Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/39935
Type: Conference paper
Title: Maximum-likelihood circle-parameter estimation via convolution
Author: Zelniker, Emanuel Emil
Clarkson, I. Vaughan L.
Citation: Digital image computing : techniques and applications ; proceedings of the VIIth Biennial Australian Pattern Recognition Society Conference, DICTA 2003 / Sun C., Talbot H., Ourselin S. and Adriaansen T. (eds.), pp. 509-518.
Publisher: CSIRO Publishing
Issue Date: 2003
ISBN: 064309041X
Conference Name: Australian Pattern Recognition Society. Conference (7th : 2003 : Sydney, N.S.W.)
School/Discipline: School of Computer Science
Statement of
Responsibility: 
Emanuel E. Zelniker and I. Vaughan L. Clarkson
Abstract: In this paper, we present an interpretation of the Maximum Likelihood Estimator (MLE) and the Delogne-K˚asa Estimator (DKE) for circle-parameter estimation via convolution. Under a certain model for theoretical images, this convolution is an exact description of the MLE. We use our convolution based MLE approach to find good starting estimates for the parameters of a circle, that is, the centre and radius. It is then possible to treat these estimates as preliminary estimates into the Newton-Raphson method which further refines these circle estimates and enables sub-pixel accuracy. We present closed form solutions to the Cram´er-Rao Lower Bound of each estimator and discuss fitting circles to noisy points along a full circle as well as along arcs. We compare our method to the DKE which uses a least squares approach to solve for the circle parameters.
Keywords: circle-parameter estimation; convolution; estimators; likelihood
RMID: 0020073815
Appears in Collections:Computer Science publications

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