A neural network approach to target recognition

Summary form only given, as follows. An approach to distortion-invariant target identification and target classification based on the adaptive resonance theory II (ART-II) is presented. The neural network used demonstrates fast unsupervised learning coupled with stable retention and retrieval of information. To keep the dimensions of the network to a bare minimum and to speed up the computational process as well as to achieve invariance, six distortion-invariant features are extracted from each target image and are used as network inputs. These continuous valued features are derived from the geometrical moments of the image. The ART-based target recognition system (ATRS) can be used in two different modes, (a) as a target classifier and (b) as a target recognition system. Several parameters associated with the network itself allow for greater flexibility in feature manipulation. The ATRS is found to perform well on three major counts: speed of processing, flexibility in feature manipulation, and noise tolerance. The determination of critical settings of the various parameters associated with the network is crucial in tuning the ATRS to ensure stable and consistent performance.<
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