On evidential reasoning in a hierarchy of hypotheses
Artificial Intelligence
Operations Research
Knowledge-based assistant for colonscopy
IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Knowledge Representation and Control in Computer Vision Systems
IEEE Expert: Intelligent Systems and Their Applications
Handling Uncertainty in Knowledge-Based Computer Vision
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
| Hi-index | 0.00 |
Bayesian networks have been used extensively in diagnostic tasks such as medicine, where they represent the dependency relations between a set of symptoms and a set of diseases. A criticism of this type of knowledge representation is that it is. restricted to this kind of task, and that it cannot cope with the knowledge required in other artificial intelligence applications. For example, in computer vision, we require the ability to model complex knowledge, including temporal and relational factors. In this paper we extend Bayesian networks to model relational and temporal knowledge for high-level vision. These extended networks have a simple structure which permits us to propagate probability efficiently. We have applied them to the domain of endoscopy, illustrating how the general modelling principles can be used in specific cases.