We investigated the possibility of using an artificial neural network as a translation invariant target detector. The one-dimensional target detection model was a linear array of 20 pixels of which three were unity and the remainder were zero. Several multi-layer back progagation networks were able to distinguish a target consisting of three contiguous pixels from a nontarget three non-contiguous pixels. Under-constrained models were not trainable. A detailed analysis was done of one network with a small number of connections. The network solution appeared to be similar to a triplet correlat ion funct ion. 1.
Jon P. Davis,
William A. Schmidt,
"Position-invariant target detection by a neural net", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21163; https://doi.org/10.1117/12.21163