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1 August 1990 Artificial neural networks for automatic target recognition
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Abstract
This paper will review recent advances in the applications of artificial neural network technology to problems in automatic target recognition. The application of feedforward networks for segmentation feature extraction and classification of targets in Forward Looking Infrared (FLIR) and laser radar range scenes will be presented. Biologically inspired Gabor functions will be shown to be a viable alternative to heuristic image processing techniques for segmentation. The use of local transforms such as the Gabor transform fed into a feedforward network is proposed as an architecture for neural based segmentation. Techniques for classification of segmented blobs will be reviewed along with neural network procedures for determining relevant features. A brief review of previous work on comparing neural network based classifiers to conventional Bayesian and K-nearest neighbor techniques will be presented. Results from testing several alternative learning algorithms for these neural network classifiers are presented. A technique for fusing information from multiple sensors using neural networks is presented and conclusions are made. 1
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Steven K. Rogers, Dennis W. Ruck, Matthew Kabrisky, and Gregory L. Tarr "Artificial neural networks for automatic target recognition", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21151
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