The MagicBook—Moving Seamlessly between Reality and Virtuality
IEEE Computer Graphics and Applications
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Pose tracking from natural features on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Augmenting text document by on-line learning of local arrangement of keypoints
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Virtual pop-up book based on augmented reality
Proceedings of the 2007 conference on Human interface: Part II
Scalable real-time planar targets tracking for digilog books
The Visual Computer: International Journal of Computer Graphics
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Digilog book for temple bell tolling experience based on interactive augmented reality
Virtual Reality - Special Issue on Cultural Technology
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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Digilog Books are AR (Augmented Reality) books, which provide additional information by visual, haptic, auditory, and olfactory senses. In this paper, we propose an accurate and adaptive feature matching method based on a page layout for the Digilog Books. While previous Digilog Books attached visual markers or matched natural features extracted from illustrations for page identification, the proposed method divides input images, captured by camera, into text and illustration regions using CRLA (Constrained Run Length Algorithm) according to the page layouts. We apply LLAH (Locally Likely Arrangement Hashing) and FAST+SURF (FAST features using SURF descriptor) algorithm to appropriate region in order to get a high matching rate. In addition, it merges matching results from both areas using page layout in order to cover large area. In our experiments, the proposed method showed similar matching performance with LLAH in text documents and FAST+SURF in illustrations. Especially, the proposed method showed 15% higher matching rate than LLAH and FAST+SURF in the case of documents that contain both text and illustration. We expect that the proposed method would be applicable to identifying various documents for diverse applications such as augmented reality and digital library.