Active Matching

Margarita Chli, AndrewJ. Davison

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In the matching tasks which form an integral part of all types of tracking and geometrical vision, there are invariably priors available on the absolute and/or relative image locations of features of interest. Usually, these priors are used post-hoc in the process of resolving feature matches and obtaining final scene estimates, via ‘first get candidate matches, then resolve’ consensus algorithms such as RANSAC. In this paper we show that the dramatically different approach of using priors dynamically to guide a feature by feature matching search can achieve global matching with much fewer image processing operations and lower overall computational cost. Essentially, we put image processing into the loop of the search for global consensus. In particular, our approach is able to cope with significant image ambiguity thanks to a dynamic mixture of Gaussians treatment. In our fully Bayesian algorithm, the choice of the most efficient search action at each step is guided intuitively and rigorously by expected Shannon information gain. We demonstrate the algorithm in feature matching as part of a sequential SLAM system for 3D camera tracking. Robust, real-time matching can be achieved even in the previously unmanageable case of jerky, rapid motion necessitating weak motion modelling and large search regions.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2008
Subtitle of host publication10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I
EditorsDavid Forsyth, Philip Torr, Andrew Zisserman
PublisherSpringer-Verlag GmbH
Number of pages14
ISBN (Electronic)978-3-540-88682-2
ISBN (Print)978-3-540-88681-5
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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