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Efficient Algorithms for Global Optimization Methods in Computer Vision : International Dagstuhl Seminar, Dagstuhl Castle, Germany, November 20-25, 2011, Revised Selected Papers / edited by Andrés Bruhn, Thomas Pock, Xue-Cheng Tai.

SpringerLink Books Computer Science (2011-2024) Available online

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Format:
Book
Contributor:
Bruhn, Andrés, Editor.
Pock, Thomas, Editor.
Tai, Xue-Cheng, Editor.
SpringerLink (Online service)
Series:
Computer Science (SpringerNature-11645)
LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 8293
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 8293
Language:
English
Subjects (All):
Computer vision.
Algorithms.
Numerical analysis.
Computer science.
Computer Vision.
Numerical Analysis.
Computer Science.
Local Subjects:
Computer Vision.
Algorithms.
Numerical Analysis.
Computer Science.
Physical Description:
1 online resource (VII, 175 pages) : 128 illustrations
Edition:
1st ed. 2014.
Contained In:
Springer Nature eBook
Place of Publication:
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014.
System Details:
text file PDF
Summary:
This book constitutes the thoroughly refereed post-conference proceedings of the International Dagstuhl-Seminar on Efficient Algorithms for Global Optimization Methods in Computer Vision, held in Dagstuhl Castle, Germany, in November 2011. The 8 revised full papers presented were carefully reviewed and selected by 12 lectures given at the seminar. The seminar focused on the entire algorithmic development pipeline for global optimization problems in computer vision: modelling, mathematical analysis, numerical solvers and parallelization. In particular, the goal of the seminar was to bring together researchers from all four fields to analyze and discuss the connections between the different stages of the algorithmic design pipeline.
Contents:
Mathematical Optimization
Modeling
Nonconvex Optimization
Shape Representations.
Other Format:
Printed edition:
ISBN:
978-3-642-54774-4
9783642547744
Access Restriction:
Restricted for use by site license.

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