Active Matching: Efficient Guided Search for Image Correspondence
published: Nov. 8, 2010, recorded: June 2010, views: 3562
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Over the past few years we have worked on an approach to matching features between images we takes full advantage of the priors which are normally available to avoid blanket, bottom-up image processing and proceed in a sequential, guided manner. In Active Matching, each measurement of one feature is used to dynamically and probabilistically update predictions of the positions of the other candidate features. In this way, image processing can be put "into the loop" of the search for global consensus, producing matching algorithms which are much more satisfying than RANSAC or similar which depend on random sampling and fixed thresholds. The decisions which must be taken at each step are determined based on explicit evaluation of expected information gain. I will explain the basic Active Matching algorithm, and recent developments which now allow us to match hundreds of features per frame in real-time.
Download slides: rss2010_davison_ameg_01.pdf (4.5 MB)
Download rss2010_davison_ameg_01.mp4 (Video - generic video source 142.8 MB)
Download rss2010_davison_ameg_01.flv (Video 70.8 MB)
Download rss2010_davison_ameg_01.wmv (Video 90.2 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !