Archeoguide: An Augmented Reality Guide for Archaeological Sites
IEEE Computer Graphics and Applications
Hybrid Tracking for Outdoor Augmented Reality Applications
IEEE Computer Graphics and Applications
Archeoguide: An Augmented Reality Guide for Archaeological Sites
IEEE Computer Graphics and Applications
Markerless Augmented Reality with a Real-Time Affine Region Tracker
ISAR '01 Proceedings of the IEEE and ACM International Symposium on Augmented Reality (ISAR'01)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Robust Hybrid Tracking System for Outdoor Augmented Reality
VR '04 Proceedings of the IEEE Virtual Reality 2004
Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Scene Modelling, Recognition and Tracking with Invariant Image Features
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Going out: robust model-based tracking for outdoor augmented reality
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Automated annotation of landmark images using community contributed datasets and web resources
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
| Hi-index | 0.00 |
This study proposes augmented reality from mobile devices based on SIFT (Scale Invariant Feature Transform) features for markerless outdoor augmented reality application. The proposed application is navigation help in a city. These SIFT features are projected on a digital model of the building façades of the square to obtain 3D co-ordinates for each feature point. The algorithms implemented calculate the camera pose for frame of a video from 3D-2D point correspondences between features extracted in the current video frame and points in the reference dataset. The algorithms were successfully tested on video films of city squares. Although they do not operate in real-time, they are capable of a correct pose estimation and projection of artificial data into the scene. In case of a loss of track, the algorithms recover automatically. The study shows the potential of SIFT features for purely image based markerless outdoor augmented reality applications. This study takes place in the MoSAIC project.