Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural processing of targets in visible, multispectral IR and SAR imagery
Neural Networks - Special issue: automatic target recognition
Classifier and shift-invariant automatic target recognition neural networks
Neural Networks - Special issue: automatic target recognition
Sonar recognition of targets embedded in sediment
Neural Networks - Special issue: automatic target recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A neural network filter to detect small targets in high clutter backgrounds
IEEE Transactions on Neural Networks
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We introduce a two stage approach to the use of pixel based neural networks for finding relatively small objects in large pictures. In the first stage the network is trained by using a back propagation algorithm and a genetic algorithm on sample objects which have been cut out from the large pictures. In the second stage the weights of the two trained networks are refined on the full training images using a second genetic algorithm. Four methods are formed by the two training algorithms in stage one and combining them with the genetic algorithm in stage two. We have tested these methods on three object detection problems of increasing diffculty. In all cases the methods with the refined genetic algorithm resulted in improved detection performance over those without the refinement. The method with the best performance is the use of the back propagation algorithm for the network training and the genetic algorithm for the network refinement.