In this volume the author gives an introduction to the theory of group representations and their applications in image science. The main feature of the presentation is a systematic treatment of the invariance principle in image processing and pattern recognition with the help of group theoretical methods. The invariance properties of a problem often largely define the solution to the problem. Invariance principles are well known in theoretical physics but their use in image processing is only a few years old. The reader will find that group theory provides a unifying framework for many problems in image science. The volume is based on graduate-level lectures given by the author, and the book is intended for students and researchers interested in theoretical aspects of computer vision.

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Bibliographic Information

Book Title: Group Theoretical Methods in Image Processing
Authors: Reiner Lenz
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/3-540-52290-5
Publisher: Springer Berlin, Heidelberg
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1990
Softcover ISBN: 978-3-540-52290-4Published: 07 March 1990
eBook ISBN: 978-3-540-46947-6Published: 02 July 2005
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: VIII, 139
Topics: Computer Applications, Signal, Image and Speech Processing, Image Processing and Computer Vision, Pattern Recognition, Group Theory and Generalizations

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Table of contents (6 chapters)

Front Matter

Introduction

Preliminaries

Representations of groups

Representations of somes matrix groups

Fourier series on compact groups

Applications

Back Matter

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