Within the frameworks of hierarchical neural feed-forward architectures for performing real-
world 3D invariant object recognition a technique is proposed that shares components like
weight-sharing (2), and pooling stages (3, 5) with earlier approaches, but focuses on new
methods for determining optimal feature-detecting units in intermediate stages (4) of the
hierarchical network. A new approach for training the hierarchical network is proposed
which uses statistical means for (incrementally) learning new feature detection stages and …