Share Email Print

Optical Engineering

Robust, sensor-independent target detection and recognition based on computational models of human vision
Author(s): Theodore J. Doll; Shane W. McWhorter; Anthony A. Wasilewski; David E. Schmieder
Format Member Price Non-Member Price
PDF $20.00 $25.00

Paper Abstract

Most current artificial vision systems lack robustness and are applicable only to a narrow range of tasks. Crevier (1997) has suggested that this is due to their reliance on a small number of vision mechanisms and to lack of knowledge about how vision algorithms should be integrated. We suggest a systems approach to artificial vision based on computational vision research. The capabilities of biological vision systems are contrasted with those of current piecemeal approaches to artificial vision. A mature, comprehensive vision system, called the Georgia Tech Vision (GTV) simulation is described. GTV incorporates quasilinear filter mechanisms to simulate the processing performed by simple and complex cortical cells. The outputs of these mechanisms are adaptively combined to discriminate targets from clutter and/or one another. GTV outputs predictions of human search and detection performance and/or targeting metrics for automatic target recognition (ATR) applications. Studies validating GTV as a model of human search and detection performance and demonstrating its performance as an ATR are presented.

Paper Details

Date Published: 1 July 1998
PDF: 16 pages
Opt. Eng. 37(7) doi: 10.1117/1.601898
Published in: Optical Engineering Volume 37, Issue 7
Show Author Affiliations
Theodore J. Doll, Georgia Institute of Technology (United States)
Shane W. McWhorter, Georgia Tech Research Institute (United States)
Anthony A. Wasilewski, Georgia Tech Research Institute (United States)
David E. Schmieder, Georgia Tech Research Institute (United States)

© SPIE. Terms of Use
Back to Top