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==Advantages and disadvantages==
{{refimprove section|date=September 2014}}
An advantage of RANSAC is its ability to do [[robust statistics|robust estimation]]<ref>Robust Statistics, Peter. J. Huber, Wiley, 1981 (republished in paperback, 2004), page 1.</ref> of the model parameters, i.e., it can estimate the parameters with a high degree of accuracy even when a significant number of [[outlier]]s are present in the data set. A disadvantage of RANSAC is that there is no upper bound on the time it takes to compute these parameters (except exhaustion). When the number of iterations computed is limited, the solution obtained may not be optimal, and it may not even be one that fits the data in a good way. In this way RANSAC offers a trade-off; by computing a greater number of iterations, the probability of a reasonable model being produced is increased. Moreover, RANSAC is not always able to find the optimal set even for moderately contaminated sets, and it usually performs badly when the number of inliers is less than 50%. Optimal RANSAC
RANSAC can only estimate one model for a particular data set. As for any one-model approach when two (or more) model instances exist, RANSAC may fail to find either one. The [[Hough transform]] is one alternative robust estimation technique that may be useful when more than one model instance is present. Another approach for multi
==Applications==
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