

CALCULUS OF IDEAS, A: A MATHEMATICAL STUDY OF HUMAN THOUGHT

Programming

World Scientific Publishing Company

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