Performance of RANSAC Techniques under Classical and Robust Methods
|R. Muthukrishnan1, E.D. Boobalan2, R.Reka2
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Robust statistics deals with deviations from the assumptions on the model and is concerned with construction of statistical procedure which still reliable and reasonably efficient in a neighbourhood of the model. In computer vision, a robust estimator should be able to correctly find the fit when outliers/noise occupies a high percentage of the data. RANSAC is most commonly used robust estimator in computer vision task. It is a nondeterministic algorithm for estimation of a mathematical model from observed data which contains outliers. This paper presents the performance of RANSAC algorithmic techniques under the various methods LS, LMS, LTS and M estimators along with the results of the simulation study which is carried out in MATLAB.