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E-grāmata: Multi-band Polarization Imaging and Applications

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Multi-band Polarization Imaging and ApplicationsPalielināt
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Multi-band Optical Polarization Imaging and Application introduces the optical multi-band polarization imaging theory and the utilization of the multi-band polarimetric information for detecting the camouflage object and the optical hidden marker, and enhancing the visibility in bad weather and water. The book describes systematically and in detail the basic optical polarimetry theory; provides abundant multi-band polarimetric imaging experiment data; and indicates practical evaluation methods for designing the multi-band polarization imager, for analyzing and modeling the object’s multi-band polarization characteristics, and for enhancing the vision performance in scattering media. This book shows the latest research results of multi-band polarimetric vision, especially in camouflage object detection, optical hidden marker detection and multi-band polarimetric imagery fusion. From this book, readers can get a complete understanding about multi-band polarimetric imaging and its application in different vision tasks. Quan Pan is the Dean of the Automation School of Northwestern Polytechnical University (NPU), China; Yong-Qiang Zhao is an Associate Professor at NPU, China.
1 Introduction
1(12)
1.1 Spectral Imaging
2(3)
1.1.1 Basic Principles
2(2)
1.1.2 Applications of Spectral Imaging Technology
4(1)
1.2 Multi-band Polarization Imaging
5(8)
1.2.1 Development of Multi-band Polarization Imaging
5(2)
1.2.2 Applications of Multi-band Polarization Imaging
7(2)
References
9(4)
2 Polarization Imaging
13(34)
2.1 Electromagnetics and Polarization
13(5)
2.1.1 Maxwell's Equations
13(2)
2.1.2 Polarization Ellipse
15(2)
2.1.3 Fresnel's Equations
17(1)
2.2 Principles of Polarization Imaging
18(3)
2.3 Polarization Imaging for Object Recognition
21(5)
2.3.1 Object Detection and Tracking
22(1)
2.3.2 Edge Detection
22(2)
2.3.3 Object Classification and Recognition
24(2)
2.4 Factors Affecting Polarization Imaging
26(4)
2.4.1 Transmission of Polarized Light
27(2)
2.4.2 Lighting
29(1)
2.5 Reduction of Measurement Errors
30(6)
2.5.1 Multi-angle Measurement
30(2)
2.5.2 Image Filtering
32(1)
2.5.3 Image Fusion
32(4)
2.6 Micro-polarizer Array Imaging
36(11)
2.6.1 Micro-polarizer Array
36(5)
2.6.2 Experiment Results
41(4)
References
45(2)
3 Multi-band Polarization Imaging
47(26)
3.1 Multi-band Polarization Imaging Methods
47(3)
3.1.1 Spectral Band Tuning
47(2)
3.1.2 Polarization Adjustment
49(1)
3.2 Multi-band Polarization Imagers Based on Tunable Filters
50(5)
3.2.1 Liquid Crystal Tunable Filters
51(1)
3.2.2 Acousto-Optic Tunable Filters
52(1)
3.2.3 Liquid Crystal Variable Retarders
52(1)
3.2.4 Typical Multi-band Polarization Imaging Systems
53(2)
3.3 Multi-band Polarization Imagers Using LCTF
55(5)
3.3.1 Implementation of Multi-band Polarization Imaging Systems
55(1)
3.3.2 Main Devices of Multi-band Polarization Imaging Systems
56(1)
3.3.3 Multi-band Polarization Imaging Sensors
57(3)
3.4 Calibration of Multi-band Polarization Imaging Systems
60(3)
3.4.1 Spectral Band Calibration
60(1)
3.4.2 Spectral Radiometric Calibration
61(1)
3.4.3 Mechanical Revolving Filters
61(1)
3.4.4 Polarization Calibration
62(1)
3.5 Reconstruction of Multi-band Polarization Imagery
63(3)
3.5.1 Spectral Inversion Methods
63(2)
3.5.2 Polarization Inversion Based on Empirical Linear Method
65(1)
3.6 Experiment Settings
66(7)
References
71(2)
4 Multi-band Polarization Bidirectional Reflectance Distribution Function
73(38)
4.1 Bidirectional Reflectance Distribution Function
73(8)
4.1.1 Measurement of Bidirectional Reflectance
75(4)
4.1.2 Bidirectional Reflectance Model
79(2)
4.2 Polarization BRDF
81(4)
4.2.1 Polarization BRDF Functions
81(1)
4.2.2 Polarized Radiative Transfer Function
82(3)
4.3 Polarization BRDF Measurements
85(8)
4.3.1 Measuring Principles
85(2)
4.3.2 Outdoor Polarization BRDF Measurement Systems
87(1)
4.3.3 Imaging Data Inversion
87(1)
4.3.4 Experiment Procedures
88(1)
4.3.5 Simulation Results and Analysis
89(4)
4.4 Polarization BRDF Model of Coated Targets
93(7)
4.4.1 Intensity Component Model of Coated Targets
93(2)
4.4.2 DoLP Model of Coated Targets
95(3)
4.4.3 Relationships of Polarization BRDF, Azimuth Angle, and Wavelength
98(2)
4.5 Polarization BRDF Model of the Background
100(11)
4.5.1 Intensity Component Model of the Background
101(3)
4.5.2 DoLP Model of the Background
104(5)
References
109(2)
5 Object Detection with Multi-band Polarization Imaging
111(44)
5.1 Multi-band Polarization Image Analysis
111(1)
5.2 Object Detection
112(12)
5.2.1 Polarization Mixture Model
112(2)
5.2.2 Multivariate Gaussian Distribution
114(1)
5.2.3 Maximum Likelihood Estimation
114(1)
5.2.4 Likelihood Ratio Test
115(4)
5.2.5 Subspace Analysis
119(2)
5.2.6 Stochastic Signal and Entropy
121(1)
5.2.7 Nonparametric Kernel Density Estimation
122(2)
5.3 Anomaly Detection Methods
124(4)
5.3.1 Statistical Hypothesis Model for Anomaly Detection
125(1)
5.3.2 Low Probability Target Detection
126(1)
5.3.3 Classical RX Detection Algorithm
126(1)
5.3.4 Improved RX Detection Algorithm
127(1)
5.4 Fuzzy Integral Fusion Detection of Band Subsets
128(7)
5.4.1 Data Model and Band Subset Division
128(1)
5.4.2 Fuzzy Measure and Fuzzy Integral
129(1)
5.4.3 Nonparametric Fuzzy Membership Functions
130(1)
5.4.4 Fuzzy Density Estimation
131(3)
5.4.5 Experiment Results
134(1)
5.5 Multi-detector Maximum Entropy Fusion Hypothesis Detection
135(6)
5.5.1 Single Likelihood Hypothesis Detection Model
135(1)
5.5.2 Kernel Density Estimation
136(1)
5.5.3 Detector Fusion Based on Maximum Entropy
137(3)
5.5.4 Experiment Results
140(1)
5.6 Fusion Detection Based on Polarization and Spectral Features
141(5)
5.6.1 Fusion Detection Algorithms
141(3)
5.6.2 Experiment Results
144(2)
5.7 Fusion Detection Based on DoLP Modulation
146(9)
5.7.1 DoLP Modulation Fusion
147(3)
5.7.2 False Color Coding Fusion of Spectral and Polarization Images
150(2)
References
152(3)
6 Bio-inspired Multi-band Polarization Imaging
155(22)
6.1 Multi-band Polarization Vision
155(3)
6.1.1 Polarization Vision
155(1)
6.1.2 Compound Eye
156(2)
6.2 Bio-inspired Multi-band Polarization Vision Model
158(3)
6.2.1 Multi-band Polarization Vision Model
158(2)
6.2.2 Heterogeneous Multi-band Polarization Imaging CCD Array Systems
160(1)
6.3 Multi-band Polarization Vision Systems
161(3)
6.3.1 Design of Multi-band Polarization Vision Systems
161(2)
6.3.2 Multi-band Polarization Vision System Software
163(1)
6.4 Spectral and Polarization Information Recovery for FOV Expansion
164(13)
6.4.1 Low-Rank Matrix Recovery
165(1)
6.4.2 Estimation of Missing Spectral and Polarization Information
166(2)
6.4.3 FOV Expansion with Estimated Spectral and Polarization Information
168(1)
6.4.4 Experiment Results
169(5)
References
174(3)
7 3D Reconstruction and Dehazing with Polarization Vision
177(16)
7.1 3D Reconstruction
177(7)
7.1.1 Background
177(2)
7.1.2 3D Reconstruction with Polarization Vision
179(3)
7.1.3 3D Reconstruction Results
182(2)
7.2 Instant Image Dehazing
184(9)
7.2.1 Physical Models and Properties
186(3)
7.2.2 Image Dehazing Using Polarization Information
189(2)
7.2.3 Experiment Results
191(2)
References 193
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