A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Antialiasing the Hough transform
CVGIP: Graphical Models and Image Processing
Ten lectures on wavelets
CVGIP: Image Understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Singular Features in Sea Surface Temperature Data
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Circle detection on images using genetic algorithms
Pattern Recognition Letters
DS'06 Proceedings of the 9th international conference on Discovery Science
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Automatic evaluation of solid state track detectors by artificial vision
Computers and Electrical Engineering
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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.