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.
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Acknowledgments
We thank Dr. Julien Pilet for the discussion. This work was supported in part by Grant-in-Aid for JSPS Fellows and a Grant-in-Aid for the Global Center of Excellence for high-Level Global Cooperation for Leading-Edge Platform on Access Spaces from the Ministry of Education, Culture, Sport, Science, and Technology in Japan.
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Uchiyama, H., Saito, H., Servières, M. et al. Camera tracking by online learning of keypoint arrangements using LLAH in augmented reality applications. Virtual Reality 15, 109–117 (2011). https://doi.org/10.1007/s10055-010-0173-7
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DOI: https://doi.org/10.1007/s10055-010-0173-7