Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition Robust Under Translations, Deformations, and Changes in Background
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active object recognition integrating attention and viewpoint control
Computer Vision and Image Understanding
Self Calibration of the Fixation Movement of a Stereo Camera Head
Machine Learning - Special issue on learning in autonomous robots
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self Calibration of the Fixation Movement of a Stereo Camera Head
Autonomous Robots
Learning to Recognize and Grasp Objects
Autonomous Robots
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Towards Imitation Learning of Grasping Movements by an Autonomous Robot
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Pose-Independent Object Representation by 2-D Views
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Robust classification of hand postures against complex backgrounds
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
A Gesture Interface for Human-Robot-Interaction
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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This paper introduces our one-armed stationary humanoid robot GripSee together with research projects carried out on this platform. The major goal is to have it analyze a table scene and manipulate the objects found. Gesture-guided pick-and-place This has already been implemented for simple cases without clutter. New objects can be learned under user assistance, and first work on the imitation of grip trajectories has been completed.Object and gesture recognition are correspondence-based and use elastic graph matching. The extension to bunch graph matching has been very fruitful for face and gesture recognition, and a similar memory organization for aspects of objects is a subject of current research.In order to overcome visual inaccuracies during grasping we have built our own type of dynamic tactile sensor. So far they are used for dynamics that try to optimize the symmetry of the contact distribution across the gripper. With the help of those dynamics the arm can be guided on an arbitrary trajectory with negligible force.