Abstract
An important mechanism in active vision is that of fixating to different targets of interest in a scene. We present a two-stage design of a neurocontroller for the execution of saccades. The first stage is an open loop mode based on a learned spatial representation while the second stage is a closed-loop visual servoing mode. Explicit calibration of the kinematic and imaging parameters of the system is replaced with a self-organized learning scheme, thereby providing a flexible and efficient saccade control strategy. Experiments on the University of Illinois Active Vision System (UIAVS) are used to establish the feasibility of this approach.
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