Recognition of geometric primitives using logic-program and probabilistic-network reasoning methods

Roger C. Munck-Fairwood

doi:10.1117/12.58604

1 March 1992 Recognition of geometric primitives using logic-program and probabilistic-network reasoning methods

Roger C. Munck-Fairwood

Proceedings Volume 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics; (1992) https://doi.org/10.1117/12.58604
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

This paper addresses the issue of recognition of 3-D objects from a potentially very large database of categories of objects, assuming the data are provided in the form of the edges available from a single monocular view, which indicate the discontinuities in depth and surface orientation. The work is partly inspired by the `Recognition by Components' approach suggested fairly recently by Irving Biederman using `geons,' chosen for their qualitatively distinguishable nonmetric viewpoint-invariant properties. The work is also inspired by Richard Gregory's model of human visual recognition which involves probabilistic reasoning, and the regarding of perception as hypothesis. Further, the interpretation of some data can influence the expectation of other data. A novel attempt is made here to apply two automatic reasoning tools to a sub-task of the general recognition process, viz., the recognition of isolated geons in an idealized image. The tools are logic programming and `belief networks' (causal probabilistic networks). Both the tools have the important property of allowing propagation of information in both directions, i.e., data to hypotheses, and vice-versa. The results to date show good patterns of reasoning consistent with one's intuition and point to the possibility of appropriately `tuning' some feature detectors according to other data received. Future goals include the recognition of geons from real gray-level image data, the extension of the belief network to composite objects, and the use of a reverse-driven image analysis logic program to generate graphics and thereby identify appropriate model constraints.

Citation Download Citation

Roger C. Munck-Fairwood "Recognition of geometric primitives using logic-program and probabilistic-network reasoning methods", Proc. SPIE 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics, (1 March 1992); https://doi.org/10.1117/12.58604

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