Central Library

Normal view MARC view ISBD view

The Maximum Consensus Problem [electronic resource] : Recent Algorithmic Advances / by Tat-Jun Chin, David Suter.

By: Chin, Tat-Jun [author.].
Contributor(s): Suter, David [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Computer Vision: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XV, 178 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031018183.Subject(s): Image processing -- Digital techniques | Computer vision | Pattern recognition systems | Computer Imaging, Vision, Pattern Recognition and Graphics | Computer Vision | Automated Pattern RecognitionAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006 Online resources: Click here to access online
Contents:
Preface -- Acknowledgments -- The Maximum Consensus Problem -- Approximate Algorithms -- Exact Algorithms -- Preprocessing for Maximum Consensus -- Appendix -- Bibliography -- Authors' Biographies -- Index .
In: Springer Nature eBookSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

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.

There are no comments for this item.

Log in to your account to post a comment.