The RANSAC Song

YOUR LINK HERE:

http://youtube.com/watch?v=1YNjMxxXO-E

RANSAC: RANdom SAmple Consensus - a robust method for model fitting in the presence of outliers. • HD video download now (Feb 2024) available at http://danielwedge.com/ransac/ • Lyrics: • When you have outliers you may face much frustration • if you include them in a model fitting operation. • But if your model's fit to a sample set of minimal size, • the probability of the set being outlier-free will rise. • Brute force tests of all sets will cause computational constipation. • N random samples • will provide an example • of a fitted model uninfluenced by outliers. No need to test all combinations! • Each random trial should have its own unique sample set • and make sure that the sets you choose are not degenerate. • N, the number of sets, to choose is based on the probability • of a point being an outlier, and of finding a set that's outlier free. • Updating N as you go will minimise the time spent. • So if you gamble • that N samples are ample • to fit a model to your set of points, it's likely that you will win the bet. • Select the set that boasts • that its number of inliers is the most (you're almost there). • Fit a new model just to those inliers and discard the rest, • an estimated model for your data is now possessed! • This marks the end point of your model fitting quest.

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