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Proceedings Paper

Automatic vision programs from predicted features
Author(s): Prasanna G. Mulgaonkar; Chien-Huei Chen; Bharath Modayur
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Paper Abstract

Techniques are presented for automatically generating optimal vision programs from high- level task descriptions. Vision programs are the object models that describe strategies to recognize and locate objects in an image. The effectiveness of the program depends on the features used for recognition and the order in which the features are evaluated. We describe three probabilistic feature utility measures and a cost function based on program execution time that serve as the basis of our technique. Computation of such utility measures from a statistically representative sample of images has been demonstrated. Problems encountered in computing such measures from computer-generated images are described.

Paper Details

Date Published: 1 November 1992
PDF: 11 pages
Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131615
Show Author Affiliations
Prasanna G. Mulgaonkar, SRI International (United States)
Chien-Huei Chen, SRI International (United States)
Bharath Modayur, SRI International (United States)

Published in SPIE Proceedings Vol. 1826:
Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods
David P. Casasent, Editor(s)

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