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Proceedings Paper

Operator for object recognition and scene analysis by estimation of set occupancy with noisy and incomplete data sets
Author(s): S. John Rees; Bryan F. Jones
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Paper Abstract

Once feature extraction has occurred in a processed image, the recognition problem becomes one of defining a set of features which maps sufficiently well onto one of the defined shape/object models to permit a claimed recognition. This process is usually handled by aggregating features until a large enough weighting is obtained to claim membership, or an adequate number of located features are matched to the reference set. A requirement has existed for an operator or measure capable of a more direct assessment of membership/occupancy between feature sets, particularly where the feature sets may be defective representations. Such feature set errors may be caused by noise, by overlapping of objects, and by partial obscuration of features. These problems occur at the point of acquisition: repairing the data would then assume a priori knowledge of the solution. The technique described in this paper offers a set theoretical measure for partial occupancy defined in terms of the set of minimum additions to permit full occupancy and the set of locations of occupancy if such additions are made. As is shown, this technique permits recognition of partial feature sets with quantifiable degrees of uncertainty. A solution to the problems of obscuration and overlapping is therefore available.

Paper Details

Date Published: 1 November 1992
PDF: 9 pages
Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131537
Show Author Affiliations
S. John Rees, Univ. of Glamorgan (United Kingdom)
Bryan F. Jones, Polytechnic of Wales (United Kingdom)


Published in SPIE Proceedings Vol. 1825:
Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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