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A continued investigation into qualitative reasoning about shape and fit

Published online by Cambridge University Press:  27 February 2009

Stephen P. Carney  and
David C. Brown
Stephen P. Carney
Affiliation:
Symbolics Inc., Eleven Cambridge Center, Cambridge, MA 02142
David C. Brown
Affiliation:
Artificial Intelligence Research Group, Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
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Abstract

Previous research on qualitative reasoning about shape and fit laid the foundations to determine whether two objects fit together. Continued investigation has refined the theory and has produced a functioning implementation. This paper describes extensions to the theory and the details of the implementation.

The reasoning process has been divided into five layers: grouping, topology, orientation, matching, and confirmation. The grouping layer clusters features such as cubes or cylinders into groups for each surface of an object. The topology layer recognizes patterns formed by the groups on each surface, and describes the pattern in terms of topological structures. The orientation layer selects promising surfaces from the two objects and attempts to align the two surfaces. If the orientation layer aligns the topological structures on the two surfaces, the matching layer tries to pair the features within the topological structures. The confirmation layer inspects paired features to determine whether the surfaces are compatible. If the surfaces are compatible, then the two objects qualitatively fit together.

Type
Research Article
Information
AI EDAM , Volume 3 , Issue 2 , May 1989 , pp. 85 - 110
Copyright
Copyright © Cambridge University Press 1989

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References

Ballard, D. and Brown, M. 1982. Computer Vision. Englewood Cliffs: Prentice-Hall.Google Scholar
Beck, J., Prazdny, K. and Rosenfeld, A. 1983. A theory of textural segmentation. In: Beck, J., Hope, B. and Rosenfeld, A. (eds.), Human and Machine Vision. New York: Academic Press, pp. 1–37.Google Scholar
Bobrow, D. 1984. Qualitative reasoning about physical systems: an introduction. In Bobrow, D. (ed.), Qualitative Reasoning about Physical Systems. Amsterdam: Elsevier Science Publishers, pp. 1–5.Google Scholar
Bohuslav, Z., Chlouba, J., Janek, J., Hegner, I., Vlcek, I. and Zoc, I. 1986. Cluster analysis in knowledge identification. Computers and Artificial Intelligence. 5: 365–374.Google Scholar
Bylander, T. and Chandrasekaran, B. 1985. Understanding behaviour using consolidation. In Proceedings of the 9th International Joint Conference on Artificial Intelligence, pp. 450–454.Google Scholar
Dague, P. and Deves, P. 1987. Troubleshooting: when modeling is the trouble. In Proceedings of the 6th National Conference on Artificial Intelligence, pp. 600–605.Google Scholar
de Kleer, J. and Brown, J. 1984. A qualitative physics based on confluences. In: Bobrow, D. (ed.) Qualitative Reasoning about Physical Systems. Amsterdam: Elsevier Science Publishers, pp. 7–83.CrossRefGoogle Scholar
Dixon, J. and Cunningham, J. 1987. Research in designing with features. IFIP Workshop on Intelligent CAD, MIT, October 1987.Google Scholar
Fisher, D. 1987. Improving inference through conceptual clustering. In Proceedings of the 6th National Conference on Artificial Intelligence, pp. 461–465.Google Scholar
Freeman, J. 1975. The modelling of spatial relations. Computer Graphics and Image Processing, New York: Academic Press. 4: 156–171.Google Scholar
Forbus, K. 1984. Qualitative process theory. In: Bobrow, D. (ed.) Qualitative Reasoning about Physical Systems. Amsterdam: Elsevier Science Publishers, pp. 85–167.CrossRefGoogle Scholar
Green, D. 1987. Qualitative reasoning about shape and fit. Master's thesis, Computer Science Department, Worcester Polytechnic Institute.CrossRefGoogle Scholar
Green, S. 1986. SPACES—A system for the representation of commonsense knowledge about space for design. Presented at the 6th Annual Technical Conference on Expert Systems, British Computer Society, Brighton, U.K., December 1986.Google Scholar
Hayes, P. 1985. The second naive physics manifesto. In: Hobbs, J. and Moore, C. (eds.) Formal Theories of the Commonsense World. Norwood: Ablex, pp. 1–36.Google Scholar
Kuipers, B. 1984. Commonsense reasoning about causality: deriving behavior from structure. In: Bobrow, D. (ed.) Qualitative Reasoning about Physical Systems. Amsterdam: Elsevier Science Publishers, pp. 167–203.Google Scholar
Mavrovouniotis, M. and Stephanopoulos, G. 1987. ‘Reasoning with orders of magnitude and approximate relations. In Proceedings of the 6th National Conference on Artificial Intelligence, pp. 626–630.Google Scholar
Natarajan, B. 1988. On planning assemblies, In Proceedings of the 4th ACM Workshop on Computation Geometry, Urbana, IL.CrossRefGoogle Scholar
Palmer, S. 1983. The psychology of perceptual organization: a transformational approach. In: Beck, J.Hope, B. and Rosenfeld, A. (eds.) Human and Machine Vision. New York: Academic Press, pp. 269–337.CrossRefGoogle Scholar
Shoham, Y. 1985. Naive kinematics: one aspect of shape. In Proceedings of the 9th International Joint Conference on Artificial Intelligence, pp. 436–442.Google Scholar
Stepp, R. 1987. Untitled-Conceptual clustering, In Proceedings of the 10th International Joint Conference on Artificial Intelligence, pp. 211–213.Google Scholar
Winston, P. 1984. Artificial Intelligence, 2nd ed. Reading, MA: Addison-Wesley.Google Scholar