| Description |
1 online resource (xi, 164 pages) : illustrations (some color) |
| Contents |
Cover -- Preface -- Table of Contents -- 1 Introduction and Theory -- 1.1 General Aspects -- 1.2 The State of Traditional Image Processing -- 1.3 Visual Cortex Theory -- 1.4 Summary. -- 2 Theory of Digital Simulation -- 2.1 The Pulse-Coupled Neural Network -- 2.2 The ICM ... A Generalized Digital Model -- 2.3 Summary. -- 3 Automated Image Object Recognition -- 3.1 Important Image Features -- 3.2 Image Segmentation ... A Red Blood Cell Example -- 3.3 Image Segmentation ... A Mammography Example -- 3.4 Image Recognition ... An Aircraft Example -- 3.5 Image Classification ... Aurora Borealis Example -- 3.6 The Fractional Power Filter. -- 3.7 Target Recognition ... Binary Correlations -- 3.8 Image Factorisation. -- 3.9 A Feedback Pulse Image Generator -- 3.10 Object Isolation -- 3.11 Dynamic Object Isolation -- 3.12 Shadowed Objects -- 3.13 Consideration of Noisy Images -- 3.14 Summary. -- 4 Image Fusion -- 4.1 The Multi-spectral Model -- 4.2 Pulse-Coupled Image Fusion Design -- 4.3 A Colour Image Example -- 4.4 Example of Fusing Wavelet Filtered Images -- 4.5 Detection of Multi-spectral Targets -- 4.6 Example of Fusing Wavelet Filtered Images -- 4.7 Summary. -- 5 Image Texture Processing -- 5.1 Pulse Spectra -- 5.2 Statistical Separation of the Spectra -- 5.3 Recognition Using Statistical Methods -- 5.4 Recognition of the Pulse Spectra via an Associative Memory -- 5.5 Summary. -- 6 Image Signatures -- 6.1 Image Signature Theory -- 6.2 The Signatures of Objects -- 6.3 The Signatures of Real Images -- 6.4 Image Signature Database -- 6.5 Computing the Optimal Viewing Angle -- 6.6 Motion Estimation -- 6.7 Summary. -- 7 Miscellaneous Applications -- 7.1 Foveation. -- 7.2 Histogram Driven Alterations -- 7.3 Maze Solutions -- 7.4 Barcode Applications -- 7.5 Summary. -- 8 Hardware Implementations -- 8.1 Theory of Hardware Implementation -- 8.2 Implementation on a CNAPs Processor -- 8.3 Implementation in VLSI -- 8.4 Implementation in FPGA -- 8.5 An Optical Implementation -- 8.6 Summary. -- References |
| Summary |
"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."--Jacket |
| Bibliography |
Includes bibliographical references (pages 155-161) and index |
| Notes |
Print version record |
| In |
Springer e-books |
| Subject |
Image processing.
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Neural networks (Computer science)
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image processing.
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TECHNOLOGY & ENGINEERING -- Imaging Systems.
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Neural networks (Computer science)
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Image processing.
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Ingénierie.
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Image processing
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Neural networks (Computer science)
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| Form |
Electronic book
|
| Author |
Kinser, Jason M., 1962-
|
| LC no. |
2005924953 |
| ISBN |
9783540242185 |
|
354024218X |
|
3540282939 |
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9783540282938 |
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6610312990 |
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9786610312993 |
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