Proceedings of the Seventh IEEE International Conference on Computer Vision (1999)
Sept. 20, 1999 to Sept. 25, 1999
Maurício Marengoni , University of Massachusetts at Amherst
Allen Hanson , University of Massachusetts at Amherst
Shlomo Zilberstein , University of Massachusetts at Amherst
Edward Riseman , University of Massachusetts at Amherst
This paper presents a control structure for general purpose image understanding that addresses both the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is performed by an independent subsystem that uses Bayesian networks and utility theory to compute the marginal value of information provided by alternative operators and selects the ones with the highest value.We have implemented and tested this control structure with several aerial image data-sets. The results show that the knowledge base used by the system can be acquired using standard learning techniques and that the value-driven approach to the selection of vision algorithms leads to performance gains. Moreover, the modular system architecture simplifies the addition of both control knowledge and new vision algorithms.
A. Hanson, M. Marengoni, S. Zilberstein and E. Riseman, "Control in a 3D Reconstruction System using Selective Perception," Proceedings of the Seventh IEEE International Conference on Computer Vision(ICCV), Corfu, Greece, 1999, pp. 1229.