Incremental reconstruction of 3D scenes from multiple, complex images
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Task-oriented vision with multiple Bayes nets
Active vision
Image Interpretation Using Bayesian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
The ascender system: automated site modeling from multiple aerial images
Computer Vision and Image Understanding
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Computer and Robot Vision
Site model acquisition and extension from aerial images
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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This paper presents interim results from an ongoing project on aerial image reconstruction. One important task in image interpretation is the process of understanding and identifying segments of an image. In this effort a knowledge based vision system is being presented, where the selection of IU algorithms and the fusion of information provided by them is combined in an efficient way. In our current work, the knowledge base and control mechanism (reasoning subsystem) are independent of the knowledge sources (visual subsystem). This gives the system the flexibility to add or change knowledge sources with only minor changes in the reasoning subsystem. The reasoning subsystem is implemented using a set of Bayesian networks forming a hierarchical structure which allows an incremental classification of a region given enough time. Experiments with an initial implementation of the system focusing primarily on building reconstruction on three different data sets are presented.