Night Vision Processing and Understanding

274 Pages·2019·14.354 MB·English

by  Lianfa Bai, Jing Han, Jiang Yue

Lianfa Bai · Jing Han · Jiang Yue
Night Vision
Processing and
Understanding
Night Vision Processing and Understanding
Lianfa Bai Jing Han Jiang Yue
(cid:129) (cid:129)
Night Vision Processing
and Understanding
123
Lianfa Bai Jing Han
Schoolof Electronic Schoolof Electronic
andOptical Engineering andOptical Engineering
NanjingUniversity ofScience NanjingUniversity ofScience
andTechnology andTechnology
Nanjing, Jiangsu,China Nanjing, Jiangsu,China
Jiang Yue
National Key Laboratory
of TransientPhysics
NanjingUniversity ofScience
andTechnology
Nanjing, Jiangsu,China
ISBN978-981-13-1668-5 ISBN978-981-13-1669-2 (eBook)
https://doi.org/10.1007/978-981-13-1669-2
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Foreword by Huilin Jiang
Withthecontinuous development ofinformationmining,cognitivecomputingand
other disciplines, this research method that combines intelligent information
understandingandnight-visionimagingtechnologycaneffectivelysimulatehuman
perception mechanisms and processes, and has wide application prospects in the
fieldofnight-visioninformationperception.However,duetothecross-disciplinary
studies, some new developments and achievements are scattered. For the time
being, there is less systematic introduction of books in this area, especially in the
field of night-vision technology.
In order to change this situation and better promote the development of
night-vision information technology, this book focuses on new theories and tech-
nologies currently being developed in the fields of multispectral imaging, dimen-
sionality reduction, data mining, feature classification learning, target recognition,
objectdetection,colorizationalgorithm,etc.,byexploringoptimizationmodelsand
newalgorithms.Itsolvestheapplicationsofperceptioncomputing,mininglearning
and information understanding technologies in night-vision data, and strives to
demonstratethemajorbreakthroughsbroughtbymoderninformationtechnologyto
the night-vision field.
Thisbookisalandmarkandtimelycontributioninthisdirectionasitoffers,for
the first time, detailed descriptions and analysis of this frontier theory and method
of night-vision information processing. Based on the differences in the imaging
environment, target characteristics and imaging methods, it concentrates on mul-
tispectraldata, videodata,etc., andresearches avarietyofinformationminingand
perceptual understanding algorithms, which aims to analyse new processing
methods for multiple types of scenes and targets.
The selection of content fully reflects the main technical connotations and
dynamics of the new field of night vision. Eight chapters include spectral imaging
and coding noise reduction, multi-vision tasks based on data structure and feature
analysis, feature classification based on manifold dimension reduction, data clas-
sificationbasedonsparserepresentationandrandomsubspace,targetdetectionand
recognition based on learning, motion detection and tracking based on
non-learning,colorizationofnight-visionimagesbasedonrulemining,etc.,which
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vi ForewordbyHuilinJiang
cover the comprehensive research areas of artificial intelligence in night vision.
These provided algorithm models and hardware systems can be used as the refer-
ence basis for the general design, algorithm design and hardware design personnel
of the photoelectric system.
In this monograph, Lianfa Bai, Jing Han and Jiang Yue have brought together
their work in Night Vision Processing and Understanding over the past decade to
result in a book that will become a standard for the area. Well done.
Changchun, China Huilin Jiang
Foreword by Xiangqun Cui
Night-vision technology is used to extend human activities beyond the limits of
natural visual ability. For example, it is widely used in the fields for observation,
monitoring and low-light detection. Night-vision research includes low-level light
(LLL)vision,infraredthermalimaging,ultravioletimagingandactivenear-infrared
systems. Multi-source night-vision technology uses the complementarity of
multi-sensor information to solve the problem of incomplete or inaccurate infor-
mation of single imaging sensor. However, the extraction of useful information
from multi-sensor presents new problems. Thus, it is necessary to synthesise
information provided by different sensors. The possible redundancy and contra-
diction of multi-source information can thus be eliminated, allowing users to
describe complete and consistent target information in complex scenes.
Along with the advancement of information mining, cognitive computing and
other disciplines, it is known and believed that combining night-vision technology
and intelligent information understanding can effectively simulate human percep-
tion. Data structure analyses, feature representation learning, dimension reduction
classifications and information mining theories have all been studied extensively.
Thesemodelsandalgorithmshaveobviousadvantagesoverconventional methods
in terms of information understanding. However, there are few studies on feature
mining of night-vision images. For complex night-vision information processing,
the manifold learning, classification and data mining methods still require
investigation.
For practical applications (e.g. security, defence and industry), this research is
needed to solve problems of multispectral target detection or large-number image
classification and recognition. This book compiles an intelligent understanding of
night-vision data under high dimensionality and complexity. Several night-vision
data processing methods, based on feature learning, dimension reduction classifi-
cation and information mining, are explored and studied, providing various new
technical approaches for information detection and understanding.
This book aims to present a systematic and comprehensive introduction of the
latest theories and technologies for various aspects of night-vision information
technologies. Specifically, it covers multispectral imaging, dimension reduction,
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viii ForewordbyXiangqunCui
data mining, feature classification learning, object recognition, object detection,
colorization algorithm, etc. Additionally, the application of the up-to-date opti-
mization models and algorithms (including perception computing, mining learning
and information understanding technologies) is explored to night-vision data and
demonstrates major breakthroughs in the field.
The reader of this book will get both, a fairly comprehensive overview of the
fieldofnight-visionprocessingandunderstanding,reachedinthelasttwodecades.
Iamveryproudtohavehadtheopportunitytofollowthisdevelopmentforalmost
20 years. Enjoy reading this book as I did.
Nanjing, China Xiangqun Cui
Preface
Alongwiththeadvancementofinformationmining,cognitivecomputingandother
disciplines, it is known and believed that combining night-vision technology and
intelligent information understanding can effectively simulate human perception;
however, this topic has not yet been studied systematically.
This book aims to present a systematic and comprehensive introduction of the
latest theories and technologies for various aspects of night-vision information
technologies.Specifically,itcoversmultispectralimaging,dimensionreduction,data
mining, feature classification learning, object recognition, object detection, col-
orization algorithm, etc. Additionally, we explore the application of the up-to-date
optimization models and algorithms (including perception computing, mining
learning and information understanding technologies) to night-vision data and
demonstratemajorbreakthroughsinthefield.
Thebookstructureisasfollows:startingwithapracticalandadvancedapproach,
comprehensively discussing the frontier theory and methods of night-vision infor-
mation processing; we then systematically analyse new night-vision imaging pro-
cessingandperceptionunderstanding-relatedtheoriesandmethods;inthelastpart,
weprovideanalgorithmmodelandahardwaresystemthatcanbeusedforgeneral,
algorithmic or hardware designaspects ofnew systems.
Nanjing, China Lianfa Bai
May 2018 Jing Han
Jiang Yue
ix
Acknowledgements
WethankProfessorYiZhang,ProfessorQianChen,ProfessorWeiqiJin,Professor
JieKongandAssociateProfessorChuangZhangfortheirsupportandconstructive
criticism of the material in this book. Professor Yi Zhang was instrumental in the
process of preparing this document. Without his continuing support, some of the
research presented here would have never been accomplished.
We thank former and current students and collaborators—Zhuang Zhao,
Xiaoyu Chen, Enlai Guo, Linli Xu, Haotian Yu, Jingsong Zhang, Dan Yan,
JianiGao,JunweiZhu,WeiZhang,QinWang,MingzuLiandDongdongChen—
for letting us draw upon their work, thus making this monograph possible.
Research efforts summarised in this book were supported by the National
Natural Science Foundation of China (61231014).
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