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Image Processing Using Pulse-Coupled Neural Networks

:	Thomas Lindblad, Jason M. Kinser
:	Image Processing Using Pulse-Coupled Neural Networks
:	Springer-Verlag
:	9783540282938
:	2
:	CHF 94.70
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	164
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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.

	Preface	5
	Preface to the First Edition	7
	Contents	9
	1 Introduction and Theory	12
	1.1 General Aspects	12
	1.2 The State of Traditional Image Processing	13
	1.2.1 Generalisation versus Discrimination	13
	1.2.2 The World of Inner Products”	14
	1.2.3 The Mammalian Visual System	15
	1.2.4 Where Do We Go From Here?	15
	1.3 Visual Cortex Theory	16
	1.3.1 A Brief Overview of the Visual Cortex	16
	1.3.2 The Hodgkin–Huxley Model	17
	1.3.3 The Fitzhugh–Nagumo Model	18
	1.3.4 The Eckhorn Model	19
	1.3.5 The Rybak Model	20
	1.3.6 The Parodi Model	21
	1.4 Summary	21
	2 Theory of Digital Simulation	22
	2.1 The Pulse-Coupled Neural Network	22
	2.1.1 The Original PCNN Model	22
	2.1.2 Time Signatures	27
	2.1.3 The Neural Connections	29
	2.1.4 Fast Linking	32
	2.1.5 Fast Smoothing	33
	2.1.6 Analogue Time Simulation	34
	2.2 The ICM – A Generalized Digital Model	35
	2.2.1 Minimum Requirements	36
	2.2.2 The ICM	37
	2.2.3 Interference	38
	2.2.4 Curvature Flow Models	42
	2.2.5 Centripetal Autowaves	43
	2.3 Summary	45
	3 Automated Image Object Recognition	46
	3.1 Important Image Features	46
	3.2 Image Segmentation – A Red Blood Cell Example	52
	3.3 Image Segmentation – A Mammography Example	53
	3.4 Image Recognition – An Aircraft Example	54
	3.5 Image Classi.cation – Aurora Borealis Example	55
	3.6 The Fractional Power Filter	57
	3.7 Target Recognition – Binary Correlations	58
	3.8 Image Factorisation	62
	3.9 A Feedback Pulse Image Generator	63
	3.10 Object Isolation	66
	3.11 Dynamic Object Isolation	69
	3.12 Shadowed Objects	71
	3.13 Consideration of Noisy Images	73
	3.14 Summary	78
	4 Image Fusion	80
	4.1 The Multi-spectral Model	80
	4.2 Pulse-Coupled Image Fusion Design	82
	4.3 A Colour Image Example	84
	4.4 Example of Fusing Wavelet Filtered Images	86
	4.5 Detection of Multi-spectral Targets	86
	4.6 Example of Fusing Wavelet Filtered Images	91
	4.7 Summary	92
	5 Image Texture Processing	94
	5.1 Pulse Spectra	94
	5.2 Statistical Separation of the Spectra	98
	5.3 Recognition Using Statistical Methods	99
	5.4 Recognition of the Pulse Spectra via an Associative Memory	100
	5.5 Summary	103
	6 Image Signatures	104
	6.1 Image Signature Theory	104
	6.1.1 The PCNN and Image Signatures	105
	6.1.2 Colour Versus Shape	106
	6.2 The Signatures of Objects	106
	6.3 The Signatures of Real Images	108
	6.4 Image Signature Database	110
	6.5 Computing the Optimal Viewing Angle	111
	Nils Zetterlund	111
	6.6 Motion Estimation	114
	6.7 Summary	117
	7 Miscellaneous Applications	118
	7.1 Foveation	118
	7.1.1 The Foveation Algorithm	119
	7.1.2 Target Recognition by a PCNN Based Foveation Model	121
	7.2 Histogram Driven Alterations	124
	7.3 Maze Solutions	126
	7.4 Barcode Applications	127
	Soonil D.D.V. Rughooputh and Harry C.S. Rughooputh	127
	7.4.1 Barcode Generation from Data Sequence and Images	128
	Binary Code for a Still Image	128
	Algorithm	128
	Binary Barcode for Data Sequences	130
	Algorithm	130
	7.4.2 PCNN Counter	132
	7.4.3 Chemical Indexing	132
	Current Searching Technique	135
	7.4.4 Identi.cation and Classi.cation of Galaxies	137
	7.4.5 Navigational Systems	142
	7.4.6 Hand Gesture Recognition	145
	7.4.7 Road Surface Inspection	148
	7.5 Summary	152
	8 Hardware Implementations	154
	8.1 Theory of Hardware Implementation	154
	8.2 Implementation on a CNAPs Processor	155
	8.3 Implementation in VLSI	157
	8.4 Implementation in FPGA	157
	8.5 An Optical Implementation	162
	8.6 Summary	164
	References	166
	Index	174