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Neural Abstraction Pyramid Architecture

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

Abstract

The last two chapters reviewed what is known about object recognition in the human brain and how the concepts of hierarchy and recurrence have been applied to image processing. Now it is time to put both together.

In this chapter, an architecture for image interpretation is defined that will be used for the remainder of this thesis. I will refer to this architecture as the Neural Abstraction Pyramid. The Neural Abstraction Pyramid is a neurobiologically inspired hierarchical neural network with local recurrent connectivity. Images are represented at multiple levels of abstraction. Local connections form horizontal and vertical feedback loops between simple processing elements. This allows to resolve ambiguities by the flexible use of partial interpretation results as context.

Keywords

Transfer Function Processing Element Human Visual System Feature Cell Output Unit 
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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