Overview
- Illustrates the potential of this powerful hyperspectral imaging and high-dimensional night vision data processing
- Helps readers learn how to use spectral information in the visible and infrared waveband to solve challenging problems in real-life applications and discover how general image processing is connected to night vision imaging
- Provides a comprehensive discussion of data mining and feature learning in state-of-the-art night vision imaging theory and methods
- Describes the development of the international status and the latest results in the field, and offers an overview of research in multi-source night vision image processing
- Explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs
- Presents the intrinsic ideas behind spectral feature selection and understanding, and demonstrates how to derive theoretical foundations, find connections to other popular algorithms, and construct practical systems
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Table of contents (8 chapters)
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Front Matter
Keywords
About this book
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.
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Night Vision Processing and Understanding
Authors: Lianfa Bai, Jing Han, Jiang Yue
DOI: https://doi.org/10.1007/978-981-13-1669-2
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2019
Hardcover ISBN: 978-981-13-1668-5Published: 24 January 2019
eBook ISBN: 978-981-13-1669-2Published: 11 January 2019
Edition Number: 1
Number of Pages: XVI, 266
Number of Illustrations: 54 b/w illustrations, 123 illustrations in colour
Topics: Image Processing and Computer Vision, Data Mining and Knowledge Discovery, Global Analysis and Analysis on Manifolds, Algorithms, Artificial Intelligence
Industry Sectors: Aerospace, Automotive, Biotechnology, Chemical Manufacturing, Consumer Packaged Goods, Electronics, Energy, Utilities & Environment, Engineering, Health & Hospitals, IT & Software, Law, Materials & Steel, Oil, Gas & Geosciences, Pharma, Telecommunications