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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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Published in: | Computers & geosciences 2011-03, Vol.37 (3), p.331-342 |
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cites | cdi_FETCH-LOGICAL-a461t-55687daf7d7dda1cc12104b83868876328eedc3e9f346d3c0aac664f7d9ec7c33 |
container_end_page | 342 |
container_issue | 3 |
container_start_page | 331 |
container_title | Computers & geosciences |
container_volume | 37 |
creator | Chauris, H. Karoui, I. Garreau, P. Wackernagel, H. Craneguy, P. Bertino, L. |
description | 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. |
doi_str_mv | 10.1016/j.cageo.2010.05.009 |
format | article |
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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
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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. 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source | ScienceDirect Journals |
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 |
title | The circlet transform: A robust tool for detecting features with circular shapes |
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