Abstract #301065

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JSM 2003 Abstract #301065
Activity Number: 329
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #301065
Title: Approximate Posterior Probabilities for System Evaluation of an Image Analysis Problem
Author(s): Thomas L. Burr*+ and C. James Elliott and Herb Fry and Brian McVey and Eric Sander
Companies: Los Alamos National Laboratory and Los Alamos National Laboratory and Los Alamos National Laboratory and Los Alamos National Laboratory and National Nuclear Security
Address: 2429 35th St., Los Alamos, NM, 87544-1555,
Keywords: image ; analysis ; Bayesian ; model ; selection
Abstract:

Remote detection and identification of chemicals in a scene is a challenging problem. One approach uses some of the image's pixels to establish the background characteristics, while other pixels represent the target for which we seek to identify all chemical species present. This leads to a generalized least squares problem where "subset selection" is used identify the chemicals thought to be present. Bayesian model selection allows us to approximate the posterior probability that each chemical in the library is present by adding the posterior probabilities of all the subsets which include the chemical. We introduce approximate results for the mean and variance of these probabilities that are available for a system evaluation prior to observing real data. We use simulated data to evaluate the quality of these results.

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