Title :
Compact algorithm for strictly ML ellipse fitting
Author :
Kanatani, Kenichi ; Sugaya, Yasuyuki
Author_Institution :
Dept. of Comput. Sci., Okayama Univ., Okayama
Abstract :
A very compact algorithm is presented for fitting an ellipse to points in images by maximum likelihood (ML) in the strict sense. Although our algorithm produces the same solution as existing ML-based methods, it is probably the simplest and the smallest of all. By numerical experiments, we show that the strict ML solution practically coincides with the Sampson solution.
Keywords :
curve fitting; image processing; maximum likelihood estimation; Sampson solution; compact algorithm; maximum likelihood; strictly ML ellipse fitting; Computer science; Equations; Fitting; Gaussian noise; Layout; Least squares methods; Maximum likelihood estimation; Noise reduction; Shape; Voting;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761605