The circlet transform: A robust tool for detecting features with circular shapes

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, m...

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Bibliographic Details
Published in:Computers & geosciences 2011-03, Vol.37 (3), p.331-342
Main Authors: Chauris, H., Karoui, I., Garreau, P., Wackernagel, H., Craneguy, P., Bertino, L.
Format: Article
Language:English
Subjects:
Astronomy
Circle detection
Circlet transform
Computer vision
computers
digital images
domain_math.appl
Fourier transforms
Image processing
Mathematical analysis
Mathematics
methodology
Multi-scale representation
Oceanography
Ophthalmology
Transforms
wavelet
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Summary: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.
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2010.05.009