Image Processing using Pulse-Coupled Neural Networks

Applications in Python

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February 27, 2022 | History

Image Processing using Pulse-Coupled Neural Networks

Applications in Python

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.

Publish Date
Publisher
Springer
Pages
238

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Edition Availability
Cover of: Image Processing using Pulse-Coupled Neural Networks
Image Processing using Pulse-Coupled Neural Networks: Applications in Python
Jun 23, 2015, Springer
paperback
Cover of: Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
2013, Springer
in English
Cover of: Image Processing using Pulse-Coupled Neural Networks
Image Processing using Pulse-Coupled Neural Networks: Applications in Python
2013, Springer Berlin Heidelberg, Imprint: Springer
electronic resource : in English - 3rd ed. 2013.
Cover of: Image Processing using Pulse-Coupled Neural Networks
Image Processing using Pulse-Coupled Neural Networks
Jan 08, 2013, Springer
paperback
Cover of: Image Processing Using Pulse-Coupled Neural Networks
Cover of: Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
Oct 14, 2010, Springer
paperback

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Book Details


Edition Notes

Source title: Image Processing using Pulse-Coupled Neural Networks: Applications in Python (Biological and Medical Physics, Biomedical Engineering)

Classifications

Library of Congress
TK5102.9

The Physical Object

Format
paperback
Number of pages
238

Edition Identifiers

Open Library
OL28946735M
ISBN 10
3642440339
ISBN 13
9783642440335

Work Identifiers

Work ID
OL19856880W

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February 27, 2022 Edited by ImportBot import existing book
August 18, 2020 Created by ImportBot Imported from amazon.com record