Clutter modeling in infrared images using genetic programming
Abbreviated Journal Title
clutter modeling; genetic programming; infrared; target detection; visual perception; data modeling; NATURAL SCENES; SEARCH; Optics
Background clutter characterization in infrared imagery has become an actively researched field, and several clutter models have been reported. These models attempt to evaluate the target detection and recognition probabilities that are characteristic of 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 general mathematical formulas is controversial, in this paper, we introduce the idea of adaptive models that are dynamically derived from a set of examples by a supervised learning mechanism based on genetic programming foundations. 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 correlations between these features and detection performance results obtained by visual observer tests on the same set of images are captured into models by a learning algorithm. The effectiveness of the adaptive modeling principle is discussed in the final part of the paper. (C) 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)01109-0].
"Clutter modeling in infrared images using genetic programming" (2000). Faculty Bibliography 2000s. 2846.