The circlet transform: A robust tool for detecting features with circular shapes
Type | Article | ||||||||||||
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Date | 2011-03 | ||||||||||||
Language | English | ||||||||||||
Author(s) | Chauris H.1, 2, Karoui Imen1, 3, Garreau Pierre3, Wackernagel H.1, Craneguy Philippe4, Bertino L.5 | ||||||||||||
Affiliation(s) | 1 : Mines ParisTech, Ctr Geosci, F-77300 Fontainebleau, France. 2 : UPMC, UMR Sisyphe 7619, Paris, France. 3 : IFREMER, Plouzane, France. 4 : Actimar, Brest, France. 5 : Nersc, Bergen, Norway. |
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Source | Computers & Geosciences (0098-3004) (Pergamon-elsevier Science Ltd), 2011-03 , Vol. 37 , N. 3 , P. 331-342 | ||||||||||||
DOI | 10.1016/j.cageo.2010.05.009 | ||||||||||||
WOS© Times Cited | 16 | ||||||||||||
Keyword(s) | Circlet transform, Circle detection, Image processing, Multi-scale representation, Computer vision | ||||||||||||
Abstract | We present a novel method for detecting circles on digital images. This transform is called the circlet transform and can be seen as an extension of classical 1D wavelets to 2D; each basic element is a circle convolved by a 1D oscillating function. In comparison with other circle-detector methods, mainly the Hough transform, the circlet transform takes into account the finite frequency aspect of the data; a circular shape is not restricted to a circle but has a certain width. The transform operates directly on image gradient and does not need further binary segmentation. The implementation is efficient as it consists of a few fast Fourier transforms. The circlet transform is coupled with a soft-thresholding process and applied to a series of real images from different fields: ophthalmology, astronomy and oceanography. The results show the effectiveness of the method to deal with real images with blurry edges. | ||||||||||||
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