Ellipse Fitting Algorithm Based on Heteroscedastic Theory
Fang Cao, Yang Zhong-gen · Jisuanji gongcheng · 2008
【Abstract】This paper analyzes the usual algorithms with less anti-jamming ability which are sensitive to the effect of noise in the application of ellipse fitting, and proposes a more robust ellipse fitting algorithm. It utilizes the heteroscedastic regression technique to create the Errors-In-Variables (EIV) model. According to the observation of data vector, the optimal algorithm is found to obtain the optimal estimations of EIV model parameters and the truth-value of the observed data vector. Experimental results show that the algorithm is more accurate and can converge steadily and rapidly, when original data is far from exact value. 【Key words】computer vision; ellipse fitting; heteroscedastic