Generalized neural networks for tactical target image segmentation
Gregory L. Tarr, Steven K. Rogers, Matthew Kabrisky, Mark E. Oxley, Kevin L. Priddy · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
ABSTRACTA generalized formalism for feedforward neural networks is presented. This generalized architectureis shown to be capable of mapping many common neural network paradigms into a single architecture.Using an intrinsically iterable element, neural networks can be used to compute common preprocessingtechniques including Karhunen-Loeve reduction, Fourier and Gabor spectral decomposition and some wavelet techniques. The generalized architecture is applied to a problem in tactical target image segmentation. 1. A Generalized Neural Network AlgorithmA generalized formulation of feedforward network node can be represented by the equation: Zkfh(XTAX+WTX+Ok) (1)Where T,T, 4, and 9k are propagation constants or weights, and zk is the output of a particular node. Thisformulation, by not specifically addressing training, allows for simple implementation of many commonneural network paradigms, as well as many common preprocessing techniques which include Karhunen-Loeve reduction, Fourier and Gabor spectral decomposition in a connectionist network architecture.Cybenko showed that one hidden layer is sufficient for any multivariate function approximation.3Oxley9 et al proved that the output of the hidden layers can be, not only a sigmoid non-linearity, butalso a negative exponential.9 While Cybenko and others