Image Processing Using Pulse-Coupled Neural Networks

en Limba Engleză Paperback – 14 oct 2010

Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction.
The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.

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Specificații

ISBN-13: 9783642063435
ISBN-10: 3642063438
Pagini: 176
Ilustrații: XI, 164 p. 139 illus.
Dimensiuni: 155 x 235 x 9 mm
Greutate: 0.26 kg
Ediția:Softcover reprint of hardcover 2nd ed. 2005
Editura: Springer Berlin, Heidelberg
Colecția Springer
Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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

Notă biografică

Thomas Lindblad is a professor at the Royal Institute of Technology (Physics) in Stockholm. 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.

Textul de pe ultima copertă

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.

Caracteristici

Excellent presentation of a new concept of image processing by electronic neural networks based on the technical realization of biological concepts Includes supplementary material: sn.pub/extras

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