Self-organizing maps
Stochastic Algorithms for Visual Tracking: Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking
Statistical color models with application to skin detection
International Journal of Computer Vision
Spatial Dependence in the Observation of Visual Contours
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Visual Hand Tracking Algorithms
GMAI '06 Proceedings of the conference on Geometric Modeling and Imaging: New Trends
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An approach to the task of Programming by demonstration (PbD) of grasping skills is introduced, where a mobile service robot is taught by a human instructor how to grasp a specific object. In contrast to other approaches the instructor demonstrates the grasping action several times to the robot to increase reconstruction performance. Only the robot's stereoscopic vision system is used to track the instructor's hand. The developed tracking algorithm is designed to not need artificial markers, data gloves or being restricted to fixed or difficult to calibrate sensor installations while at the same time being real-time capable on a mobile service robot with limited resources. Due to the instructor's repeated demonstrations and his low repeating accuracy, every time a grasp is demonstrated the instructor performs it differently. To compensate for these variations and also to compensate for tracking errors, the use of a Self-Organizing-Map (SOM) with a one-dimensional topology is proposed. This SOM is used to generalize over differently demonstrated grasping actions and to reconstruct the intended approach trajectory of the instructor's hand while grasping an object. The approach is implemented and evaluated on the service robot TASER using synthetically generated data as well as real world data.