FN Archimer Export Format PT J TI The circlet transform: A robust tool for detecting features with circular shapes BT AF CHAURIS, H. KAROUI, Imen GARREAU, Pierre WACKERNAGEL, H. CRANEGUY, Philippe BERTINO, L. AS 1:1,2;2:1,3;3:3;4:1;5:4;6:5; FF 1:;2:PDG-DOP-DCB-DYNECO-PHYSED;3:PDG-ODE-DYNECO-PHYSED;4:;5:;6:; C1 Mines ParisTech, Ctr Geosci, F-77300 Fontainebleau, France. UPMC, UMR Sisyphe 7619, Paris, France. IFREMER, Plouzane, France. Actimar, Brest, France. Nersc, Bergen, Norway. C2 MINES PARISTECH, FRANCE UNIV PARIS 06, FRANCE IFREMER, FRANCE ACTIMAR, FRANCE NERSC, NORWAY SI BREST SE PDG-DOP-DCB-DYNECO-PHYSED PDG-ODE-DYNECO-PHYSED IN WOS Ifremer jusqu'en 2018 copubli-france copubli-europe copubli-univ-france IF 1.429 TC 16 UR https://archimer.ifremer.fr/doc/00033/14451/11752.pdf LA English DT Article DE ;Circlet transform;Circle detection;Image processing;Multi-scale representation;Computer vision AB 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. PY 2011 PD MAR SO Computers & Geosciences SN 0098-3004 PU Pergamon-elsevier Science Ltd VL 37 IS 3 UT 000288926700007 BP 331 EP 342 DI 10.1016/j.cageo.2010.05.009 ID 14451 ER EF