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The Maximum Consensus Problem: Recent Algorithmic Advances

The Maximum Consensus Problem: Recent Algorithmic Advances

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作者: Tat-Jun Chin, David Suter, Gerard Medioni 作者

出版社: Morgan and Claypool Life Sciences

出版时间: 2017-02

ISBN: 1627052925 或 9781627052924

定价: 593.80

装帧: 平装

开本: 其他

页数: 194页

正文语种: 英语

丛书: Synthesis Lectures on Computer Vision

分类: 外文古旧书>英文书>文学

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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.
内容简介:
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.

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