By Topic

Augmenting text document by on-line learning of local arrangement of keypoints

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Uchiyama, Hideaki ; Keio Univ., Tokyo, Japan ; Saito, H.

We propose a technique for text document tracking over a large range of viewpoints. Since the popular SIFT or SURF descriptors typically fail on such documents, our method considers instead local arrangement of keypoints. We extends locally likely arrangement hashing (LLAH), which is limited to fronto-parallel images: We handle a large range of viewpoints by learning the behavior of keypoint patterns when the camera viewpoint changes. Our method starts tracking a document from a nearly frontal view. Then, it undergoes motion, and new configurations of keypoints appear. The database is incrementally updated to reflect these new observations, allowing the system to detect the document under the new viewpoint. We demonstrate the performance and robustness of our method by comparing it with the original LLAH.

Published in:

Mixed and Augmented Reality, 2009. ISMAR 2009. 8th IEEE International Symposium on

Date of Conference:

19-22 Oct. 2009