عنوان

Night vision processing and understanding /

(Number (ISBN

9789811316685

(Number (ISBN

9789811316692

(Number (ISBN

9789811316708

(Number (ISBN

9811316686

(Number (ISBN

9811316694

(Number (ISBN

9811316708

Erroneous ISBN

9789811316685

Title Proper

Night vision processing and understanding /

General Material Designation

[Book]

First Statement of Responsibility

Lianfa Bai, Jing Han, Jiang Yue.

Place of Publication, Distribution, etc.

Singapore :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2019.

Specific Material Designation and Extent of Item

1 online resource

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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)

Text of Note

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

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Text of Note

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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9789811316692

International Standard Book Number

9789811316685

International Standard Book Number

9789811316708

Night vision devices.

Night vision devices.

TECHNOLOGY & ENGINEERING-- Technical & Manufacturing Industries & Trades.

TEC-- 040000

UYQV

UYT

UYT

Number

Edition

Class number

Bai, Lianfa

Han, Jing

Yue, Jiang

Date of Transaction

20200823225126.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y