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A. J. Katz and P. R. Thrift
ABSTRACT
Describes results obtained from applying genetic algorithms to the problem of detecting targets in image data. The method described is a two-layered approach, with the first layer providing a focus-of-attention function for the second layer. The first layer is called a Screener and selects subimages from the original image data to be processed by the second layer, called the Classifier. The Screener reduces the computational load of the system. Each layer consists of a set of linear operators (filters) applied directly to the image data. A genetic algorithm is applied to populations of filters based on fitness criteria. The authors note that the statistical classifier chosen for the Classifier stage drives the evolution of filters that are useful for that classifier to make good discriminations. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Generating Image Filters for Target Recognition by Genetic LearningSite generated on Friday, 06 January 2006