The proposed recognition system consists of four stages and includes different kinds of artificial neural networks: Preprocessing is done by a Gabor-based wavelet transform. A Dynamic Link Matching algorithm, extended by several modifications, forms the second stage. It implements recognition and learning of the view classes. The temporal order of the views is recorded by a STORE network which transforms the output for a presented sequence of views into an item-and-order coding. A subsequent Gaussian-ARTMAP architecture is used for the classification of the sequences and for their mapping onto object classes by means of supervised learning.
The results achieved with this system show its capability to autonomously learn and to recognize considerably similar objects. Furthermore the given examples illustrate the benefits for object recognition stemming from the utilization of the temporal context. Ambiguous views become manageable and a higher degree of robustness against misclassifications can be accomplished.