Bibtex:Maidi06
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| Version du 18 mai 2006 à 14:18 (modifier) Maidi (Discuter | Contributions) ← Différence précédente |
Version actuelle (12 avril 2010 à 19:19) (modifier) (défaire) Maidi (Discuter | Contributions) |
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| <bibtex> | <bibtex> | ||
| @InProceedings{Maidi06, | @InProceedings{Maidi06, | ||
| - | author = {M. Maidi | + | author = {M. Maidi and F. Ababsa and M. Mallem}, |
| title = {Active Contours Motion based on Optical Flow for Tracking in Augmented Reality}, | title = {Active Contours Motion based on Optical Flow for Tracking in Augmented Reality}, | ||
| booktitle = {8th International Conference on Virtual Reality (VRIC 2006)}, | booktitle = {8th International Conference on Virtual Reality (VRIC 2006)}, | ||
| Ligne 7 : | Ligne 7 : | ||
| year = {2006}, | year = {2006}, | ||
| address = {Laval (France)}, | address = {Laval (France)}, | ||
| - | month = {26-28 | + | month = {April 26-28}, |
| - | abstract = {In this paper we present a visual object tracking approach to extract motion information for Augmented Reality (AR) systems. Our proposed system tracks the target object by applying a model based pose estimation | + | abstract = {In this paper we present a visual object tracking approach to extract motion information for Augmented Reality (AR) systems. Our proposed system tracks the target object by applying a model based pose estimation algorithm. The approach is to fuse information from an active contours model and optical flow motion estimation. The optical flow is used to provide a constraint on the deformable model motion and place the initial contour in the region of interest of the active contour. For pose estimation we use the Extended Kalman Filter (EKF), the measurement equation models the feature points of object in image and the process model predicts the behavior of the system based on the current state and estimates the position and orientation of the object toward the camera coordinate frame. The algorithm is tested in real time and shows to be robust and efficient.} |
| - | algorithm. The approach is to fuse information from an active contours model and optical flow motion estimation. | + | |
| - | The optical flow is used to provide a constraint on the deformable model motion and place the initial | + | |
| - | contour in the region of interest of the active contour. For pose estimation we use the Extended Kalman Filter | + | |
| - | (EKF), the measurement equation models the feature points of object in image and the process model predicts | + | |
| - | the behavior of the system based on the current state and estimates the position and orientation of the object | + | |
| - | toward the camera coordinate frame. The algorithm is tested in real time and shows to be robust and efficient. | + | |
| - | + | ||
| } | } | ||
| </bibtex> | </bibtex> | ||
Version actuelle
M. Maidi, F. Ababsa, M. Mallem - Active Contours Motion based on Optical Flow for Tracking in Augmented Reality
