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| Content Provider | Springer Nature Link |
|---|---|
| Author | Uchiyama, Hideaki Saito, Hideo Servières, Myriam Moreau, Guillaume |
| Copyright Year | 2010 |
| Abstract | We propose a camera-tracking method by on-line learning of keypoint arrangements in augmented reality applications. As target objects, we deal with intersection maps from GIS and text documents, which are not dealt with by the popular SIFT and SURF descriptors. For keypoint matching by keypoint arrangement, we use locally likely arrangement hashing (LLAH), in which the descriptors of the arrangement in a viewpoint are not invariant to the wide range of viewpoints because the arrangement is changeable with respect to viewpoints. In order to solve this problem, we propose online learning of descriptors using new configurations of keypoints at new viewpoints. The proposed method allows keypoint matching to proceed under new viewpoints. We evaluate the performance and robustness of our tracking method using view changes. |
| Starting Page | 109 |
| Ending Page | 117 |
| Page Count | 9 |
| File Format | |
| ISSN | 13594338 |
| Journal | Virtual Reality |
| Volume Number | 15 |
| Issue Number | 2-3 |
| e-ISSN | 14349957 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2010-10-07 |
| Publisher Place | London |
| Access Restriction | Subscribed |
| Subject Keyword | LLAH Feature descriptor Camera tracking Augmented reality Artificial Intelligence (incl. Robotics) Computer Graphics Image Processing and Computer Vision Computing Methodologies |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Graphics and Computer-Aided Design Human-Computer Interaction Software |
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