Scalable Active Matching

In matching tasks in computer vision, and particularly in real-time tracking from video, there are generally strong priors available on absolute and relative correspondence locations thanks to motion and scene models. While these priors are often partially used post-hoc to resolve matching consensus in algorithms like RANSAC, it was recently shown that fully integrating them in an `Active Matching' (AM) approach permits efficient guided image processing with rigorous decisions guided by Information Theory. AM's weakness was that the overhead induced by intermediate Bayesian updates required meant poor scaling to cases where many correspondences were sought. In this paper we show that relaxation of the rigid probabilistic model of AM, where every feature measurement directly affects the prediction of every other, permits dramatically more scalable operation without affecting accuracy. We take a general graph-theoretic view of the structure of prior information in matching to ...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Ankur Handa, Margarita Chli, Hauke Strasdat, Andrew Davison
• Keywords: Active Matching, Computer Vision, Correspondence Locations Thanks, CVPR 2010, Strong Priors