Abstract
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.
Funded by the National Council for Scientific Research-CNPq, Brazil grant number 260185/92.2, by the APGD-DARPA project contract number DACA76-97-K-0005, and by Army Research Office, contract number DAAG55-97-1-0188
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andersen, S., Olesen, K., Jensen, F., and F., J. Hugin-a shell for building bayesian belief universes for expert systems. In Proceedings of the 11th International Congress on Uncertain Artificial Intelligence (1989), pp. 1080–1085.
Brooks, R. Symbolic reasoning among 3-d models and 2-d images. Artificial Intelligence 17 (1981), 285–348.
Brown, C., Marengoni, M., and Kardaras, G. Bayes nets for selective perception and data fusion. In Proceedings of the SPIE on Image and Information Systems: Applications and Opportunities (1994).
Collins, R., Cheng, Y., Jaynes, C., Stolle, F., Wang, X., Hanson, A., and Riseman, E. Site model acquisition and extension from aerial images. In Proceedings of the Interantional Conference on Computer Vision (1995), pp. 888–893.
Collins, R., Jaynes, C., Y. Cheng, X. W., Stolle, F., Hanson, A., and Riseman, E. The ascender system: Automated site modelling from multiple aerial images. Computer Vision and Image Understanding-Special Issue on Building Detection and Reconstruction from Aerial Images (1998), to appear.
Cooper, G. The computational complexity of probabilistic inference using bayesian belief networks. Artificial Intelligence 42 (1990), 393–405.
Draper, B., Collins, R., Broglio, J., Hanson, A., and Riseman, E. The schema system. International Journal of Computer Vision 2 (1989), 209–250.
Hanson, A., and Riseman, E. Visions: A computer system for interpreting scenes. In Computer Vision Systems, A. Hanson and E. Riseman, Eds. Academic Press, 1978.
Haralick, R., and Shapiro, L. Computer and Robot Vision. Addison-Wesley, 1993.
Herman, M., and Kanade, T. Incremental reconstruction of 3-d scenes from multiple complex images. Artificial Intelligence 30 (1986), 289–341.
Jaynes, C., and et.al. Three-dimensional grouping and information fusion for site modeling from aerial images. DARPA Image Understanding Workshop (1996), 479–490.
Jaynes, C., Marengoni, M., Hanson, A., and Riseman, E. 3d model acquisition using a bayesian controller. In Proceedings of the International Symposium on Engineering of Intelligent Systems, Tenerife, Spain (1998), p. To appear.
Jaynes, C., Stolle, F., and Collins, R. Task driven perceptual organization for extraction of rooftop polygons. IEEE Workshop on Applications of Computer Vision (1994), 152–159.
Jensen, F. An introduction to Bayesian networks. Springer Verlag New York, 1996.
Kumar, V., and Desai, U. Image interpretation using bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(1) (1996), 74–77.
Lindley, D. Making Decisions: Second Edition. John Wiley and Sons, 1985.
Mann, W., and Binford, T. An example of 3-d interpretation of images using bayesian networks. DARPA Image Understanding Workshop (1992), 793–801.
McKeown, D., Jr., A. W., and McDermott, J. Rule-based interpretation of aerial imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7 (1985), 570–585.
Pearl, J. Probabilistic Reasoning in Intelligent System: Networks of Plausible Inference. Morgan Kaufmann, 1988.
Rimey, R., and Brown, C. Task-oriented vision with multiple bayes nets. In Active Vision, B. A. and Y. A., Eds. The MIT Press, 1992.
Strat, T. Employing contextual information in computer vision. In Proceedings of ARPA Image Understanding Workshop (1993).
Wang, C., and Srihari, S. A framework for object recognition in a visually complex environment and its application to locating address blocks on mail pieces. International Journal of Computer Vision 2 (1988), 125–151.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marengoni, M., Jaynes, C., Hanson, A., Riseman, E. (1999). Ascender II, a Visual Framework for 3D Reconstruction. In: Computer Vision Systems. ICVS 1999. Lecture Notes in Computer Science, vol 1542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49256-9_28
Download citation
DOI: https://doi.org/10.1007/3-540-49256-9_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65459-9
Online ISBN: 978-3-540-49256-6
eBook Packages: Springer Book Archive