Taxonomy of interpretation trees

Patrick J. Flynn; Anil K. Jain

doi:10.1117/12.57091

1 February 1992 Taxonomy of interpretation trees

Patrick J. Flynn, Anil K. Jain

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57091
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

This paper explores alternative models of the interpretation tree (IT), whose search is one of the dominant paradigms for object recognition. Recurrence relations for the unpruned size of eight different types of search tree are introduced. Since exhaustive search of the IT in most recognition systems is impractical, pruning of various types is employed. It is therefore useful to see how much of the IT will be explored in a typical recognition problem. Probabilistic models of the search process have been proposed in the literature and used as a basis for theoretical bounds on search tree size, but experiments on a large number of images suggest that for 3-D object recognition from range data, the error probabilities (assumed to be constant) display significant variation. Hence, the theoretical bounds on the interpretation tree's size can serve only as rough estimates of the computational burden incurred during object recognition.

Citation Download Citation

Patrick J. Flynn and Anil K. Jain "Taxonomy of interpretation trees", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57091

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