Image Processing Using Pulse-Coupled Neural Networks / Edition 2

by Thomas Lindblad, Jason M. Kinser

Overview

This is the first book to explain and demonstrate the tremendous ability of Pulse-Coupled Neural Networks (PCNNs) when applied to the field of image processing. PCNNs and their derivatives are biologically inspired models that are powerful tools for extracting texture, segments, and edges from images. As these attributes form the foundations of most image processing tasks, the use of PCNNs facilitates traditional tasks such as recognition, foveation, and image fusion. PCNN technology has also paved the way for new image processing techniques such as object isolation, spiral image fusion, image signatures, and content-based image searches. This volume contains examples of several image processing applications, as well as a review of hardware implementations.

Product Details

ISBN-13:	9783642063435
Publisher:	Springer Berlin Heidelberg
Publication date:	12/16/2010
Edition description:	Softcover reprint of hardcover 2nd ed. 2005
Pages:	164
Product dimensions:	6.10(w) x 9.25(h) x 0.36(d)

About the Author

Thomas Lindblad is a professor at the Royal Institute of Technology (Physics) in Skholm. Working and teaching nuclear and environmental physics his main interest is with sensors, signal processing and intelligent data analysis of torrent data from experiments on-line accelerators, in space, etc.

Jason Kinser is an associate professor at George Mason University. He has developed a plethora of image processing applications in the medical, military, and industrial fields. He has been responsible for the conversion of PCNN theory into practical applications providing many improvements in both speed and performance.

Table of Contents

and Theory.- Theory of Digital Simulation.- Automated Image Object Recognition.- Image Fusion.- Image Texture Processing.- Image Signatures.- Miscellaneous Applications.- Hardware Implementations.

Editorial Reviews

3 Stars from Doody

This book introduces the Pulse-Coupled Neural Network (PCNN) and its applications in image processing. The PCNN is a neural network algorithm based on a visual cortex theory. The PCNN theory, the PCNN algorithms for various image processing techniques, and the software and hardware implementation of the PCNN are discussed. Known image processing applications, such as segmentation, edge detection, texture extraction, object recognition, object isolation, motion processing, foveation, noise suppression, and image fusion are explored. This book is written for those who are familiar with image processing techniques and Fourier transforms, and requires some basic mathematical background. Three visual cortex models are reviewed (Eckhornl, Rybakl, and Parodi). The PCNN is based on the Eckhorn model with some modifications. It contains two major compartments: the feeding and the linking compartments. The PCNN algorithm provides an iterative computing scheme and terminates according to user's needs. It produces a series of binary pulse images when stimulated with a gray-scale or color image. Unlike traditional neural networks, the PCNN does not have a large fan-in and fan-out. It only has local connections and is quite plausible for electronic implementation. This book is a great introduction to the PCNN algorithm in image processing. It helps us to understand how the PCNN approaches human vision. Unlike other neural networks books, this book provides an extensive review of the various image processing applications of the PCNN.

Meide Zhao

Reviewer: Meide Zhao, PhD (University of Illinois at Chicago College of Medicine)
Description: This book introduces the Pulse-Coupled Neural Network (PCNN) and its applications in image processing. The PCNN is a neural network algorithm based on a visual cortex theory.
Purpose: The PCNN theory, the PCNN algorithms for various image processing techniques, and the software and hardware implementation of the PCNN are discussed. Known image processing applications, such as segmentation, edge detection, texture extraction, object recognition, object isolation, motion processing, foveation, noise suppression, and image fusion are explored.
Audience: This book is written for those who are familiar with image processing techniques and Fourier transforms, and requires some basic mathematical background.
Features: Three visual cortex models are reviewed (Eckhornl, Rybakl, and Parodi). The PCNN is based on the Eckhorn model with some modifications. It contains two major compartments: the feeding and the linking compartments. The PCNN algorithm provides an iterative computing scheme and terminates according to user's needs. It produces a series of binary pulse images when stimulated with a gray-scale or color image. Unlike traditional neural networks, the PCNN does not have a large fan-in and fan-out. It only has local connections and is quite plausible for electronic implementation.
Assessment: This book is a great introduction to the PCNN algorithm in image processing. It helps us to understand how the PCNN approaches human vision. Unlike other neural networks books, this book provides an extensive review of the various image processing applications of the PCNN.

Doody's Review Service