The Maximum Consensus Problem

Recent Algorithmic Advances
2. Auflage 2027
ISBN: 978-3-032-47312-7
Verlag: Springer International Publishing

Buch, Englisch, 230 Seiten, Format (B × H): 168 mm x 240 mm

Reihe: Synthesis Lectures on Computer Vision

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. 

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Chin, Tat-Jun

Suter, David

Inhaltsverzeichnis

The Maximum Consensus Problem.- Approximate Algorithms.- Exact Algorithms.- Preprocessing for Maximum Consensus.- Maxcon and Graph Theory.- Boolean Monotone Functions.- Hypergraph Classes.- Utilizing New Computing Paradigms.

Über Autor(innen)

Tat-Jun Chin, Ph.D., is a Professor of Computer Science at Adelaide University. He received his Ph.D. in Computer Systems Engineering from Monash University and B.S. in Mechatronics Engineering from Universiti Teknologi Malaysia. Dr. Chin’s research interests include optimization for computer vision and machine learning and related applications for intelligent satellites and space robotics. He has published more than 100 research articles and has won several awards for his research.

David Suter, Ph.D., is a Research Professor at Edith Cowan University. He received his Ph.D. from La Trobe University. Dr. Suter previously held positions at La Trobe University, Monash University, and The University of Adelaide. His research interests have spanned image processing, computer vision, and machine learning.