This toolbox provides an engine style design where the estimate and error computing are customizable using a callback function.
The model I used was a simple translation model. The algorithm would take 2 features sets from two video frames then compute the translation vector (for stabilization).
What is important is that for each frame, 10 feature points were calculated. Now the correspondence is unknown between the two frames. Therefore I had to replicated the vectors to give the RANSAC algorithm all possible correspondences. It then selects which pairs are in consensus.
Below are plots of the results. The inliers selected by RANSAC are indicated by the boxed points.
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