Invariants for Pattern Recognition and Classification

ISBN 10: 9810242786 / ISBN 13: 9789810242787

Published by World Scientific Publishing Company, 2001

About this Item

Good condition. Good dust jacket. Volume 42. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Seller Inventory # GD-BD3TBTP-1-9810242786-1-LB-RED-2001

Bibliographic Details

• Title: Invariants for Pattern Recognition and Classification
• Publisher: World Scientific Publishing Company
• Publication Date: 2001
• Language: English
• ISBN 10: 9810242786
• ISBN 13: 9789810242787
• Binding: Hardcover
• Condition: Good
• Weight: 1 pound

About this title

Synopsis

This book was conceived from the realization that there was a need to update recent work on invariants in a single volume providing a useful set of references and pointers to related work. Since the publication in 1992 of J L Mundy and A Zisserman's Geometric Invariance in Computer Vision, the subject has been evolving rapidly. New approaches to invariants have been proposed and novel ways of defining and applying invariants to practical problem solving are testimony to the fundamental importance of the study of invariants to machine vision. This book represents a snapshot of current research around the world.A version of this collection of papers has appeared in the International Journal of Pattern Recognition and Artificial Intelligence (December 1999). The papers in this book are extended versions of the original material published in the journal. They are organized into two categories: foundations and applications. Foundation papers present new ways of defining or analyzing invariants, and application papers present novel ways in which known invariant theory is extended and effectively applied to real-world problems in interesting and difficult contexts. Each category contains roughly half of the papers, but there is considerable overlap. All papers carry an element of novelty and generalization that will be useful to theoreticians and practitioners alike. It is hoped that this volume will be not only useful but also inspirational to researchers in image processing, pattern recognition and computer vision at large.