The Maximum Consensus Problem : Recent Algorithmic Advances
Chin, Tat-Jun
The Maximum Consensus Problem : Recent Algorithmic Advances by Tat-Jun Chin, David Suter - 1st edition 2017 - 1 recurso en línea (XV, 178 páginas) - Synthesis Lectures on Computer Vision 2153-1064 .
Preface -- Acknowledgments -- The Maximum Consensus Problem -- Approximate Algorithms -- Exact Algorithms -- Preprocessing for Maximum Consensus -- Appendix -- Bibliography -- Authors' Biographies -- Index .
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.
9783031018183
10.1007/978-3-031-01818-3 doi
Optimización matemática
Visión por ordenador--Modelos matemáticos
Algoritmos
TA1634 / 2017 EB
The Maximum Consensus Problem : Recent Algorithmic Advances by Tat-Jun Chin, David Suter - 1st edition 2017 - 1 recurso en línea (XV, 178 páginas) - Synthesis Lectures on Computer Vision 2153-1064 .
Preface -- Acknowledgments -- The Maximum Consensus Problem -- Approximate Algorithms -- Exact Algorithms -- Preprocessing for Maximum Consensus -- Appendix -- Bibliography -- Authors' Biographies -- Index .
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.
9783031018183
10.1007/978-3-031-01818-3 doi
Optimización matemática
Visión por ordenador--Modelos matemáticos
Algoritmos
TA1634 / 2017 EB