Bibtex:Maidi06b - Historique des versions

Maidi le 12 avril 2010 à 19:18

Maidi — Mon, 12 Apr 2010 19:18:16 GMT

← Version précédente		Version du 12 avril 2010 à 19:18
Ligne 8 :		Ligne 8 :
	address = {Setúbal (Portugal)},		address = {Setúbal (Portugal)},
	month = {August 2-5},		month = {August 2-5},
-	abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. Our approach identifies the target object with an internal barecode of the fiducial and extracts its 2D features points. Given the 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. For pose estimation, we presented two methods for recovering pose using the Extended Kalman Filter and the Orthogonal Iteration algorithm. The first algorithm is a sequential estimator that predicts and corrects the state vector. While the later uses the object space collinearity error and derives an iterative algorithm to compute orthogonal rotation matrices. Due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend our tracking method using a RANSAC algorithm to deal with occlusions. The algorithm is tested with	+	abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. Our approach identifies the target object with an internal barecode of the fiducial and extracts its 2D features points. Given the 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. For pose estimation, we presented two methods for recovering pose using the Extended Kalman Filter and the Orthogonal Iteration algorithm. The first algorithm is a sequential estimator that predicts and corrects the state vector. While the later uses the object space collinearity error and derives an iterative algorithm to compute orthogonal rotation matrices. Due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend our tracking method using a RANSAC algorithm to deal with occlusions. The algorithm is tested with different camera viewpoints under various image conditions and shows to be accurate and robust.},
-	different camera viewpoints under various image conditions and shows to be accurate and robust.},	+
	}		}
	</bibtex>		</bibtex>

Maidi le 12 avril 2010 à 18:18

Maidi — Mon, 12 Apr 2010 18:18:35 GMT

← Version précédente		Version du 12 avril 2010 à 18:18
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	abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. Our approach identifies the target object with an internal barecode of the fiducial and extracts its 2D features points. Given the 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. For pose estimation, we presented two methods for recovering pose using the Extended Kalman Filter and the Orthogonal Iteration algorithm. The first algorithm is a sequential estimator that predicts and corrects the state vector. While the later uses the object space collinearity error and derives an iterative algorithm to compute orthogonal rotation matrices. Due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend our tracking method using a RANSAC algorithm to deal with occlusions. The algorithm is tested with		abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. Our approach identifies the target object with an internal barecode of the fiducial and extracts its 2D features points. Given the 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. For pose estimation, we presented two methods for recovering pose using the Extended Kalman Filter and the Orthogonal Iteration algorithm. The first algorithm is a sequential estimator that predicts and corrects the state vector. While the later uses the object space collinearity error and derives an iterative algorithm to compute orthogonal rotation matrices. Due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend our tracking method using a RANSAC algorithm to deal with occlusions. The algorithm is tested with
	different camera viewpoints under various image conditions and shows to be accurate and robust.},		different camera viewpoints under various image conditions and shows to be accurate and robust.},
-	pdf = {Maidi06b.pdf}
	}		}
	</bibtex>		</bibtex>

Maidi le 31 octobre 2008 à 10:32

Maidi — Fri, 31 Oct 2008 10:32:20 GMT

← Version précédente		Version du 31 octobre 2008 à 10:32
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	title = {Robust Augmented Reality Tracking based Visual Pose Estimation},		title = {Robust Augmented Reality Tracking based Visual Pose Estimation},
	booktitle = {3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)},		booktitle = {3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)},
-	pages = {},	+	pages = {346-351},
	year = {2006},		year = {2006},
	address = {Setúbal (Portugal)},		address = {Setúbal (Portugal)},

Maidi le 21 septembre 2007 à 07:18

Maidi — Fri, 21 Sep 2007 07:18:59 GMT

← Version précédente		Version du 21 septembre 2007 à 07:18
Ligne 9 :		Ligne 9 :
	month = {August 2-5},		month = {August 2-5},
	abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. Our approach identifies the target object with an internal barecode of the fiducial and extracts its 2D features points. Given the 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. For pose estimation, we presented two methods for recovering pose using the Extended Kalman Filter and the Orthogonal Iteration algorithm. The first algorithm is a sequential estimator that predicts and corrects the state vector. While the later uses the object space collinearity error and derives an iterative algorithm to compute orthogonal rotation matrices. Due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend our tracking method using a RANSAC algorithm to deal with occlusions. The algorithm is tested with		abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. Our approach identifies the target object with an internal barecode of the fiducial and extracts its 2D features points. Given the 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. For pose estimation, we presented two methods for recovering pose using the Extended Kalman Filter and the Orthogonal Iteration algorithm. The first algorithm is a sequential estimator that predicts and corrects the state vector. While the later uses the object space collinearity error and derives an iterative algorithm to compute orthogonal rotation matrices. Due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend our tracking method using a RANSAC algorithm to deal with occlusions. The algorithm is tested with
-	different camera viewpoints under various image conditions and shows to be accurate and robust.}	+	different camera viewpoints under various image conditions and shows to be accurate and robust.},
	pdf = {Maidi06b.pdf}		pdf = {Maidi06b.pdf}
	}		}
	</bibtex>		</bibtex>

Maidi le 21 septembre 2007 à 07:15

Maidi — Fri, 21 Sep 2007 07:15:33 GMT

← Version précédente		Version du 21 septembre 2007 à 07:15
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	address = {Setúbal (Portugal)},		address = {Setúbal (Portugal)},
	month = {August 2-5},		month = {August 2-5},
		+	abstract = {In this paper, we present a robust fiducials tracking method for real time Augmented Reality systems. Our approach identifies the target object with an internal barecode of the fiducial and extracts its 2D features points. Given the 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. For pose estimation, we presented two methods for recovering pose using the Extended Kalman Filter and the Orthogonal Iteration algorithm. The first algorithm is a sequential estimator that predicts and corrects the state vector. While the later uses the object space collinearity error and derives an iterative algorithm to compute orthogonal rotation matrices. Due to lighting or contrast conditions or occlusion of the target object by an other object, the tracking may fail. Therefore, we extend our tracking method using a RANSAC algorithm to deal with occlusions. The algorithm is tested with
		+	different camera viewpoints under various image conditions and shows to be accurate and robust.}
	pdf = {Maidi06b.pdf}		pdf = {Maidi06b.pdf}
	}		}
	</bibtex>		</bibtex>

Gi: Modèle:Bibtex/Maidi06b déplacé vers Bibtex:Maidi06b

Gi — Tue, 23 Jan 2007 13:30:50 GMT

Modèle:Bibtex/Maidi06b déplacé vers Bibtex:Maidi06b

← Version précédente

Version du 23 janvier 2007 à 13:30

Maidi le 11 juillet 2006 à 08:19

Maidi — Tue, 11 Jul 2006 08:19:10 GMT

← Version précédente		Version du 11 juillet 2006 à 08:19
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	year = {2006},		year = {2006},
	address = {Setúbal (Portugal)},		address = {Setúbal (Portugal)},
-	month = {2-5 ~~August~~},	+	month = {August 2-5},
	pdf = {Maidi06b.pdf}		pdf = {Maidi06b.pdf}
	}		}
	</bibtex>		</bibtex>

Maidi le 11 juillet 2006 à 05:31

Maidi — Tue, 11 Jul 2006 05:31:43 GMT

← Version précédente		Version du 11 juillet 2006 à 05:31
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	year = {2006},		year = {2006},
	address = {Setúbal (Portugal)},		address = {Setúbal (Portugal)},
-	month = {2-5 ~~Août~~},	+	month = {2-5 August},
	pdf = {Maidi06b.pdf}		pdf = {Maidi06b.pdf}
	}		}
	</bibtex>		</bibtex>

Maidi le 7 juillet 2006 à 07:26

Maidi — Fri, 07 Jul 2006 07:26:22 GMT

← Version précédente		Version du 7 juillet 2006 à 07:26
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	address = {Setúbal (Portugal)},		address = {Setúbal (Portugal)},
	month = {2-5 Août},		month = {2-5 Août},
-	pdf = {}	+	pdf = {Maidi06b.pdf}
	}		}
	</bibtex>		</bibtex>

Maidi le 1 juillet 2006 à 10:02

Maidi — Sat, 01 Jul 2006 10:02:22 GMT

← Version précédente		Version du 1 juillet 2006 à 10:02
Ligne 3 :		Ligne 3 :
	author = {M. Maidi and F. Ababsa and M. Mallem },		author = {M. Maidi and F. Ababsa and M. Mallem },
	title = {Robust Augmented Reality Tracking based Visual Pose Estimation},		title = {Robust Augmented Reality Tracking based Visual Pose Estimation},
-	booktitle = {~~3nd~~ International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)},	+	booktitle = {3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)},
	pages = {},		pages = {},
	year = {2006},		year = {2006},