Random neural network recognition of shaped objects in strong clutter
Hakan Bakırcıoğlu, Erol Gelenbe · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
Detecting objects in images containing strong clutter is an important issue in a variety of applications such as medical imaging and automatic target recognition. Artificial neural networks are used as non-parametric pattern recognizers to cope with different problems due to their inherent ability to learn from training data. In this paper we propose a neural approach based on the Random Neural Network model (Gelenbe 1989, 1990, 1991, 1993), to detect shaped targets with the help of multiple neural networks whose outputs are combined for making decisions.