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Summary and Conclusions

  • Sven Behnke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2766)

Abstract

In order to overcome limitations of current computer vision systems, this thesis proposed an architecture for image interpretation, called Neural Abstraction Pyramid. This hierarchical architecture consists of simple processing elements that interact with their neighbors. The recurrent interactions are described be weight templates.Weighted links form horizontal and vertical feedback loops that mediate contextual influences. Images are transformed into a sequence of representations that become increasingly abstract as their spatial resolution decreases, while feature diversity as well as invariance increase. This process works iteratively. If the interpretation of an image patch cannot be decided locally, the decision is deferred, until contextual evidence arrives that can be used as bias. Local ambiguities are resolved in this way.

Keywords

Processing Element Recurrent Neural Network Cellular Neural Network Recurrent Network Implementation Option 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Sven Behnke

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