PROBABILISTIC REASONING IN HIGH-LEVEL VISION

High-level vision is concerned with constructing a model of the visual world and making use of this knowledge for later recognition. In this paper, we develop a general framework for representing uncertain know ledge in high-level vision. Starting from a probabilistic network repr esentation, we develop a structure for presenting visual knowledge, an d techniques for probability propagation, parameter learning and struc tural improvement. This framework provides an adequate basis for repre senting uncertain knowledge in computer vision, especially in complex natural environments. It has been tested in a realistic problem in end oscopy, performing image interpretation with good results. We consider that it can be applied in other domains, providing a coherent basis f or developing knowledge-based vision systems.