KNOWLEDGE BASED 3D-MODELLING BY SELFORGANISED LEARNING ALGORITHMS - Image understanding based on automated knowledge refinement

Eckhard Büscher

Abstract

This paper discusses the design and implementation of a knowledge based Modelling system KMS, which combines semantic and rule based approaches in the modelling process. The design and implementation of the semantic concepts are controlled dynamically to achieve an optimal degree of reality and to employ efficient interactivity and accessibility for the user. The model-based controlling module is developed to achieve efficiency and consistence in the basic analysis process, and to avoid the static structure that frequently occurs in data driven systems. By using a hypothesis and verification scheme in order to ensure interactivity and accessibility without sacrificing efficiency the KMS evokes the important task of merging the use of heuristic knowledge in form of a knowledge base with domain specific requirements. By detecting contradicting and inconsistent rules and by performing tests in the knowledge base and finally by creating new hypothesis to solve the problems, the controlling process also provides the decision module with a concept for automated knowledge refinement. This paper focuses on the implementation and Multimedia adaptation of the learning processes in correlation with the linked databases.

References

  1. Zadeh, L.A., 1994. Fuzzy logic, neural networks, and soft computing. In Communications of the ACM. vol. 37, pp. 77-84.
  2. Jang, J.S.R., 1993. ANFIS adaptive network based fuzzy inference systems. In IEEE Transactions on Systems, Man, and Cybernetics. vol. 23, pp. 665-685.
  3. Dague, P., Raiman, O., and Deves, P., 1987. Troubleshooting: When modelling is the difficulty, In Proceedings of 6th National Conference on Artificial Intelligence, Seattle, WA, August, pp. 600-605.
  4. Mosterman, P.J., and Biswas, G., 1999 .Diagnosis of continuous valued systems in transient operating regions, In IEEE Transactions on Systems, Man, and Cybernetics, vol. 29, no. 6, pp. 554-565.
  5. Fink, P.K., and Lusth, J.C., 1987 .Expert systems and diagnostic expertise in the mechanical and electrical domains, In IEEE Trans.on Systems, Man, and Cybernetics, vol. SMC-17, pp. 340-349.
  6. Canton, R., Pipitone, F., Lander, W., and M. Marrone, 1983 .Model-based probabilistic reasoning for electronics troubleshooting, In Proceedings of 8th IJCAI, pp. 207-211.
  7. Patil, R.S., 1981 .Causal representation of patient illness for electrolyte and acid-based diagnosis, In Technical Report, no. 267, Massachussetts Institute of Technology, Laboratory for Computer Science, Cambridge, MA.
  8. Huang, Y., and Miles, R., 1996 .Using case-based techniques to enhance constraint satisfaction problem solving, In Applied Artificial Intelligence, an International Journal, vol. 10, no. 4.
  9. Faugeras, O., 1995 .Stratification of 3-D Vision: Projective, Affine, and Metric Representations, In J. Optical Soc. Am. A, vol.12, pp. 465-484.
  10. Büscher, E., 2000 .Generation of 3D - Scenes by knowledge based Adaption of parametric Models, In Proc. of the IASTED International Conference. INTERNET AND MULTIMEDIA SYSTEMS AND APPLICATIONS Nev. USA, pp. 411-417.
  11. Bowyer, K. W. and Dyer, C. R., 1994 .Three-dimensional shape representation, In Handbook of Pattern Recognition and Computer Vision, Volume 2: Computer Vision (T. Y Young, Ed.), New York, Academic Press, pp.17-51.
  12. Hepner, 1994 .Artificial Neural Network Classification Using a Minimal Training Set: Comparison to Conventional Supervised Classification, In Photogrammetric Engineering and Remote Sensing 56: 469-473.
  13. Kanatani, K., 1996 .Statistical Optimization for Geometric Computation: Theory and Practice, In Amsterdam: Elsevier.
Download


Paper Citation


in Harvard Style

Büscher E. (2004). KNOWLEDGE BASED 3D-MODELLING BY SELFORGANISED LEARNING ALGORITHMS - Image understanding based on automated knowledge refinement . In Proceedings of the First International Conference on E-Business and Telecommunication Networks - Volume 3: ICETE, ISBN 972-8865-15-5, pages 356-362. DOI: 10.5220/0001399203560362


in Bibtex Style

@conference{icete04,
author={Eckhard Büscher},
title={KNOWLEDGE BASED 3D-MODELLING BY SELFORGANISED LEARNING ALGORITHMS - Image understanding based on automated knowledge refinement },
booktitle={Proceedings of the First International Conference on E-Business and Telecommunication Networks - Volume 3: ICETE,},
year={2004},
pages={356-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001399203560362},
isbn={972-8865-15-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on E-Business and Telecommunication Networks - Volume 3: ICETE,
TI - KNOWLEDGE BASED 3D-MODELLING BY SELFORGANISED LEARNING ALGORITHMS - Image understanding based on automated knowledge refinement
SN - 972-8865-15-5
AU - Büscher E.
PY - 2004
SP - 356
EP - 362
DO - 10.5220/0001399203560362