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E-grāmata: Night Vision Processing and Understanding

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  • Formāts: PDF+DRM
  • Izdošanas datums: 11-Jan-2019
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811316692
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Night Vision Processing and UnderstandingPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 11-Jan-2019
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811316692
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This book systematically analyses the latest insights into night vision imaging processing and perceptual understanding as well as related theories and methods. The algorithm model and hardware system provided can be used as the reference basis for the general design, algorithm design and hardware design of photoelectric systems. Focusing on the differences in the imaging environment, target characteristics, and imaging methods, this book discusses multi-spectral and video data, and investigates a variety of information mining and perceptual understanding algorithms. It also assesses different processing methods for multiple types of scenes and targets.
Taking into account the needs of scientists and technicians engaged in night vision optoelectronic imaging detection research, the book incorporates the latest international technical methods. The content fully reflects the technical significance and dynamics of the new field of night vision. The eight chapters cover topics including multispectral imaging, Hadamard transform spectrometry; dimensionality reduction, data mining, data analysis, feature classification, feature learning; computer vision, image understanding, target recognition, object detection and colorization algorithms, which reflect the main areas of research in artificial intelligence in night vision.

The book enables readers to grasp the novelty and practicality of the field and to develop their ability to connect theory with real-world applications. It also provides the necessary foundation to allow them to conduct research in the field and adapt to new technological developments in the future.

1 Introduction
1(16)
1.1 Research Topics of Multidimensional Night-Vision Information Understanding
1(9)
1.1.1 Data Analysis and Feature Representation Learning
2(3)
1.1.2 Dimension Reduction Classification
5(3)
1.1.3 Information Mining
8(2)
1.2 Challenges to Multidimensional Night-Vision Data Mining
10(2)
1.3 Summary
12(5)
References
12(5)
2 High-SNR Hyperspectral Night-Vision Image Acquisition with Multiplexing
17(28)
2.1 Multiplexing Measurement in Hyperspectral Imaging
17(2)
2.2 Denoising Theory and HTS
19(8)
2.2.1 Traditional Denoising Theory of HTS
19(3)
2.2.2 Denoising Bound Analysis of HTS with S Matrix
22(3)
2.2.3 Denoising Bound Analysis of HTS with H Matrix
25(2)
2.3 Spatial Pixel-Multiplexing Coded Spectrometre
27(8)
2.3.1 Typical HTS System
28(1)
2.3.2 Spatial Pixel-Multiplexing Coded Spectrometre
29(6)
2.4 Deconvolution-Resolved Computational Spectrometre
35(6)
2.5 Summary
41(4)
References
42(3)
3 Multi-visual Tasks Based on Night-Vision Data Structure and Feature Analysis
45(42)
3.1 Infrared Image Super-Resolution via Transformed Self-similarity
45(12)
3.1.1 The Introduced Framework of Super-Resolution
47(3)
3.1.2 Experimental Results
50(7)
3.2 Hierarchical Superpixel Segmentation Model Based on Vision Data Structure Feature
57(13)
3.2.1 Hierarchical Superpixel Segmentation Model Based on the Histogram Differential Distance
58(4)
3.2.2 Experimental Results
62(8)
3.3 Structure-Based Saliency in Infrared Images
70(11)
3.3.1 The Framework of the Introduced Method
71(6)
3.3.2 Experimental Results
77(4)
3.4 Summary
81(6)
References
82(5)
4 Feature Classification Based on Manifold Dimension Reduction for Night-Vision Images
87(40)
4.1 Methods of Data Reduction and Classification
87(3)
4.1.1 New Adaptive Supervised Manifold Learning Algorithms
87(2)
4.1.2 Kernel Maximum Likelihood-Scaled LLE for Night-Vision Images
89(1)
4.2 A New Supervised Manifold Learning Algorithm for Night-Vision Images
90(8)
4.2.1 Review of LDA and CMVM
90(2)
4.2.2 Introduction of the Algorithm
92(2)
4.2.3 Experiments
94(4)
4.3 Adaptive and Parameterless LPP for Night-Vision Image Classification
98(11)
4.3.1 Review of LPP
98(1)
4.3.2 Adaptive and Parameterless LPP (APLPP)
99(4)
4.3.3 Connections with LDA, LPP, CMVM and MMDA
103(1)
4.3.4 Experiments
104(5)
4.4 Kernel Maximum Likelihood-Scaled Locally Linear Embedding for Night-Vision Images
109(14)
4.4.1 KML Similarity Metric
109(3)
4.4.2 KML Outlier-Probability-Scaled LLE (KLLE)
112(1)
4.4.3 Experiments
113(7)
4.4.4 Discussion
120(3)
4.5 Summary
123(4)
References
124(3)
5 Night-Vision Data Classification Based on Sparse Representation and Random Subspace
127(48)
5.1 Classification Methods
127(3)
5.1.1 Research on Classification via Semi-supervised Random Subspace Sparse Representation
128(1)
5.1.2 Research on Classification via Semi-supervised Multi-manifold Structure Regularisation (MMSR)
129(1)
5.2 Night-Vision Image Classification via SSM-RSSR
130(16)
5.2.1 Motivation
130(2)
5.2.2 SSM-RSSR
132(4)
5.2.3 Experiment
136(10)
5.3 Night-Vision Image Classification via P-RSSR
146(13)
5.3.1 Probability Semi-supervised Random Subspace Sparse Representation (P-RSSR)
146(5)
5.3.2 Experiment
151(8)
5.4 Night-Vision Image Classification via MMSR
159(10)
5.4.1 Mr
159(1)
5.4.2 Multi-manifold Structure Regularisation (MMSR)
159(5)
5.4.3 Experiment
164(5)
5.5 Summary
169(6)
References
171(4)
6 Learning-Based Night-Vision Image Recognition and Object Detection
175(26)
6.1 Machine Learning in IM
175(2)
6.1.1 Autoencoders
176(1)
6.1.2 Feature Extraction and Classifier
176(1)
6.2 Lossless-Constraint Denoising Autoencoder Based Night-Vision Image Recognition
177(11)
6.2.1 Denoising and Sparse Autoencoders
177(2)
6.2.2 LDAE
179(3)
6.2.3 Experimental Comparison
182(6)
6.3 Integrative Embedded Night-Vision Target Detection System with DPM
188(9)
6.3.1 Algorithm and Implementation of Detection System
188(6)
6.3.2 Experiments and Evaluation
194(3)
6.4 Summary
197(4)
References
198(3)
7 Non-Iearning-Based Motion Cognitive Detection and Self-adaptable Tracking for Night-Vision Videos
201(34)
7.1 Target Detection and Tracking Methods
201(3)
7.1.1 Investigation of Infrared Small-Target Detection
201(1)
7.1.2 Moving Object Detection Based on Non-learning
202(1)
7.1.3 Researches on Target Tracking Technology
203(1)
7.2 Infrared Small Object Detection Using Sparse Error and Structure Difference
204(4)
7.2.1 Framework of Object Detection
204(2)
7.2.2 Experimental Results
206(2)
7.3 Adaptive Mean Shift Algorithm Based on LARK Feature for Infrared Image
208(9)
7.3.1 Tracking Model Based on Global LARK Feature Matching and CAMSHTFT
208(3)
7.3.2 Target Tracking Algorithm Based on Local LARK Feature Statistical Matching
211(1)
7.3.3 Experiment and Analysis
212(5)
7.4 An SMSM Model for Human Action Detection
217(15)
7.4.1 Technical Details of the SMSM Model
219(5)
7.4.2 Experiments Analysis
224(8)
7.5 Summary
232(3)
References
232(3)
8 Colourization of Low-Light-Level Images Based on Rule Mining
235
8.1 Research on Colorization of Low-Light-Level Images
235(1)
8.2 Carm
236(10)
8.2.1 Summary of the Principle of the Algorithm
236(2)
8.2.2 Mining of Multi-attribute Association Rules in Grayscale Images
238(1)
8.2.3 Colorization of Grayscale Images Based on Rule Mapping
239(1)
8.2.4 Analysis and Comparison of Experimental Results
240(6)
8.3 Multi-sparse Dictionary Colorization Algorithm Based on Feature Classification and Detail Enhancement
246(17)
8.3.1 Colorization Based on a Single Dictionary
247(1)
8.3.2 Multi-sparse Dictionary Colorization Algorithm for Night-Vision Images, Based on Feature Classification and Detail Enhancement
248(6)
8.3.3 Experiment and Analysis
254(9)
8.4 Summary
263
References
265
Lianfa Bai: His interests include photoelectron imaging, multispectral imaging, image processing and computer vision, and intelligent applications of spectral imaging. He has also pursued unique research on low level light visible infrared (near-infrared, medium-wave infrared, long-wave infrared) imaging and understanding. He has published more than 130 relevant papers, including in Optics Letters, IEEE Transactions, and PLOS ONE.







Jing Han: Her research is mainly based on system imaging characteristics, studying spectral data mining, visual modelling and optimization, and non-training/small sample training classification to improve the computational efficiency and robustness of multidimensional images, and to promote the practicality of multi-source multispectral imaging systems.







Jiang Yue: He is currently working on new technologies to boost the SNR of high-dimension data, including acquisition methods, and data denoising algorithms. In particularhe is dealing with two problems: developing high SNR coding snapshot measurements and finding reversible denoising transformations. He and his co-operators have published more than 15 relevant papers, including in Optics Letters, IEEE Transactions on Image Processing, and Applied Physics B.
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