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Stochastic Partial Differential Equations by Preusser Tobias (17 results)
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Stochastic Partial Differential Equations for Computer Vision with Uncertain Data (Synthesis Lectures on Visual Computing)
Published by Morgan & Claypool Publishers, 2017
ISBN 10: 1681731436 ISBN 13: 9781681731438
Language: English
US$ 13.68
Convert currencyUS$ 20.12 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketSoft cover. Condition: As New. This book has been in storage since publication and is unread. Hence the description as new .
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Stochastic Partial Differential Equations for Computer Vision with Uncertain Data
Published by Morgan & Claypool "Synthesis Lectures on Visual Computing" series, [Kentfield CA], 2017
ISBN 10: 1681731436 ISBN 13: 9781681731438
Seller: Philip Gibbons Books, Newcastle Emlyn, United Kingdom
Seller rating 5 out of 5 stars
First Edition
US$ 9.51
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Add to basketTrade Paperback. First edition. ||| A digital text - this copy printed in the UK. quarto-size, 164 pages (xvi, 148), mathematical notatioon throughout, diagrams, half-tone illustrations, bibliography: publisher's white card covers. ||| CONDITION: New book awaiting dispatch from UK. My pictures always show the actual book ||| SHIPPING: UK Second-Class postage is free.
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US$ 51.77
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US$ 55.33
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US$ 53.99
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Stochastic Partial Differential Equations for Computer Vision With Uncertain Data
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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US$ 57.04
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Stochastic Partial Differential Equations for Computer Vision with Uncertain Data (Synthesis Lectures on Visual Computing: Computer Graphics, Animation, Computational Photography and Imaging)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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US$ 62.41
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US$ 58.17
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Stochastic Partial Differential Equations for Computer Vision With Uncertain Data
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Stochastic Partial Differential Equations for Computer Vision with Uncertain Data
Published by Springer International Publishing, 2017
ISBN 10: 3031014669 ISBN 13: 9783031014666
Language: English
US$ 55.35
Convert currencyUS$ 33.03 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In image processing and computer vision applications such as medical or scientific image data analysis, as well as in industrial scenarios, images are used as input measurement data. It is good scientific practice that proper measurements must be equipped with error and uncertainty estimates. For many applications, not only the measured values but also their errors and uncertainties, should be-and more and more frequently are-taken into account for further processing. This error and uncertainty propagation must be done for every processing step such that the final result comes with a reliable precision estimate. The goal of this book is to introduce the reader to the recent advances from the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). It presents a concept with which error propagation and sensitivity analysis can be formulated with a set of basic operations.The approach discussed in this book has the potential for application in all areas of quantitative computer vision, image processing, and image analysis. In particular, it might help medical imaging finally become a scientific discipline that is characterized by the classical paradigms of observation, measurement, and error awareness. This book is comprised of eight chapters. After an introduction to the goals of the book (Chapter 1), we present a brief review of PDEs and their numerical treatment (Chapter 2), PDE-based image processing (Chapter 3), and the numerics of stochastic PDEs (Chapter 4). We then proceed to define the concept of stochastic images (Chapter 5), describe how to accomplish image processing and computer vision with stochastic images (Chapter 6), and demonstrate the use of these principles for accomplishing sensitivity analysis (Chapter 7). Chapter 8 concludes the book and highlights new research topics for the future.
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Stochastic Partial Differential Equations for Computer Vision with Uncertain Data (Synthesis Lectures on Visual Computing)
Published by Morgan & Claypool Publishers, 2017
ISBN 10: 1681731436 ISBN 13: 9781681731438
Language: English
US$ 195.51
Convert currencyUS$ 33.20 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketpaperback. Condition: Like New. Like New. book.
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Stochastic Partial Differential Equations for Computer Vision with Uncertain Data
Published by Springer International Publishing Jul 2017, 2017
ISBN 10: 3031014669 ISBN 13: 9783031014666
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
US$ 55.35
Convert currencyUS$ 25.68 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In image processing and computer vision applications such as medical or scientific image data analysis, as well as in industrial scenarios, images are used as input measurement data. It is good scientific practice that proper measurements must be equipped with error and uncertainty estimates. For many applications, not only the measured values but also their errors and uncertainties, should be-and more and more frequently are-taken into account for further processing. This error and uncertainty propagation must be done for every processing step such that the final result comes with a reliable precision estimate. The goal of this book is to introduce the reader to the recent advances from the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). It presents a concept with which error propagation and sensitivity analysis can be formulated with a set of basic operations. The approach discussed in this book has the potential for application in all areas of quantitative computer vision, image processing, and image analysis. In particular, it might help medical imaging finally become a scientific discipline that is characterized by the classical paradigms of observation, measurement, and error awareness. This book is comprised of eight chapters. After an introduction to the goals of the book (Chapter 1), we present a brief review of PDEs and their numerical treatment (Chapter 2), PDE-based image processing (Chapter 3), and the numerics of stochastic PDEs (Chapter 4). We then proceed to define the concept of stochastic images (Chapter 5), describe how to accomplish image processing and computer vision with stochastic images (Chapter 6), and demonstrate the use of these principles for accomplishing sensitivity analysis (Chapter 7). Chapter 8 concludes the book and highlights new research topics for the future. 164 pp. Englisch.
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US$ 75.27
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US$ 81.24
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Stochastic Partial Differential Equations for Computer Vision with Uncertain Data
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2017
ISBN 10: 3031014669 ISBN 13: 9783031014666
Language: English
US$ 49.40
Convert currencyUS$ 54.69 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In image processing and computer vision applications such as medical or scientific image data analysis, as well as in industrial scenarios, images are used as input measurement data. It is good scientific practice that proper measurements must be equipped w.
