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Synopsis
This volume reviews the theoretical foundations of pulse-coupled neural networks, and then discusses image processing applications including segmentation and foveation. It also looks at the PCNN ability to process logical arguments and at how to implement it into specialized hardware.
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From the Back Cover
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.
About the Author
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.
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Image processing using pulse coupled neural networks / Thomas Lindblad and Jason M. Kinser / Perspectives in neural computing
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London ; Berlin ; Singapore ; Tokyo ; Heidelberg ; New York ; Barcelona ; Budapest ; Hong Kong ; Milan ; Paris ; Santa Clara : Springer, 1998
ISBN 10: 3540762647
ISBN 13: 9783540762645
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Image Processing Using Pulse Coupled Neural Networks
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