Occluded object recognition: an approach which combines neurocomputing and conventional algorithms
Chung-Mong Lee, D.W. Patterson · 1991
A system which combines the power of neural network learning and computing with conventional vision processing methods has been developed. At the heart of the system is a neural network composed of neocognitron and self-created layer components. During the recognition phase, the network computations are augmented by conventional vision algorithms which perform some low- and intermediate-level processing functions. The system is first trained under supervision to recognize several types of nonoccluded objects. It is then used to identify each of the objects appearing in an image even though the objects appear at different locations and are partially occluded or even somewhat deformed. A high degree of accuracy has been achieved with the system.>