No abstract

We present an algorithm for optimal view point selection for 3-D reconstruction of an object using 2-D image points. Since the image points are noisy, a Kalman filter is used to obtain the best estimate of the object's geometry. This Kalman filter allows us to efficiently predict the effect of any given camera position on the uncertainty, and therefore quality, of the estimate. By choosing a suitable optimization criterion, we are able to determine the camera positions which minimize our reconstruction error.We verify our results using two experiments with real images: one experiment uses a calibration pattern for comparison to a ground-truth state, the other reconstructs a real world object.

No abstract

Abstract. We present a new method for planning the optimal next view for a probabilistic visual object tracking task. Our method uses a variable number of cameras, can plan an action sequence several time steps into the future, and allows for real-time usage due to a computation time which is linear both in the number of cameras and the number of time steps. The algorithm can also handle object loss in one, more or all cameras, interdependencies in the camera's information contribution, and variable action costs. We evaluate our method by comparing it to previous approaches with a prerecorded sequence of real world images.From K. Franke et al., Pattern Recognition, 28th DAGM Symposium, Springer, 2006, (pp. 536-545).

We describe an enhanced method for the selection of optimal sensor actions in a probabilistic state estimation framework. We apply this to the selection of optimal focal lengths for cameras with a variable motor zoom in a real-time visual object tracking task. The optimal camera action is determined by the expected state estimate entropy for each candidate action. Varying action costs are taken into account by predicting the entropy several steps into the future. Our contribution is the use of the sequential Kalman filter to deal transparently with a variable number of cameras, potential object loss in a subset of the cameras, and to reduce the calculation time through independent optimization.