Description

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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.

About the Author

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Tat-Jun Chin was born and raised in Nibong Tebal, Penang. He received his B.Eng. in Electrical Engineering (Mechatronics) from Universiti Teknologi Malaysia (UTM) in 2003, and his Ph.D. in Computer Systems Engineering from Monash University in 2007 with a thesis titled "Kernel Subspace Methods in Computer Vision". His undergraduate studies were partly supported by an Agilent Technologies Scholarship, and his Ph.D. studies were mainly supported by an Endeavour Australia-Asia Award. He was a Research Fellow at the Institute for Infocomm Research (I2R) in Singapore from 2007-2008. Since 2008 he has been a Senior Research Associate (2008-2010), Lecturer (2010-2013), and Senior Lecturer (2014-2016) at e University of Adelaide. Since 2017 he was an Associate Professor at the same university. His research interests include robust estimation and geometric optimization. He won a CVPR award and DSTO award (both in 2015) for his research work.