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@pagefooting(immediate, left "@c<Technical Report 1996>",
center "@c<Computer Science>",
right "@c<Carnegie Mellon>")
@heading(Evolution-Based Methods for Selecting Point Data for Object Localization:
Applications to Computer-Assisted Surgery)
@heading(CMU-CS-96-183)
@center(@b(Shumeet Baluja, David A. Simon@foot<Robotics Institute, Carnegie Mellon University>))
@center(November 1996)
@center(FTP: CMU-CS-96-183.ps.gz)
@blankspace(1)
@begin(text)

Object localization has applications in many areas of engineering and
science. The goal is to spatially locate an arbitrarily-shaped object.  In
many applications, it is desirable to minimize the number of measurements
collected for this purpose, while ensuring sufficient localization accuracy.
In surgery, for example, collecting a large number of localization
measurements may either extend the time required to perform a surgical
procedure, or increase the radiation dosage to which a patient is exposed.

Localization accuracy is a function of the spatial distribution of discrete
measurements over an object when measurement noise is present. In [Simon
etal.,1995a], metrics were presented to evaluate the information available
from a set of discrete object measurements. In this study, new approaches to
the discrete point data selection problem are described. These include
hillclimbing, genetic algorithms (GAs), and Population-Based Incremental
Learning (PBIL). Extensions of the standard GA and PBIL methods, which
employ multiple parallel populations, are explored. The results of extensive
empirical testing are provided. The results suggest that a combination of
PBIL and hillclimbing result in the best overall performance. A
computer-assisted surgical system which incorporates some of the methods
presented in this paper is currently being evaluated in cadaver trials.

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@begin(transparent,size=10)
@b(Keywords:@ )@c<Object localization, surface-based registration, evolutionary
optimization, genetic algorithms, population-based incremental learning>
@end(transparent)
@blankspace(1line)
@end(text)
@flushright(@b[(24 pages)])