Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Classifier and shift-invariant automatic target recognition neural networks
Neural Networks - Special issue: automatic target recognition
Target detection in SAR imagery by genetic programming
Advances in Engineering Software
Pixel statistics and false alarm area in genetic programming for object detection
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
A neural network filter to detect small targets in high clutter backgrounds
IEEE Transactions on Neural Networks
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This paper describes a domain independent approach to the use of neural networks (NNs) and genetic programming (GP) for object detection problems. Instead of using high level features for a particular task, this approach uses domain independent pixel statistics for object detection. The paper first compares an NN method and a GP method on four image data sets providing object detection problems of increasing difficulty. The results show that the GP method performs better than the NN method on these problems but still produces a large number of false alarms on the difficult problem and computation cost is still high. To deal with these problems, we develop a new method called GP-refine that uses a two stage learning process. The new GP method further improves object detection performance on the difficult detection task.