| Description |
1 online resource |
| Contents |
Intro; Foreword by Huilin Jiang; Foreword by Xiangqun Cui; Preface; Acknowledgements; Contents; 1 Introduction; 1.1 Research Topics of Multidimensional Night-Vision Information Understanding; 1.1.1 Data Analysis and Feature Representation Learning; 1.1.2 Dimension Reduction Classification; 1.1.3 Information Mining; 1.2 Challenges to Multidimensional Night-Vision Data Mining; 1.3 Summary; References; 2 High-SNR Hyperspectral Night-Vision Image Acquisition with Multiplexing; 2.1 Multiplexing Measurement in Hyperspectral Imaging; 2.2 Denoising Theory and HTS |
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2.2.1 Traditional Denoising Theory of HTS2.2.2 Denoising Bound Analysis of HTS with S Matrix; 2.2.3 Denoising Bound Analysis of HTS with H Matrix; 2.3 Spatial Pixel-Multiplexing Coded Spectrometre; 2.3.1 Typical HTS System; 2.3.2 Spatial Pixel-Multiplexing Coded Spectrometre; 2.4 Deconvolution-Resolved Computational Spectrometre; 2.5 Summary; References; 3 Multi-visual Tasks Based on Night-Vision Data Structure and Feature Analysis; 3.1 Infrared Image Super-Resolution via Transformed Self-similarity; 3.1.1 The Introduced Framework of Super-Resolution; 3.1.2 Experimental Results |
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3.2 Hierarchical Superpixel Segmentation Model Based on Vision Data Structure Feature3.2.1 Hierarchical Superpixel Segmentation Model Based on the Histogram Differential Distance; 3.2.2 Experimental Results; 3.3 Structure-Based Saliency in Infrared Images; 3.3.1 The Framework of the Introduced Method; 3.3.2 Experimental Results; 3.4 Summary; References; 4 Feature Classification Based on Manifold Dimension Reduction for Night-Vision Images; 4.1 Methods of Data Reduction and Classification; 4.1.1 New Adaptive Supervised Manifold Learning Algorithms |
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4.1.2 Kernel Maximum Likelihood-Scaled LLE for Night-Vision Images4.2 A New Supervised Manifold Learning Algorithm for Night-Vision Images; 4.2.1 Review of LDA and CMVM; 4.2.2 Introduction of the Algorithm; 4.2.3 Experiments; 4.3 Adaptive and Parameterless LPP for Night-Vision Image Classification; 4.3.1 Review of LPP; 4.3.2 Adaptive and Parameterless LPP (APLPP); 4.3.3 Connections with LDA, LPP, CMVM and MMDA; 4.3.4 Experiments; 4.4 Kernel Maximum Likelihood-Scaled Locally Linear Embedding for Night-Vision Images; 4.4.1 KML Similarity Metric; 4.4.2 KML Outlier-Probability-Scaled LLE (KLLE) |
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4.4.3 Experiments4.4.4 Discussion; 4.5 Summary; References; 5 Night-Vision Data Classification Based on Sparse Representation and Random Subspace; 5.1 Classification Methods; 5.1.1 Research on Classification via Semi-supervised Random Subspace Sparse Representation; 5.1.2 Research on Classification via Semi-supervised Multi-manifold Structure Regularisation (MMSR); 5.2 Night-Vision Image Classification via SSM-RSSR; 5.2.1 Motivation; 5.2.2 SSM-RSSR; 5.2.3 Experiment; 5.3 Night-Vision Image Classification via P-RSSR |
| Summary |
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 |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed January 21, 2019) |
| Subject |
Night vision devices.
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TECHNOLOGY & ENGINEERING -- Technical & Manufacturing Industries & Trades.
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Night vision devices
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| Form |
Electronic book
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| Author |
Han, Jing, author
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Yue, Jiang, author
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| ISBN |
9789811316692 |
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9811316694 |
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9811316686 |
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9789811316685 |
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9789811316708 |
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9811316708 |
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