The Maximum Consensus Problem

This book provides an overview of the algorithms used for solving the maximum consensus problem. This problem solves the issues by extracting visual information from noisy and outlier-prone visual measurements. 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. The book discusses how the maximum consensus robust criterion plays a critical role. Moreover, the authors explain the various features of the algorithms used to solve this problem, as well as the mathematical characteristics of algorithms in terms of optimality and efficiency. The book also highlights the applications of the techniques for common computer vision tasks. This timely Second Edition incorporates many recent theoretical and practical advances since publication of the prior edition, collects existing techniques into one comprehensive resource, and encourages readers to pursue further research and developments in this area.