Main Content

Fit model to noisy data

`[`

fits a model to noisy data using the M-estimator sample consensus (MSAC) algorithm,
a version of the random sample consensus (RANSAC) algorithm.`model`

,`inlierIdx`

]
= ransac(`data`

,`fitFcn`

,`distFcn`

,`sampleSize`

,`maxDistance`

)

Specify your function for fitting a model, `fitFcn`

, and your
function for calculating distances from the model to your data,
`distFcn`

. The `ransac`

function takes
random samples from your `data`

using
`sampleSize`

and uses the fit function to maximize the number
of inliers within `maxDistance`

.

`[___] = ransac(___,`

additionally specifies one or more `Name,Value`

)`Name,Value`

pair
arguments.

[1] Torr, P. H. S., and A. Zisserman. "MLESAC: A New Robust Estimator with
Application to Estimating Image Geometry." *Computer Vision and Image
Understanding*. Vol. 18, Issue 1, April 2000, pp. 138–156.