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https://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 |
Appears in Collections: | Computer Science publications |
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