| 摘要 |
<p>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 significantly reduces the training effort for complex pattern recognition tasks, compared to the prior art. The incremental learning is based on detecting increasingly statistically independent features in higher stages of the processing hierarchy. Since this learning is unsupervised, no teacher signal is necessary and the recognition architecture can be pre-structured for a certain recognition scenario. Only a final classification step must be trained with supervised learning, which reduces significantly the effort for the adaptation to a recognition task. Due to the improved learning efficiency, not only two dimensionally objects, but also three dimensional objects with variations of three dimensional rotation, size and lightning conditions can be recognized. As another advantage this learning method is viable for arbitrary nonlinearities between stages in the hierarchical convolutional networks, like e.g. non-differentiable Winner-Take-All nonlinearities. In contrast thereto the technology according to the abovementioned prior art can only perform backpropagation learning for differentiable nonlinearities which poses certain restrictions on the network design. <IMAGE></p> |
