Trajectories for optimal temporal integration in active vision systems

We describe a general technique for specifying trajectories of controllable imaging parameters in an active vision system so that temporal integration processes are optimized. The technique assumes that a Kalman filter is used to perform the temporal integration of measurements and is based on determining, at each point in time, the set of imaging parameter values that minimizes the trace of the state estimate error covariance matrix. We present the application of this technique to the active vision task of extracting the location and orientation of a plane from shadows cast on it with a position controlled light source