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Thinning Based Eccentricity on Volumetric Data

Bakk-Abschlussvortrag Martin Reiterer

  • Presentation
When Nov 28, 2008
from 02:00 pm to 03:00 pm
Where Sem 183/2
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Eccentricity has its roots in graph theory where it describes the length of the shortest path from one node to the node furthest away. This unit of measurement was picked up for describing shapes in digital images. It is called the eccentricity transform and defined in any dimension. Furthermore it is robust against salt & pepper noise and quasi invariant to articulated motion. On the other hand, computation takes a long time for large images, especially for typical three or higher dimensional ones. So in practice, one could consider to perform transformation only on an approximated and consequently reduced version of the initial volumetric dataset. In this presentation a novel approximation is proposed that uses thinning of a given image region to reduce the amount of data for accelerating the eccentricity transform. Based on this concept, detailed studies are performed for verification of its descriptive performance, invariance to articulation and robustness against salt & pepper noise.
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