
The Maximum Consensus Problem
Recent Algorithmic Advances
Morgan and Claypool Life Sciences (Publisher)
Published on 28. February 2017
Book
Paperback/Softback
194 pages
978-1-62705-292-4 (ISBN)
Description
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 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 performing this optimization. The emphasis is on the basic operation or ""inner workings"" of the algorithms, and on their mathematical characteristics in terms of optimality and efficiency. The applicability of the techniques to common computer vision tasks is also highlighted. By collecting existing techniques in a single article, this book aims to trigger further developments in this theoretically interesting and practically important area.
More details
Series
Language
English
Place of publication
San Rafael, CA
United States
Publishing group
Morgan & Claypool Publishers
Dimensions
Height: 235 mm
Width: 190 mm
Weight
380 gr
ISBN-13
978-1-62705-292-4 (9781627052924)
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Schweitzer Classification
Persons
Content
- Preface
- Acknowledgments
- The Maximum Consensus Problem
- Approximate Algorithms
- Exact Algorithms
- Preprocessing for Maximum Consensus
- Appendix
- Bibliography
- Authors' Biographies
- Index
- Acknowledgments
- The Maximum Consensus Problem
- Approximate Algorithms
- Exact Algorithms
- Preprocessing for Maximum Consensus
- Appendix
- Bibliography
- Authors' Biographies
- Index