3D Imaging, Analysis and Applications (PDF)
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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.
Nick Pears is an Associate Professor in Computer Science at the University of York, UK. He works in Computer Vision and Machine Learning - particularly medical, biometric and creative applications that employ statistical 3D shape modelling. He was recently awarded a Senior Research Fellowship award by the Royal Academy of Engineering (UK) and has had his recent work supported by two Google Faculty Awards. Previously he has worked at the University of Cambridge and University of Oxford, UK, and he gained his BSc in Engineering Science and his PhD in Robotics from the University of Durham, UK.
Paul L. Rosin is a Professor at the School of Computer Science & Informatics, Cardiff University. He has worked on many aspects of
Patrik Huber is an Assistant Professor at the Department of Computer Science of the University of York, UK. His research is focused on computer vision, and in particular, around 3D face reconstruction and 3D face modelling from images and videos. He is interested in statistical face models, face tracking and analysis, inverse rendering, and combining deep learning with 3D face models, and has co-authored several publications in top ranked international conferences and journals in the field. In particular, he is interested in applications of his research in health care and in the creative industries. In 2018, he founded 4dface Ltd, a research-focused small-business specialising in 3D face modelling and 3D face avatars for professional applications. Previously he has worked as a Research Fellow at the Centre for Vision, Speech and Signal Processing of the University of Surrey, UK, where he obtained his PhD degree in 2017 with his work on real-time 3D face model fitting. Before that, he obtained his BSc. and MSc. from the University of Basel, Switzerland.
- 2020, 2nd ed. 2020, 736 Seiten, Englisch
- Herausgegeben: Yonghuai Liu, Nick Pears, Paul L. Rosin, Patrik Huber
- Verlag: Springer International Publishing
- ISBN-10: 3030440702
- ISBN-13: 9783030440701
- Erscheinungsdatum: 11.09.2020
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- Grösse: 23 MB
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