Night vision processing and understanding. (2019)

• Record Type: Book
• Further Information: Note: Lianfa Bai, Jing Han, Jiang Yue.
• Authors: Bai, Lianfa; Han, Jing; Yue, Jiang
• 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 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 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 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) 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
• Publisher Details: Singapore : Springer
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 681/.4; Night vision devices; TECHNOLOGY & ENGINEERING / Technical & Manufacturing Industries & Trades; Night vision devices; Electronic books
• Languages: English
• ISBNs: 9789811316692; 9811316694; 9811316686; 9789811316685
• Related ISBNs: 9789811316685
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed January 21, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.