Introduction fig. 1 picture from 'Multiview geometry in Computer Vision' book. If you use orthogonal regression ( minimizes the sum of squared perpendicular distances -- LMS ), there will be a problem if you have an outliers (see fig 1a). RANSAC algorithm RANSAC algorithm will cope with this problem by discarding outliers. Slide from 25th year of RANSAC, Philip Torr slides has very clear picture of the algorithm. support = number of points that lie within a distance threshold points within the threshold distance of a line with most support are the inliers. If a point is an outliers , a line will not have so much support. ( see fig 1b from mvg book above ) Explain the algorithm - First we randomly pick two red point and estimate m, c for y=mx+c ( this is easy, right ) - for consider if a point is a inlier for every yellow point (x, y) If | y - (m*x + c) | < t, number of inlier need to justify model -- re-estimate m, c again by using all ...
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