Detection performance prediction on IR images assisted by evolutionary learning

Liviu I. Voicu; Ronald Patton; Harley R. Myler

doi:10.1117/12.352956

14 July 1999 Detection performance prediction on IR images assisted by evolutionary learning

Liviu I. Voicu, Ronald Patton, Harley R. Myler

Proceedings Volume 3699, Targets and Backgrounds: Characterization and Representation V; (1999) https://doi.org/10.1117/12.352956
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

Background clutter characterization in IR imagery has become an actively researched field and several clutter models have been reported. These models attempt to evaluate the target detection/recognition probabilities that are characteristic to a certain scene when specific target and human visual perception features are known. The prior knowledge assumed and required by these models is a severe limitation. Furthermore, the attempt to model subjective and intricate mechanisms such as human perception with simple mathematical formulae is controversial. In this paper, we introduce the idea of adaptive models that are dynamically derived from a set of examples by a supervised evolutionary learning scheme. A set of characteristic scene and target features with a demonstrated influence on the human visual perception mechanism is first extracted from the original images. Then, the correlation between these features and the results obtained by visual observer tests on the same set of images are captured into a model by the learning scheme. The effectiveness of the adaptive modeling principle is discussed in the final part of the paper.

Citation Download Citation

Liviu I. Voicu, Ronald Patton, and Harley R. Myler "Detection performance prediction on IR images assisted by evolutionary learning", Proc. SPIE 3699, Targets and Backgrounds: Characterization and Representation V, (14 July 1999); https://doi.org/10.1117/12.352956

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