Real-time binocular smooth pursuit
International Journal of Computer Vision
Driving saccade to pursuit using image motion
International Journal of Computer Vision
A neural model of the saccade generator in the reticular formation
Neural Networks - Special issue on neural control and robotics: biology and technology
The handbook of brain theory and neural networks
Implicit and Explicit Camera Calibration: Theory and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Learning Approach to Fixating on 3D Targets with Active Cameras
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
The role of the striatum in adaptation learning: a computational model
Biological Cybernetics
A self-organizing neural model of motor equivalent reaching and tool use by a multijoint arm
Journal of Cognitive Neuroscience
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This paper describes a head-neck-eye camera system that is capable of learning to saccade to 3-D targets in a self-organized fashion. The self-organized learning process is based on action perception cycles where the camera system performs micro saccades about a given head-neck-eye camera position and learns to map these micro saccades to changes in position of a 3-D target currently in view of the stereo camera. This motor babbling phase provides self-generated movement commands that activate correlated visual, spatial and motor information that are used to learn an internal coordinate transformation between vision and motor systems. The learned transform is used by resulting head-neck-eye camera system to accurately saccade to 3-D targets using many different combinations of head, neck, and eye positions. The interesting aspect of the learned transform is that it is robust to a wide variety of disturbances including reduced degrees of freedom of movement for the head, neck, one eye, or any combination of two of the three, movement of head and neck as a function of eye movements, changes in the stereo camera separation distance and changes in focal lengths of the cameras. These disturbances were not encountered during motor babbling phase. This feature points to general nature of the learned transform in its ability to control autonomous systems with redundant degrees of freedom in a very robust and fault-tolerant fashion.