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The maximum consensus problem : recent algorithmic advances

Author: Tat-Jun Chin; David Suter, (Computer scientist)
Publisher: [San Rafael, California] : Morgan & Claypool Publishers, [2017] ©2017
Series: Synthesis digital library of engineering and computer science.; Synthesis lectures on computer vision, #11.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Outlier-contaminated data is a fact of life in computer vision. For computer vision applications to perform reliably and accurately in practical settings, the processing of the input data must be conducted in a robust manner. In this context, the maximum consensus robust criterion plays a critical role by allowing the quantity of interest to be estimated from noisy and outlier-prone visual measurements. The maximum  Read more...
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Details

Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Tat-Jun Chin; David Suter, (Computer scientist)
ISBN: 9781627052863 1627052860
OCLC Number: 978253071
Description: 1 online resource (xiii, 178 pages) : illustrations.
Contents: 1. The maximum consensus problem --
1.1 Introduction --
1.1.1 Problem definition --
1.1.2 What is this book about? --
1.1.3 Road map --
1.2 Relation to other robust fitting methods --
1.2.1 Hough transform --
1.2.2 M-estimator --
1.2.3 Least median squares --
1.3 Problem difficulty --
1.3.1 Exact vs. approximate solutions --
1.3.2 Computational hardness --
1.4 Bibliographical remarks --
2. Approximate algorithms --
2.1 Introduction --
2.2 Random sample consensus --
2.2.1 Extensions and improvements --
2.2.2 Data span and quasidegeneracy --
2.3 L1 minimization --
2.3.1 Generalized fractional models --
2.4 Chebyshev approximation --
2.4.1 Characterization of the Chebyshev estimate --
2.4.2 Outlier removal with L[infinity] minimization --
2.4.3 Generalised fractional programming --
2.5 LP-type problems --
2.5.1 Definition and properties --
2.5.2 Solving LP-type problems --
2.5.3 Outlier removal for LP-type problems --
2.6 The K-slack method --
2.6.1 A relaxed minimax formulation --
2.6.2 Outlier removal with the K-slack method --
2.7 Exact penalty method --
2.7.1 Penalized formulation --
2.7.2 Deterministic local refinement algorithm --
2.8 Evaluation --
2.9 Bibliographical remarks --
3. Exact algorithms --
3.1 Introduction --
3.2 Optimal line fitting --
3.2.1 Characterization of the solution --
3.2.2 Plane sweep method --
3.3 Integer linear programming method --
3.3.1 Numerical accuracy and performance --
3.3.2 Generalized fractional models --
3.4 Robust point set registration --
3.4.1 Rotational alignment --
3.4.2 Euclidean registration --
3.5 Tractable algorithms with subset search --
3.5.1 Characterization of the solution --
3.5.2 Subset enumeration --
3.6 Tree search --
3.6.1 Existence of tree structure --
3.6.2 Breadth first search --
3.6.3 A* search --
3.7 Bibliographical remarks --
4. Preprocessing for maximum consensus --
4.1 Introduction --
4.1.1 Guaranteed outlier removal --
4.2 Geometrically inspired approaches --
4.2.1 2D rigid transformation --
4.2.2 3D rotational alignment --
4.3 Integer linear programming approach --
4.3.1 An integer linear program formulation for GORE --
4.3.2 Generalised fractional models --
4.4 Bibliographical remarks --
Appendix --
Bibliography --
Authors' biographies --
Index.
Series Title: Synthesis digital library of engineering and computer science.; Synthesis lectures on computer vision, #11.
Responsibility: Tat-Jun Chin and David Suter.
More information:

Abstract:

The Maximum Consensus Problem refers to the problem of optimizing the quantity of interest according to the maximum consensus criterion. This book provides an overview of the algorithms for  Read more...

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