Augmented Vision Perception in Infrared: Algorithms and Applied Systems

Front Cover
Riad I. Hammoud
Springer Science & Business Media, Jan 1, 2009 - Technology & Engineering - 471 pages
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Throughout much of machine vision’s early years the infrared imagery has suffered from return on investment despite its advantages over visual counterparts. Recently, the ?scal momentum has switched in favor of both manufacturers and practitioners of infrared technology as a result of today’s rising security and safety challenges and advances in thermographic sensors and their continuous drop in costs. This yielded a great impetus in achieving ever better performance in remote surveillance, object recognition, guidance, noncontact medical measurements, and more. The purpose of this book is to draw attention to recent successful efforts made on merging computer vision applications (nonmilitary only) and nonvisual imagery, as well as to ?ll in the need in the literature for an up-to-date convenient reference on machine vision and infrared technologies. Augmented Perception in Infrared provides a comprehensive review of recent deployment of infrared sensors in modern applications of computer vision, along with in-depth description of the world’s best machine vision algorithms and intel- gent analytics. Its topics encompass many disciplines of machine vision, including remote sensing, automatic target detection and recognition, background modeling and image segmentation, object tracking, face and facial expression recognition, - variant shape characterization, disparate sensors fusion, noncontact physiological measurements, night vision, and target classi?cation. Its application scope includes homeland security, public transportation, surveillance, medical, and military. Mo- over, this book emphasizes the merging of the aforementioned machine perception applications and nonvisual imaging in intensi?ed, near infrared, thermal infrared, laser, polarimetric, and hyperspectral bands.
 

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Contents

Infrared Thermography for Land Mine Detection
3
11 Introduction
4
12 Thermal Modeling of the Soil Including Shallowly Buried Objects
7
122 Estimation of the Soil Thermal Diffusivity
12
123 Estimation of the SoilSurface Boundary Condition
13
13 Inverse Problem Setting for Buried Object Detection
14
132 Simplification of the Inverse Problem
15
133 A TwoStep Method for Solving the Simplified Inverse Problem
16
Chapters References
238
Runway Positioning and Moving Object Detection Prior to Landing
241
111 Introduction
244
113 Enhanced Vision System Framework
246
114 Runway Segmentation Process
247
1141 Adaptive Binarization
248
1142 Runway Quadrilateral Fitting Algorithms
249
11421 Adaptive Hough Fitting
250

14 Experimental Data and Processing Chain
18
142 Preprocessing
20
143 Estimation of the Soil Thermal Parameters
21
145 Effect of Mine Properties and Soil Type on the SoilSurface Thermal Contrast
23
1452 Effect of the Mine Height
24
1453 Effect of Soil Type
26
146 Anomaly Detection and Reduction
27
147 Reconstruction of the Geometric and Thermal Properties
30
148 Classification of the Detected Anomalies
32
Chapters References
34
Passive Polarimetric Information Processing for Target Classification
37
22 Theory
38
221 Background
39
2211 Polarization
40
2212 Refraction
42
222 Surface Normal from Geometry
43
2221 Special Case
44
2222 General Case
45
223 Invariants of Polarization Transformations
47
2231 Probabilistic Representation
48
23 Simulation and Experimental Results
49
231 Surface Properties and Geometry
50
2312 DualSensor Example
51
2313 Laboratory Experiments
52
232 Polarimetric Invariants
55
24 Summary and Conclusions
56
Chapters References
61
Vehicle Classification in Infrared Video Using the Sequential Probability Ratio Test
62
32 OneClass Classification
64
33 Object Classification
65
331 ShapeBased Classification
66
332 MotionBased Classification
68
35 SingleLook Vehicle Classifier
70
352 Signature Extraction
71
36 Multilook Sequential Classifier
74
37 Data and Results
76
372 Algorithm Parameters
77
373 Results
78
38 Conclusions and Future Work
80
Scaling the Decision Boundaries for Handling Dependence
81
Chapters References
83
Multiresolution Approach for Noncontact Measurements of Arterial Pulse Using Thermal Imaging
87
411 Related Work
88
42 Thermal Imaging
89
43 Thermal Radiation
90
44 Anatomy
91
45 Multiscale Image Decomposition
94
451 Multiresolution Analysis
95
46 Continuous Wavelet Analysis
97
461 Continuous Wavelet Transformation
98
462 Sample CWA
100
463 Periodicity Detection PD Algorithm
103
47 Method
104
471 Thermal Delegates of the Arterial Pulse
105
472 Measurement of Arterial Pulse
107
48 Experiments and Results
108
49 Conclusions
111
Chapters References
112
Coalitional Tracker for Deception Detection in Thermal Imagery
113
511 Prior Work
114
52 Tracking Methodology
115
522 The Coalitional Game
117
523 Target State Estimation
121
524 Configuration of Tracking Network
123
53 Experimental Design
125
532 Design of Thermal Infrared Experiment
126
542 Results of Thermal Infrared Experiment
128
543 Results of Visual Experiment
130
55 Application Perspective
131
56 Conclusion
135
Chapters References
136
Thermal Infrared Imaging in Early Breast Cancer Detection
138
611 Breast Cancer and Imaging Modalities
140
62 PathophysiologicalBased Understanding of IR Imaging
142
63 Smart ImageProcessing Approaches to IR Images
143
631 Smart Image Enhancement and Restoration Algorithms
144
632 Asymmetry Analysis
145
634 The ThermalElectric Analog
146
64 NewGeneration Infrared Technologies
148
65 Summary
149
Hyperspectral Image Analysis for Skin Tumor Detection
155
72 Hyperspectral Fluorescence Imaging
157
722 Hyperspectral Imaging Experiment
158
73 Spectral Signatures of Normal and Malignant Skin Tissues
162
74 Spectral Signature Classification
165
743 Experiment Results
168
75 Conclusions
170
Spectral Screened Orthogonal Subspace Projection for Target Detection in Hyperspectral Imagery
172
81 Introduction
174
82 Spectral Screening
175
822 Spectral Distance Measures
177
8222 Spectral Information Divergence
178
831 Maximum Spectral Screening
179
832 Minimum Spectral Screening
180
84 Target Detection Using Spectral Screening
181
842 Kernel Orthogonal Subspace Projection
182
843 Spectral Screening and Orthogonal Projection Target Detection
183
85 Experiments
184
852 SOC Data
190
86 Conclusions
192
Chapters References
193
Face Recognition in LowLight Environments Using Fusion of Thermal Infrared and Intensified Imagery
197
92 Image Intensification and Thermal Imaging
199
94 Experimental Results and Discussion
202
95 Conclusions
210
Facial Expression Recognition in Nonvisual Imagery
212
102 Facial Expression Recognition
214
1021 FER in Thermal Images
216
1041 Face Localization
218
10421 Interest Point Clustering
220
10431 Principal Component Analysis
221
1044 Facial Expression Classification
222
1046 Experimental Setup
223
10463 Testing
224
1047 Conclusions
225
1051 Outline of the Approach
226
10511 Main Research Contributions
227
1053 Texture Analysis and the GrayLevel Cooccurrence Matrix
228
1054 Genetic Algorithm for Visual Learning
230
10541 ROI Selection
231
10542 Feature Extraction
232
10544 Fitness Evaluation
233
1055 Experimental Results
234
10552 Approach Evaluation
235
106 Discussion and Future Work
237
11423 Random Sample Consensus Line Regression for Runway Fitting
251
115 Dynamic Stabilization of Runway Detection
252
1152 ResidueBased Estimate
253
1153 Kalman Filter Estimate
254
116 Obstacle Detection Approach
258
11611 Feature Point Correspondence
259
1162 Motion Detection Module
262
117 Experimental Results
264
1172 Obstacle Detection
265
118 Conclusion
266
Chapters References
268
Moving Object Localization in Thermal Imagery by ForwardBackward Motion History Images
271
122 Related Work
272
123 Moving Object Localization by MHIs
275
1231 Preprocessing
277
1232 Motion History Image Generation
278
1233 Object Localization
281
124 Experiment Analysis
283
1243 Experiment Result
285
125 Conclusion
289
Chapters References
291
FeatureLevel Fusion for Object Segmentation Using Mutual Information
295
1311 Alternate Fusion Methodologies
297
1312 Outline
298
132 Related Work
299
133 Contour Features
300
1341 Preliminaries
301
1342 Contour Affinity
302
1343 Estimation of Conditional Probability Using Contour Affinity
304
1344 Computing Mutual Information
305
135 Contour Feature Selection Using Mutual Information
306
136 Experiments
309
1361 Quantitative Evaluation
311
Comparison Against Other Methods
314
1362 Discussion
315
137 Summary
318
Chapters References
319
Registering Multimodal Imagery with Occluding Objects Using Mutual Information Application to Stereo Tracking of Humans
321
142 Related Research
322
143 Multimodal Test Bed
324
145 Multimodal Stereo Using Primitive Matching
329
1451 Image Acquisition and Foreground Extraction
331
1453 Disparity Voting with Sliding Correspondence Windows
332
146 Experimental Analysis and Discussion
334
1461 Algorithmic Evaluation
335
1462 Comparative Evaluation Using Ground Truth Disparity Values
337
1463 Comparative Assessment of Registration Algorithms with Nonideal Segmentation
340
Basic Framework and Experimental Study
341
148 Summary and Concluding Remarks
345
Chapters References
346
ThermalVisible Video Fusion for Moving Target Tracking and Pedestrian Motion Analysis and Classification
348
152 Related Work
350
1521 Tracking Review
351
1522 Motionbased Classification Review
352
154 System Overview
353
Observations and States
355
15532 Likelihoods
356
1554 JumpDiffusion Dynamics
358
156 SymmetryBased Pedestrian Classification
359
157 Experimental Results
361
1572 Classification
362
15731 Across Cameras
364
15732 Across Time
365
158 Conclusion
367
Multi StereoBased Pedestrian Detection by Daylight and FarInfrared Cameras
371
162 The Approach
372
163 Detection of Areas of Attention
373
1631 RunTime Calibration
374
1632 FIROnly Processing
375
16322 Edge Detection
377
1633 Independent Tetravision Obstacle Detection
379
1634 Merge and Rough Filtering Step
380
16341 Bounding Box Registration
381
16342 CrossDomain Fusion of Results
382
164 SymmetryBased Refinement
383
165 Human Shape Detection
386
1651 Active Contour Models
387
1652 Probabilistic Models
390
1653 Head Detection
392
16532 Probabilistic Model
393
16533 Warm Area Search
394
166 Acquisition
395
1662 Cameras Calibration
396
168 Conclusions
399
RealTime Detection and Tracking of Multiple People in Laser Scan Frames
405
171 Introduction
406
172 Related Work
408
173 Sensor System Architecture and Data Collection
410
1732 System Architecture and Data Collection
411
174 Feature Extraction and People Detection
414
1743 Evaluations of Detection Algorithm
417
175 Bayesian Tracking and Data Association
419
17511 State Space and Observation Space
420
17512 The Motion Model
421
1752 Independent Tracking Using Kalman Filters
422
1753 Joint Tracking of Multiple Targets Using RBMCDAF
424
17532 RaoBlackwellized Monte Carlo Data Association
425
17533 Mutual Correlation Detection and Modeling
427
176 Data Association with Assistance of Visual Data
429
1761 Sensor Configuration and Data Collection
430
1763 Visual Representation and Similarity Distance
432
1764 Approach Summary
434
1765 Evaluations of VisualAssisted Tracking Results
435
177 Conclusions
437
Chapters References
438
On Boosted and Adaptive Particle Filters for AffineInvariant Target Tracking in Infrared Imagery
440
182 Problem Formulation
444
1822 AffineInvariant Target Model
445
1823 Likelihood Function of the Observations
446
183 ParticleFiltering Theory
448
1832 Basic Particle Filtering
449
1833 Recent Improvements to Particle Filters
450
18332 BottomUp Methods
451
1834 Challenges of AffineInvariant Tracking
452
1841 Track Quality Indicator
453
1842 BAPF Algorithm
454
18423 BAPF Implementation
455
18424 Comments on BAPF
456
18431 AAPF Implementation
457
18432 Comments on AAPF
458
1844 Additional Remarks on BAPF and AAPF
460
186 Conclusions
464
Chapters References
465
Index
467
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