A new neural net is described that can easily and cost-effectively accommodate multiple objects in the field of view in parallel. The use of a correlator achieves shift invariance and accommodates multiple objects in parallel. Distortion-invariant filters provide aspect-invariant distortion. Symbolic encoding, the use of generic object parts, and a production system neural net allow large class problems to be addressed. Optical laboratory data on the production system inputs are provided and emphasized. Test data assume binary inputs, although analog (probability) input neurons are possible.
© 1992 Optical Society of America
Original Manuscript: February 5, 1991
Published: March 10, 1992
David Casasent and Elizabeth Botha, "Optical correlator production system neural net," Appl. Opt. 31, 1030-1040 (1992)