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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Behnke, S. (2003). Neural Abstraction Pyramid Architecture. In: Hierarchical Neural Networks for Image Interpretation. Lecture Notes in Computer Science, vol 2766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45169-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-45169-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40722-5
Online ISBN: 978-3-540-45169-3
eBook Packages: Springer Book Archive