Image.Processing.Using.Pulse.Coupled.Neural.Networks.Springer.Sep.2005.eBook-DDU.pdf

Image Processing Using Pulse-Coupled Neural Networks

T. Lindblad J.M. Kinser

Image Processing

Using Pulse-Coupled

Neural Networks

Second, Revised Edition

With 140 Figures

123

Professor Dr. Thomas Lindblad

Royal Institute of Technology, KTH-Physics, AlbaNova

S-10691 Stockholm, Sweden

E-mail: Lindblad@particle.kth.se

ProfessorDr.JasonM.Kinser

George Mason University

MSN 4E3, 10900 University Blvd., Manassas, VA 20110, USA, and

12230 Scones Hill Ct., Bristow VA, 20136, USA

E-mail: jkinser@gmu.edu

Library of Congress Control Number: 2005924953

ISBN-10 3-540-24218-X 2nd Edition, Springer Berlin Heidelberg New York

ISBN-13 978-3-540-24218-5 2nd Edition Springer Berlin Heidelberg New York

ISBN 3-540-76264-7 1st Edition, Springer Berlin Heidelberg New York

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Preface

It was stated in the preface to the ﬁrst edition of this book that image pro-

cessing by electronic means has been a very active ﬁeld for decades. This

is certainly still true and the goal has been, and still is, to have a machine

perform the same image functions which humans do quite easily. In reaching

this goal we have learnt about the human mechanisms and how to apply this

knowledge to image processing problems. Although there is still a long way to

go, we have learnt a lot during the last ﬁve or six years. This new information

and some ideas based upon it has been added to the second edition of our book

The present edition includes the theory and application of two cortical

models: the PCNN (pulse coupled neural network) and the ICM (intersecting

cortical model). These models are based upon biological models of the visual

cortex and it is prudent to review the algorithms that strongly inﬂuenced the

development of the PCNN and ICM. The outline of the book is otherwise

very much the same as in the ﬁrst edition although several new application

examples have been added.

In Chap. 7 a few of these applications will be reviewed including original

ideas by co-workers and colleagues. Special thanks are due to Soonil D.D.V.

Rughooputh, the dean of the Faculty of Science at the University of Mauritius

Guisong, and Harry C.S. Rughooputh, the dean of the Faculty of Engineering

at the University of Mauritius.

We should also like to acknowledge that Guisong Wang, a doctoral can-

didate in the School of Computational Sciences at GMU, made a signiﬁcant

contribution to Chap. 5.

We would also like to acknowledge the work of several diploma and Ph.D.

students at KTH, in particular Jenny Atmer, Nils Zetterlund and Ulf Ekblad.

Stockholm and Manassas,

Thomas Lindblad

April 2005

Jason M. Kinser

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