Point Probe Decision Trees for Geometric Concept Classes (1993)
| Citations: | 7 - 5 self |
BibTeX
@MISC{Arkin93pointprobe,
author = {Esther Arkin and Michael T. Goodrich and Joseph S. B. Mitchell and David Mount and Christine D. Piatko and Steven S. Skiena},
title = {Point Probe Decision Trees for Geometric Concept Classes},
year = {1993}
}
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Abstract
A fundamental problem in model-based computer vision is that of identifying to which of a given set of concept classes of geometric models an observed model belongs. Considering a "probe" to be an oracle that tells whether or not the observed model is present at a given point in an image, we study the problem of computing efficient strategies ("decision trees") for probing an image, with the goal to minimize the number of probes necessary (in the worst case) to determine in which class the observed model belongs. We prove a hardness result and give strategies that obtain decision trees whose height is within a log factor of optimal. These results grew out of discussions that began in a series of workshops on Geometric Probing in Computer Vision, sponsored by the Center for Night Vision and Electro-Optics, Fort Belvoir, Virginia, and monitored by the U.S. Army Research Office. The views, opinions, and/or findings contained in this report are those of the authors and should not be con...
