Fitting geometric models to data with outliers
RANSAC (RANdom SAmple Consensus) is an iterative method of fitting a given model to data containing outliers. In the case of 2D or 3D point clouds, a geometric model is defined, and inliers are calculated according to some relationship to the model geometry (e.g. points within a specified distance are marked as inliers). The variable model parameters are randomly generated N times, after which the set with the highest number of inliers is selected as the best fit.
STORM image I/O for STORM molecule lists generated with Insight3
- OpenMolListTxt for .txt molecule lists
- OpenMolList for .bin molecule lists
- WriteMolBinNXcYcZc for saving molecule list corresponding to model inliers
ransac_circle, ransac_line, ransac_ring, and ransac_plane can be run as MATLAB scripts with user-specified 2D or 3D (for ransac_plane only) point cloud coordinates. STORM molecule lists are loaded by default. User-specified coordinates should be x,y,z column vectors. Fixed model parameters, as well as number of ransac iterations, must be specified.
In this case, random sets of three points are sampled in each iteration and are used to define a circle. The number of inliers, calculated as the number of points within a given distance threshold to the circle's edge, is used as the fitting score.
RANSAC model fitting rapidly approaches a good solution when the number of random parameters is small (for the ring-fitting case, N=3):
