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Tables of Contents for Optical Pattern Recognition
Chapter/Section Title
Page #
Page Count
Contributors
xiii
2
Preface
xv
1 Pattern recognition with optics
1
39
1.1 Introduction
1
1
1.2 Optical correlators
1
2
1.3 Hybrid optical correlators
3
4
1.4 Autonomous target tracking
7
2
1.5 Optical-disk-based joint transform correlator
9
3
1.6 Photorefractive-crystal-based correlator
12
2
1.7 Optical neural networks
14
1
1.8 Scale-and rotational-invariant correlation
15
2
1.9 Wavelet transform filtering
17
1
1.10 Discriminant filtering
17
2
1.11 Phase-only filtering
19
1
1.12 Pattern recognition with neural networks
20
4
1.13 Position-encoding joint transform correlator
24
1
1.14 Phase-representation joint transform correlator
25
2
1.15 Composite filtering with the joint transform correlator
27
1
1.16 Non-zero-order joint transform correlator
28
3
1.17 Summary and conclusions
31
1
References
31
9
2 Hybrid neural networks for nonlinear pattern recognition
40
24
2.1 Introduction
40
1
2.2 Neural network background
41
5
2.2.1 Neural networks for nonlinear transformation
41
3
2.2.2 Black box versus transparent box
44
1
2.2.3 Hidden neurons
45
1
2.2.4 Hybrid neural networks
45
1
2.3 Hybrid optical neural networks
46
5
2.3.1 Basic architecture of the holographic optical neural network system
47
2
2.3.2 Construction of an automatic recording system
49
2
2.4 Construction of holographic optical neural network systems
51
2
2.4.1 Benchtop demonstration system
51
1
2.4.2 Portable demonstration system
51
1
2.4.3 Compact lunchbox demonstration system
52
1
2.5 Holographic optical neural network for pattern recognition
53
8
2.5.1 Hybrid holographic optical neural network hybrid for distortion-invariant pattern recognition
53
5
2.5.2 Lunchbox demonstration of shift-, scale-, and rotation-invariant automatic target recognition
58
3
2.6 Conclusions
61
1
Acknowledgments
62
1
References
62
2
3 Wavelets, optics, and pattern recognition
64
25
3.1 Introduction
64
1
3.2 Historical background
64
2
3.3 Wavelet transforms: definitions and properties
66
3
3.3.1 Continuous wavelet transform
66
1
3.3.2 Discrete wavelet transform and the frame
67
1
3.3.3 Other important wavelet-related concepts
68
1
3.4 Wavelets in general optics
69
3
3.4.1 Wavelets in diffraction
69
1
3.4.2 Wavelets in early vision interpretation
70
1
3.4.3 Wavelets in binocular vision
71
1
3.5 Optical wavelet transforms
72
9
3.5.1 Coherent optical wavelet transforms
73
3
3.5.2 Coherent optical inverse wavelet transforms
76
1
3.5.3 Incoherent optical wavelet transforms
77
1
3.5.4 Other modified wavelet or waveletlike optical transforms
78
1
3.5.5 Advantages and limitations of optical wavelet transforms
78
3
3.6 Optical wavelet transforms for pattern recognition
81
5
3.6.1 Wavelet matched filters
81
1
3.6.2 Adaptive composite wavelet matched filters
82
3
3.6.3 Scale-invariant data classifications
85
1
3.6.4 Feature-based neural wavelet pattern classifier
86
1
3.7 Concluding remarks
86
1
References
86
3
4 Applications of the fractional Fourier transform to optical pattern recognition
89
37
4.1 Preface
89
1
4.2 Introduction
89
3
4.3 Fractional correlator performance analysis
92
8
4.3.1 Performance criteria
92
1
4.3.2 Performance optimization in conventional correlators
93
1
4.3.3 Performance optimization in fractional correlators
93
2
4.3.4 Signal-to-noise ratio comparison between a fractional correlator and a conventional correlator
95
1
4.3.5 Fractional correlator performance with additive colored noise
96
2
4.3.6 Fractional Fourier transform of white noise
98
2
4.4 Fractional correlator with real-time control of the space-invariance property
100
3
4.4.1 Mathematical analysis
100
2
4.4.2 Interpretations
102
1
4.5 Localized fractional processor
103
7
4.5.1 Mathematical definitions
103
3
4.5.2 General applications
106
1
4.5.3 Application for pattern recognition
107
3
4.6 Anamorphic fractional Fourier transform for pattern recognition
110
7
4.6.1 Anamorphic fractional Fourier transform
110
2
4.6.2 Multiple fractional-Fourier-transform filters
112
1
4.6.3 Optical implementation
113
1
4.6.4 Results
114
3
4.7 Fractional joint transform correlator
117
6
4.7.1 Wigner distribution function
117
1
4.7.2 Concept of the joint fractional correlator
118
1
4.7.3 Removal of the extraneous terms
119
4
4.8 Concluding remarks
123
1
Acknowledgments
124
1
References
124
2
5 Optical implementation of mathematical morphology
126
15
5.1 Introduction
126
4
5.1.1 Binary morphology
127
2
5.1.2 Gray-scale morphology
129
1
5.2 Optical morphological processor
130
6
5.2.1 Shadow-cast optical morphological processor
133
1
5.2.2 Reconfigurable optical morphological processor
134
1
5.2.3 Optical morphological processor with a diffraction grating and a shutter spatial light modulator
135
1
5.2.4 Optical morphological processor with a laser source array
136
1
5.3 Miniature system architecture
136
3
5.4 Gray-scale optical morphological processor
139
1
Acknowledgments
139
1
References
140
1
6 Nonlinear optical correlators with improved discrimination capability for object location and recognition
141
30
6.1 Introduction: a review of the theory
141
3
6.2 Nonlinear optical correlators
144
1
6.3 Nonlinear optical correlators with (-k)th-law nonlinearity in the Fourier plane
145
14
6.3.1 Optimal adaptive correlator
149
1
6.3.2 Suboptimal correlators with (-k)th-law nonlinearity and empirical estimation of the image power spectrum
149
10
6.3.3 Phase-only filters and phase-only correlators
159
1
6.4 Nonlinear joint transform correlators
159
10
6.4.1 Logarithmic joint transform correlators
159
5
6.4.2 Nonlinear joint transform correlators with (1/k)th-law nonlinearity
164
3
6.4.3 Binary joint transform correlators
167
2
6.5 Conclusion
169
1
Acknowledgments
169
1
References
169
2
7 Distortion-invariant quadratic filters
171
22
7.1 Introduction
171
1
7.2 Technical background
172
7
7.2.1 Notation
172
1
7.2.2 Bayes decision theory and discriminant functions
172
2
7.2.3 Quadratic filters and their optical implementation
174
3
7.2.4 Normalization of input signals
177
2
7.3 Invariant quadratic filters
179
8
7.3.1 Quadratic filters invariant to a training set
180
2
7.3.2 Quadratic filters invariant to a linear transformation group
182
3
7.3.3 Principal component analysis and invariant feature extraction
185
2
7.4 Performance analysis of invariant quadratic filters
187
5
7.4.1 Assumptions and models for target and clutter
187
2
7.4.2 Fisher ratio of filters
189
1
7.4.3 Relationship between filter performance and key parameters
189
3
References
192
1
8 Composite filter synthesis as applied to pattern recognition
193
28
8.1 Introduction
193
1
8.2 Bipolar composite filter synthesis by simulated annealing
194
10
8.2.1 Simulated annealing algorithm
194
1
8.2.2 Bipolar composite filter synthesis
194
3
8.2.3 Target detection with a bipolar composite filter
197
1
8.2.4 Pattern discrimination capability of a bipolar composite filter
197
3
8.2.5 Noise performance of bipolar composite filter
200
4
8.3 Multilevel composite filter synthesis by simulated annealing
204
2
8.4 Multitarget composite filter synthesis
206
1
8.5 Optical implementation of a bipolar composite filter by a photorefractive crystal hologram
207
3
8.6 Implementation in a joint transform correlator
210
8
8.6.1 Position encoding
211
4
8.6.2 Position-encoding joint transform correlator
215
2
8.6.3 Experimental demonstration
217
1
8.7 Summary
218
1
References
218
3
9 Iterative procedures in electro-optical pattern recognition
221
41
9.1 Introduction
221
1
9.2 Pattern recognition and optimization
222
2
9.3 Iterative optimization algorithms: an overview
224
7
9.3.1 Gradient-descent algorithm
224
1
9.3.2 Hill-climbing procedure
225
1
9.3.3 Simulated annealing
225
1
9.3.4 Genetic algorithms
226
1
9.3.5 Projections-onto-constraint-sets algorithms
227
3
9.3.6 Discussion
230
1
9.4 Detection criteria: information theoretical approach
231
7
9.4.1 Generalized information function
234
1
9.4.2 Performance comparison of different cost functions
235
3
9.5 Hybrid electro-optical implementation
238
6
9.5.1 Performance comparison for various algorithms
241
3
9.6 Applications of projection algorithms
244
5
9.6.1 Class discrimination by a linear correlator
245
2
9.6.2 Class discrimination by the phase-extraction correlator
247
2
9.7 Adaptive procedures for distortion invariance
249
9
9.7.1 Scale measurement procedure
250
3
9.7.2 Filter design
253
5
9.8 Conclusions
258
1
Acknowledgments
258
1
References
258
4
10 Optoelectronic hybrid system for three-dimensional object pattern recognition
262
25
10.1 Introduction
262
1
10.2 Fresnel holographic filter
263
11
10.2.1 Principle of the Fresnel holographic filter
263
3
10.2.2 Experimental demonstrations of a lensless intensity correlator
266
1
10.2.3 White-light intensity correlator with a volume Fresnel holographic filter
267
5
10.2.4 Lensless intensity correlator with high discrimination
272
2
10.2.5 Multiplex intensity correlator with a Fourier-transform holographic filter
274
1
10.3 Serial-code filters
274
3
10.3.1 Principle of serial-code filters
274
2
10.3.2 Digital simulations
276
1
10.4 Cascaded model of neural networks suitable for optical implementation
277
8
10.4.1 Structure and features of the cascaded model
277
2
10.4.2 Learning algorithm of the cascaded model of neural networks
279
2
10.4.3 Gray-level compression of the mask
281
1
10.4.4 Property analysis of the model
282
2
10.4.5 Optoelectronic hybrid system for three-dimensional pattern recognition
284
1
10.5 Conclusion
285
1
References
285
2
11 Applications of photorefractive devices in optical pattern recognition
287
32
11.1 Introduction
287
1
11.2 Fundamentals of the photorefractive effect
287
6
11.2.1 Photorefractive effect
287
2
11.2.2 Two-wave mixing and four-wave mixing
289
2
11.2.3 Multiplexing schemes
291
1
11.2.4 Commonly used photorefractive materials
292
1
11.3 Photorefractive correlators for two-dimensional pattern recognition
293
10
11.3.1 VanderLugt-type correlator
293
6
11.3.2 Joint transform correlator
299
2
11.3.3 Optical wavelet transform correlator
301
2
11.4 Photorefractive processors for radio frequency signal processing
303
4
11.4.1 Photorefractive time-integrating correlator
303
1
11.4.2 Photorefractive radio frequency notch filter
304
3
11.5 Photorefractive novelty filters
307
3
11.6 Implementation of artificial neural networks by photorefractive media
310
3
11.7 Summary
313
1
References
314
5
12 Optical pattern recognition with microlasers
319
26
12.1 Introduction: microlasers, surface-emitting laser diode arrays, or vertical-cavity surface-emitting lasers
319
2
12.1.1 What is a surface-emitting laser?
319
1
12.1.2 What is a microlaser?
320
1
12.1.3 Why microlasers?
321
1
12.2 Status of microlasers
321
7
12.2.1 Low threshold current
321
1
12.2.2 Coherence
322
1
12.2.3 Visible microlasers
323
1
12.2.4 Two-dimensional addressing schemes
324
1
12.2.5 Polarization control
324
1
12.2.6 Multiple wavelengths
324
1
12.2.7 Wavelength tuning
325
1
12.2.8 Efficiency
326
1
12.2.9 Modulation speed
327
1
12.2.10 High-power output
328
1
12.3 Optical correlators with microlasers
328
5
12.3.1 Introduction
328
1
12.3.2 Classification of optical pattern recognition systems
329
2
12.3.3 Multichannel optical correlator based on a mutually incoherent microlaser array
331
1
12.3.4 Compact and robust incoherent correlator
332
1
12.4 Holographic memory readout with microlasers
333
2
12.4.1 Introduction
333
1
12.4.2 Compact and ultrafast holographic memory with a SELDA
333
1
12.4.3 Combined angular and wavelength multiplexing with a two-dimensional MC-SELDA
334
1
12.4.4 Incoherent-coherent multiplexing with a SELDA
335
1
12.5 Microlasers for holographic associative memory
335
3
12.5.1 Introduction
335
1
12.5.2 Holographic neurons
336
1
12.5.3 Microlaser-based holographic associative memory
336
1
12.5.4 Holographic associative memory with time-division multiplexing
337
1
12.6 Integration and packaging
338
2
12.7 Conclusion
340
5
13 Optical properties and applications of bacteriorhodopsin
345
22
13.1 Introduction
345
1
13.2 Optical characterization
345
5
13.3 Wave mixing and phase conjugation
350
2
13.4 Real-time holography
352
2
13.5 Spatial light modulators
354
3
13.6 Optical correlation and pattern recognition
357
2
13.7 Holographic switching and optical interconnection
359
1
13.8 Photoreceptor/artificial retina
359
3
13.9 Optical memory
362
1
13.10 Other applications
362
2
13.11 Conclusions
364
1
Acknowledgment
364
1
References
364
3
14 Liquid-crystal spatial light modulators
367
29
14.1 Introduction
367
3
14.1.1 Building blocks of an optical processor
367
1
14.1.2 Spatial light modulator
368
2
14.2 Liquid crystals
370
3
14.2.1 Liquid-crystal classifications
370
1
14.2.2 Optical and electro-optical properties of twisted nematic liquid crystals
371
2
14.3 Electrically addressed spatial light modulators
373
4
14.3.1 Background
373
3
14.3.2 Liquid-crystal television spatial light modulators
376
1
14.4 Optically addressed spatial light modulators
377
3
14.4.1 Liquid-crystal light valves
378
1
14.4.2 Ferroelectric liquid-crystal spatial light modulators
379
1
14.5 Implementations of a liquid-crystal television in real-time optical processing
380
10
14.5.1 Programmable joint transform correlators
380
3
14.5.2 Real-time phase modulators
383
5
14.5.3 Application in camera
388
2
14.6 The applications, the problems, and the future of liquid-crystal spatial light modulators
390
2
References
392
4
15 Representations of fully complex functions on real-time spatial light modulators
396
37
15.1 Introduction
396
2
15.2 Early methods of complex-valued representation
398
4
15.2.1 Holographic encoding
398
1
15.2.2 Detour-phase-encoding methods
399
3
15.2.3 Multiple spatial light modulators
402
1
15.3 Methods of representing complex values on current spatial light modulators
402
21
15.3.1 Synthesis of Fourier transforms by use of time-integrated spectrum analyzers
402
3
15.3.2 Full-bandwidth methods of encoding: encoding by global optimization
405
1
15.3.3 Full-bandwidth methods of encoding: point-oriented encoding
406
14
15.3.4 Optimality
420
3
15.4 Discussion: scenarios for fully complex representations
423
5
15.4.1 Optical security
424
1
15.4.2 Arbitrary multispot scanning and beam shaping
424
1
15.4.3 Hybrid optoelectronic correlators for autonomous recognition and tracking
424
4
15.5 Summary and conclusion
428
1
References
429
4
Index
433