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Imaging, vision and learning based o...
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Bae, Egil.
Imaging, vision and learning based on optimization and PDEsIVLOPDE, Bergen, Norway, August 29 - September 2, 2016 /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Imaging, vision and learning based on optimization and PDEsedited by Xue-Cheng Tai, Egil Bae, Marius Lysaker.
Reminder of title:
IVLOPDE, Bergen, Norway, August 29 - September 2, 2016 /
other author:
Tai, Xue-Cheng.
Published:
Cham :Springer International Publishing :2018.
Description:
viii, 255 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Image processingCongresses.Mathematics
Online resource:
https://doi.org/10.1007/978-3-319-91274-5
ISBN:
9783319912745$q(electronic bk.)
Imaging, vision and learning based on optimization and PDEsIVLOPDE, Bergen, Norway, August 29 - September 2, 2016 /
Imaging, vision and learning based on optimization and PDEs
IVLOPDE, Bergen, Norway, August 29 - September 2, 2016 /[electronic resource] :edited by Xue-Cheng Tai, Egil Bae, Marius Lysaker. - Cham :Springer International Publishing :2018. - viii, 255 p. :ill., digital ;24 cm. - Mathematics and visualization,1612-3786. - Mathematics and visualization..
This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs) It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms. This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on applications ranging from early image formation to high-level image and data analysis.
ISBN: 9783319912745$q(electronic bk.)
Standard No.: 10.1007/978-3-319-91274-5doiSubjects--Topical Terms:
439192
Image processing
--Mathematics--Congresses.
LC Class. No.: TA1637.5 / .I434 2018
Dewey Class. No.: 621.367028553
Imaging, vision and learning based on optimization and PDEsIVLOPDE, Bergen, Norway, August 29 - September 2, 2016 /
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edited by Xue-Cheng Tai, Egil Bae, Marius Lysaker.
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This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs) It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms. This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on applications ranging from early image formation to high-level image and data analysis.
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