Probabilistic reasoning on object occurrence in complex scenes

The interpretation of complex scenes requires a large amount of prior knowledge and experience. To utilize prior knowledge in a computer vision or a decision support system for image interpretation, a probabilistic scene model for complex scenes is developed. In conjunction with a model of the observe's characteristics (a human interpreter or a computer vision system), it is possible to support bottom-up inference from observations to interpretation as well as to focus the attention of the observer on the most promising classes of objects. The presented Bayesian approach allows rigorous formulation of uncertainty in the models and permits manifold inferences, such as the reasoning on unobserved object occurrences in the scene. Monte-Carlo methods for approximation of expectations from the posterior distribution are presented, permitting the efficient application even for high-dimensional models. The approach is illustrated on the interpretation of airfield scenes.