Multisensor object segmentation using a neural network
Patrick T. Gaughan, Gerald M. Flachs, Jay B. Jordan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
A neural network architecture is presented to segment objects using multiple sensor/feature images. The neural architecture consists of a region growing net to separate an object from the surrounding background based upon local statistical properties. The region growing net consists of a lattice of neural processing elements for propagating a similarity activity between image pixels. A potential function approach is presented to define the neural weights by measuring pixel similarity in multisensor/feature images. The performance of the neural segmenter is demonstrated by comparing its performance to that of an architecture using a statistical decision theoretic technique.