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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