LLN-based Model-Driven Validation of Data Points for Random Sample Consensus Methods

This paper presents an on-the-fly model-driven validation of data points for random sample consensus methods (RANSAC). The novelty resides in the idea that an analysis of the outcomes of previous random model samplings can benefit subsequent samplings. Given a sequence of successful model samplings, information from the inlier sets and the model errors is used to provide a validness of a data point. This validness is used to guide subsequent model samplings, so that the data point with a higher validness has more chance to be selected. To evaluate the performance, the proposed method is applied to the problem of the line model fitting and the estimation of the fundamental matrix. Experimental results confirm that the proposed algorithm improves the performance of RANSAC in terms of the estimate accuracy and the number of samplings.