Overview
- Features a comprehensive description of regularization through optimization
- Contains a large selection of data fusion algorithms
- Includes chapters devoted to video compression and enhancement
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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About this book
This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems.The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc.
Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.
Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.
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Keywords
- regularization parameter selection
- shape representation in image processing
- image interpolation algorithms
- regularization methods for linear inverse problems
- one dimensional optimization
- unconstrained optimization methods
- optimization with linear constraints
- 3D image smoothing
- 3D volumetric description
- algorithm analysis and problem complexity
Table of contents (12 chapters)
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Front Matter
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Part II
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Front Matter
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Back Matter
Reviews
“The presentation of the problems is accompanied by illustrating examples. The book contains both a great theoretical background and practical applications and is thus self-contained. It is useful for master and doctoral students, as well as for researchers and practitioners dealing with computer vision and image processing, but also working in mathematical optimization.” (Ruxandra Stoean, zbMATH 1362.68003, 2017)
Authors and Affiliations
Bibliographic Information
Book Title: Optimization Techniques in Computer Vision
Book Subtitle: Ill-Posed Problems and Regularization
Authors: Mongi A. Abidi, Andrei V. Gribok, Joonki Paik
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-3-319-46364-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-46363-6Published: 16 December 2016
Softcover ISBN: 978-3-319-83501-3Published: 04 July 2018
eBook ISBN: 978-3-319-46364-3Published: 06 December 2016
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 1
Number of Pages: XV, 293
Number of Illustrations: 104 b/w illustrations, 23 illustrations in colour
Topics: Image Processing and Computer Vision, Signal, Image and Speech Processing, Algorithm Analysis and Problem Complexity, Mathematical Applications in Computer Science