PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Rotational Features for Filament Detection
German Gonzalez, Francois Fleuret and Pascal Fua
Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition 2009.


State-of-the-art approaches for detecting filament-like structures in noisy images rely on filters optimized for sig- nals of a particular shape, such as an ideal edge or ridge. While these approaches are optimal when the image con- forms to these ideal shapes, their performance quickly de- grades on many types of real data where the image deviates from the ideal model, and when noise processes violate a Gaussian assumption. In this paper, we show that by learning rotational fea- tures, we can outperform state-of-the-art filament detection techniques on many different kinds of imagery. More specif- ically, we demonstrate superior performance for the detec- tion of blood vessel in retinal scans, neurons in brightfield microscopy imagery, and streets in satellite imagery.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:6235
Deposited By:Francois Fleuret
Deposited On:08 March 2010