Learning Rotational Features for Filament Detection

State-of-the-art approaches for detecting filament-like structures in noisy images rely on filters optimized for signals of a particular shape, such as an ideal edge or ridge. While these approaches are optimal when the image conforms to these ideal shapes, their performance quickly degrades on many types of real data where the image deviates from the ideal model, and when noise processes violate a Gaussian assumption.


Published in:
Cvpr: 2009 Ieee Conference On Computer Vision And Pattern Recognition, Vols 1-4, 1582-1589
Presented at:
International Conference on Computer Vision and Pattern Recognition
Year:
2009
ISBN:
978-1-4244-3992-8
Keywords:
Laboratories:




 Record created 2010-02-11, last modified 2018-03-18

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