A trainable software system known as JARtool 2.0 has been developed to help scientists find localized objects of interest ("target objects") in image data bases. A human expert implicitly trains the system by using a graphical user interface (see figure) to circle all examples of the target object within a set of images. From the user-provided examples, the system learns an appearance model that can be used to detect the target object in previously unseen images.
JARtool 2.0 is built on top of an image display and graphical user interface program called "SAOtng 1.7," which was developed by the Smithsonian Astrophysics Observatory. JARtool utilizes the basic image labeling and browsing capabilities of SAOtng, but also incorporates components that perform matched filtering, principal components analysis, and supervised classification. These components provide the trainable pattern recognition capability.
In the original application for which it was developed, JARtool has been used to locate small volcanoes in synthetic aperture radar (SAR) images of Venus returned by the Magellan spacecraft. However, the system can be applied to other domains. The user must simply supply a new set of training examples for the new class of target objects; there is little or no need for explicit reprogramming.
This work was done by Michael Burl, Usama Fayyad, Padhraic Smyth, Pietro Perona, Saleem Mukhtar, Maureen Burl, Lars Asker, Jayne Aubele, Larry Crumpler, and Joseph Roden for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com under the Mathematics and Information Sciences category, or circle no. 156 on the TSP Order Card in this issue to receive a copy by mail ($5 charge).This software is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393-3425. Refer to NPO-20213.
This Brief includes a Technical Support Package (TSP).
System for locating objects of interest in image data bases
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