Bibtex:Maidi06c
Un article de Wiki-evr@.
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| Version du 31 octobre 2008 à 10:31 (modifier) Maidi (Discuter | Contributions) ← Différence précédente |
Version actuelle (12 avril 2010 à 18:18) (modifier) (défaire) Maidi (Discuter | Contributions) |
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| month = {September 21-23}, | month = {September 21-23}, | ||
| abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. We use a generic algorithm for object detection and feature points extraction, then, we identify the target object with its barcode for template matching. Given a 2D feature points and a 3D object model, object pose consists in recovering the position and the orientation of the object with respect to the camera. Using the computed pose and the identified object, we propose a first tracking algorithm based on homography computation. However due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend the previous object pose algorithm for bar-coded fiducials, using a second algorithm based on RANSAC estimator and fiducial reprojection on the image. The proposed method is computationally efficient and showed to be accurate and robust.}, | abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. We use a generic algorithm for object detection and feature points extraction, then, we identify the target object with its barcode for template matching. Given a 2D feature points and a 3D object model, object pose consists in recovering the position and the orientation of the object with respect to the camera. Using the computed pose and the identified object, we propose a first tracking algorithm based on homography computation. However due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend the previous object pose algorithm for bar-coded fiducials, using a second algorithm based on RANSAC estimator and fiducial reprojection on the image. The proposed method is computationally efficient and showed to be accurate and robust.}, | ||
| - | pdf = {Maidi06c.pdf} | ||
| } | } | ||
| </bibtex> | </bibtex> | ||
Version actuelle
M. Maidi, F. Ababsa, M. Mallem - Robust Fiducials Tracking in Augmented Reality
