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Proceedings Paper

Robustness in primitive extraction and correspondence computation
Author(s): Gerhard Roth; Martin D. Levine
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Paper Abstract

Two related and important problems in the field of model-based computer vision are the extraction of predefined primitives from geometric data, and the computation of correspondences among such primitives. We show that both problems are equivalent to the optimization of a cost function, which often has very many local minima. One implication of this model is that a robust algorithm for these problems must find the global minimum of the associated cost function from among the local minima. The minimal subset principle states that a small subset of a set is often able to encode the characteristics of the entire set. For primitive extraction a minimal subset is the smallest number of points necessary to define a geometric primitive. Similarly, for correspondence computation a minimal subset is the smallest number of correspondences between geometric and model primitives necessary to define a pose (position and orientation) of the model. Randomly choosing such minimal subsets and evaluating them by using a cost function is a general and robust way to perform primitive extraction and correspondence computation. The main difficulty with this approach is that sometimes a large number of random samples (and therefore cost function evaluations) are necessary. We use a genetic algorithm to decrease the number of random samples significantly, and therefore to decrease the execution time. Some approaches to speeding up minimal subsets using algorithms on different parallel architectures are also described.

Paper Details

Date Published: 30 April 1992
PDF: 13 pages
Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); doi: 10.1117/12.57952
Show Author Affiliations
Gerhard Roth, National Research Council Canada (Canada)
Martin D. Levine, McGill Univ. (Canada)


Published in SPIE Proceedings Vol. 1611:
Sensor Fusion IV: Control Paradigms and Data Structures
Paul S. Schenker, Editor(s)

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