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3D Imaging, Analysis and Applications brings together core topics,
both in terms of well-established fundamental techniques and the
most promising recent techniques in the exciting field of 3D
imaging and analysis. Many similar techniques are being used in a
variety of subject areas and applications and the authors attempt
to unify a range of related ideas. With contributions from high
profile researchers and practitioners, the material presented is
informative and authoritative and represents mainstream work and
opinions within the community. Composed of three sections, the
first examines 3D imaging and shape representation, the second, 3D
shape analysis and processing, and the last section covers 3D
imaging applications. Although 3D Imaging, Analysis and
Applications is primarily a graduate text, aimed at masters-level
and doctoral-level research students, much material is accessible
to final-year undergraduate students. It will also serve as a
reference text for professional academics, people working in
commercial research and development labs and industrial
practitioners.
Robot intelligence has become a major focus of intelligent
robotics. Recent innovation in computational intelligence including
fuzzy learning, neural networks, evolutionary computation and
classical Artificial Intelligence provides sufficient theoretical
and experimental foundations for enabling robots to undertake a
variety of tasks with reasonable performance. This book reflects
the recent advances in the field from an advanced knowledge
processing perspective; there have been attempts to solve knowledge
based information explosion constraints by integrating
computational intelligence in the robotics context.
This textbook is designed for postgraduate studies in the field of
3D Computer Vision. It also provides a useful reference for
industrial practitioners; for example, in the areas of 3D data
capture, computer-aided geometric modelling and industrial quality
assurance. This second edition is a significant upgrade of existing
topics with novel findings. Additionally, it has new material
covering consumer-grade RGB-D cameras, 3D morphable models, deep
learning on 3D datasets, as well as new applications in the 3D
digitization of cultural heritage and the 3D phenotyping of crops.
Overall, the book covers three main areas: 3D imaging, including
passive 3D imaging, active triangulation 3D imaging, active
time-of-flight 3D imaging, consumer RGB-D cameras, and 3D data
representation and visualisation; 3D shape analysis, including
local descriptors, registration, matching, 3D morphable models, and
deep learning on 3D datasets; and 3D applications, including 3D
face recognition, cultural heritage and 3D phenotyping of plants.
3D computer vision is a rapidly advancing area in computer science.
There are many real-world applications that demand high-performance
3D imaging and analysis and, as a result, many new techniques and
commercial products have been developed. However, many challenges
remain on how to analyse the captured data in a way that is
sufficiently fast, robust and accurate for the application. Such
challenges include metrology, semantic segmentation, classification
and recognition. Thus, 3D imaging, analysis and their applications
remain a highly-active research field that will continue to attract
intensive attention from the research community with the ultimate
goal of fully automating the 3D data capture, analysis and
inference pipeline.
This book focuses on the fundamentals and recent advances in RGB-D
imaging as well as covering a range of RGB-D applications. The
topics covered include: data acquisition, data quality assessment,
filling holes, 3D reconstruction, SLAM, multiple depth camera
systems, segmentation, object detection, salience detection, pose
estimation, geometric modelling, fall detection, autonomous
driving, motor rehabilitation therapy, people counting and
cognitive service robots. The availability of cheap RGB-D sensors
has led to an explosion over the last five years in the capture and
application of colour plus depth data. The addition of depth data
to regular RGB images vastly increases the range of applications,
and has resulted in a demand for robust and real-time processing of
RGB-D data. There remain many technical challenges, and RGB-D image
processing is an ongoing research area. This book covers the full
state of the art, and consists of a series of chapters by
internationally renowned experts in the field. Each chapter is
written so as to provide a detailed overview of that topic. RGB-D
Image Analysis and Processing will enable both students and
professional developers alike to quickly get up to speed with
contemporary techniques, and apply RGB-D imaging in their own
projects.
This textbook is designed for postgraduate studies in the field of
3D Computer Vision. It also provides a useful reference for
industrial practitioners; for example, in the areas of 3D data
capture, computer-aided geometric modelling and industrial quality
assurance. This second edition is a significant upgrade of existing
topics with novel findings. Additionally, it has new material
covering consumer-grade RGB-D cameras, 3D morphable models, deep
learning on 3D datasets, as well as new applications in the 3D
digitization of cultural heritage and the 3D phenotyping of crops.
Overall, the book covers three main areas: 3D imaging, including
passive 3D imaging, active triangulation 3D imaging, active
time-of-flight 3D imaging, consumer RGB-D cameras, and 3D data
representation and visualisation; 3D shape analysis, including
local descriptors, registration, matching, 3D morphable models, and
deep learning on 3D datasets; and 3D applications, including 3D
face recognition, cultural heritage and 3D phenotyping of plants.
3D computer vision is a rapidly advancing area in computer science.
There are many real-world applications that demand high-performance
3D imaging and analysis and, as a result, many new techniques and
commercial products have been developed. However, many challenges
remain on how to analyse the captured data in a way that is
sufficiently fast, robust and accurate for the application. Such
challenges include metrology, semantic segmentation, classification
and recognition. Thus, 3D imaging, analysis and their applications
remain a highly-active research field that will continue to attract
intensive attention from the research community with the ultimate
goal of fully automating the 3D data capture, analysis and
inference pipeline.
This book focuses on the fundamentals and recent advances in RGB-D
imaging as well as covering a range of RGB-D applications. The
topics covered include: data acquisition, data quality assessment,
filling holes, 3D reconstruction, SLAM, multiple depth camera
systems, segmentation, object detection, salience detection, pose
estimation, geometric modelling, fall detection, autonomous
driving, motor rehabilitation therapy, people counting and
cognitive service robots. The availability of cheap RGB-D sensors
has led to an explosion over the last five years in the capture and
application of colour plus depth data. The addition of depth data
to regular RGB images vastly increases the range of applications,
and has resulted in a demand for robust and real-time processing of
RGB-D data. There remain many technical challenges, and RGB-D image
processing is an ongoing research area. This book covers the full
state of the art, and consists of a series of chapters by
internationally renowned experts in the field. Each chapter is
written so as to provide a detailed overview of that topic. RGB-D
Image Analysis and Processing will enable both students and
professional developers alike to quickly get up to speed with
contemporary techniques, and apply RGB-D imaging in their own
projects.
3D Imaging, Analysis and Applications brings together core topics,
both in terms of well-established fundamental techniques and the
most promising recent techniques in the exciting field of 3D
imaging and analysis. Many similar techniques are being used in a
variety of subject areas and applications and the authors attempt
to unify a range of related ideas. With contributions from high
profile researchers and practitioners, the material presented is
informative and authoritative and represents mainstream work and
opinions within the community. Composed of three sections, the
first examines 3D imaging and shape representation, the second, 3D
shape analysis and processing, and the last section covers 3D
imaging applications. Although 3D Imaging, Analysis and
Applications is primarily a graduate text, aimed at masters-level
and doctoral-level research students, much material is accessible
to final-year undergraduate students. It will also serve as a
reference text for professional academics, people working in
commercial research and development labs and industrial
practitioners.
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