Layered object recognition system using a hierarchical hybrid neural network architecture
Srinivasan Raghavan, Naresh Gupta, Barbara A. Lambird, David Lavine, Laveen Kanall · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
A layered object recognition paradigm is described in this paper. The lower layers of the proposed system extracts rich feature information in the sense of a primal sketch including oriented edges, blobs, corners, and texture primitives from a raw image. The middle layers of the system extracts object parts such as faces, sides and adjacency relationships between them. The highest layers of the system use the information obtained beneath them to recognize the objects. The system consists of a combination of different types of neural networks making appropriate use of their different capabilities. That is, a collection of unsupervised neural networks are employed for generic feature extraction, while a similar collection of supervised networks are employed for learning object-specific shape information. We present some results of a partial implementation of this system.