Cellular neural networks and visual computing: foundation and applications
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2002
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XI, 396 S. Ill., graph. Darst. |
| ISBN: | 0521652472 |
Internformat
MARC
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| 100 | 1 | |a Chua, Leon O. |d 1936- |e Verfasser |0 (DE-588)173544630 |4 aut | |
| 245 | 1 | 0 | |a Cellular neural networks and visual computing |b foundation and applications |c Leon O. Chua and Tamás Roska |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2002 | |
| 300 | |a XI, 396 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-009423624 | |
Datensatz im Suchindex
| _version_ | 1809768493255491584 |
|---|---|
| adam_text |
Contents
Acknowledgements page xi
¦
Introduction 1
¦
Notation, def initions, and mathematical foundation 7
2.1 Basic notation and definitions 7
2.2 Mathematical foundations 14
¦
Characteristics and analysis of simple CNN templates 35
3.1 Two case studies: the EDGE and EDGEGRAY templates 35
3.2 Three quick Steps for sketching the shifted DP plot 49
3.3 Some other useful templates 50
¦
Simulation of the CNN dynamics 100
4.1 Integration of the Standard CNN differential equation 100
4.2 Image input 101
4.3 Software Simulation 102
4.4 Digital hardware accelerators 110
4.5 Analog CNN implementations 111
4.6 Scaling the signals 113
4.7 Discrete-time CNN (DTCNN) 114
vii
viii Contents
Binary CNN characterization via Boolean f unctions 115
5.1 Binary and universal CNN truth table 115
5.2 Boolean and compressed local rules 122
5.3 Optimizing the truth table 124
Uncoupled CNNs: unif ied theory and applications 139
6.1 The complete stability phenomenon 139
6.2 Explicit CNN Output formula 140
6.3 Proof of completely stable CNN theorem 142
6.4 The primary CNN mosaic 155
6.5 Explicit formula for transient waveform and settling time 156
6.6 Which local Boolean functions are realizable by uncoupled CNNs? 161
6.7 Geometrical interpretations 162
6.8 How to design uncoupled CNNs with prescribed Boolean functions 166
6.9 How to realize non-separable local Boolean functions? 173
Introduction to the CNN Universal Machine 183
7.1 Global clock and global wire 184
7.2 Setinclusion 184
7.3 Translation of sets and binary images 188
7.4 Opening and closing and implementing any morphological Operator 190
7.5 Implementing any prescribed Boolean transition function by not more than
256templates 195
7.6 Minimizing the number of templates when implementing any possible
Boolean transition function 198
7.7 Analog-to-digital array Converter 201
8 Back tobasics: Nonlinear dynamics and complete stability 205
8.1 A glimpse of things to come 205
8.2 An oscillatory CNN with only two cells 205
8.3 A chaotic CNN with only two cells and one sinusoidal input 210
8.4 Symmetrie A template implies complete stability 214
8.5 Positive and sign-symmetric A template implies complete stability 219
ix Contents
8.6 Positive and cell-linking A template implies complete stability 224
8.7 Stability of some sign-antisymmetric CNNs 231
A Appendix to Chapter 8 236
¦
The CNN Universal Machine (CNN-UM) 239
9.1 The architecture 240
9.2 A simple example in more detail 244
9.3 A very simple example on the circuit level 246
9.4 Language, Compiler, operating System 254
¦
10 Template designtools 258
10.1 Various design techniques 258
10.2 Binary representation, linear separability, and simple decomposition 260
10.3 Template optimization 264
10.4 Template decomposition techniques 265
11 CNNs for linear imageprocessing 267
11.1 Linear image processing with B templates is equivalent to spatial convo-
lution with FIR kerneis 267
11.2 Spatial frequency characterization 269
11.3 A primer on properties and applications of discrete-space Fourier trans-
form (DSFT) 272
11.4 Linear image processing with A and B templates is equivalent to spatial
convolution with IIR kerneis 272
12 Coupled CNN with linear synaptic weights 276
12.1 Active and inactive cells, dynamic local rules 278
12.2 Binary activation pattern and template format 283
12.3 A simple propagating type example with B/W symmetrical rule 284
12.4 The Connectivity problem 286
13 Uncoupled Standard CNNs with nonlinear synaptic weights 290
13.1 Dynamic equations and DP plot 291
Contents
14 Standard CNNswithdelayedsynapticweights and motionanalysis 296
14.1 Dynamic equations 296
14.2 Motion analysis - discrete time and continuous time image acquisition 297
Visual microprocessors - analog and digital VLSI implementation 3()4
of the CNN Universal Machine
15.1 The analog CNN core 304
15.2 Analogie CNN-UM cell 310
15.3 Emulated digital implementation 312
15.4 The Visual microprocessor and its computational infrastrueture 313
15.5 Computing power comparison 318
16 CNN modeis in the Visual pathway and the "BionicEye" 320
16.1 Receptive field organization, synaptic weights, and cloning template 321
16.2 Some prototype elementary funetions and CNN modeis of the Visual
pathway 322
16.3 A simple qualitative "engineering" model of a vertebrate retina 329
16.4 The "Bionic Eye" implemented on a CNN Universal Machine 338
Notes 339
Bibliograph)/ 348
Exercises 361
Appendices 389
Index 390 |
| any_adam_object | 1 |
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| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.3/2 |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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| indexdate | 2024-09-10T00:55:30Z |
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| isbn | 0521652472 |
| language | English |
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| spelling | Chua, Leon O. 1936- Verfasser (DE-588)173544630 aut Cellular neural networks and visual computing foundation and applications Leon O. Chua and Tamás Roska 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2002 XI, 396 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Neural networks (Computer science) Graphische Programmierung (DE-588)4337137-1 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Graphische Programmierung (DE-588)4337137-1 s DE-604 Roska, Tamas Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009423624&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Chua, Leon O. 1936- Roska, Tamas Cellular neural networks and visual computing foundation and applications Neural networks (Computer science) Graphische Programmierung (DE-588)4337137-1 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4337137-1 (DE-588)4226127-2 |
| title | Cellular neural networks and visual computing foundation and applications |
| title_auth | Cellular neural networks and visual computing foundation and applications |
| title_exact_search | Cellular neural networks and visual computing foundation and applications |
| title_full | Cellular neural networks and visual computing foundation and applications Leon O. Chua and Tamás Roska |
| title_fullStr | Cellular neural networks and visual computing foundation and applications Leon O. Chua and Tamás Roska |
| title_full_unstemmed | Cellular neural networks and visual computing foundation and applications Leon O. Chua and Tamás Roska |
| title_short | Cellular neural networks and visual computing |
| title_sort | cellular neural networks and visual computing foundation and applications |
| title_sub | foundation and applications |
| topic | Neural networks (Computer science) Graphische Programmierung (DE-588)4337137-1 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Neural networks (Computer science) Graphische Programmierung Neuronales Netz |
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