Fit model to noisy data
a model to noisy data using the M-estimator sample consensus (MSAC)
algorithm, a version of the random sample consensus (RANSAC) algorithm.
Specify your function for fitting a model,
and your function for calculating distances from the model to your
takes random samples from your
uses the fit function to maximize the number of inliers within
[___] = ransac(___,
additionally specifies one or more
Load and plot a set of noisy 2-D points.
load pointsForLineFitting.mat plot(points(:,1),points(:,2),'o'); hold on
Fit a line using linear least squares. Due to outliers, the line is not a good fit.
modelLeastSquares = polyfit(points(:,1),points(:,2),1); x = [min(points(:,1)) max(points(:,1))]; y = modelLeastSquares(1)*x + modelLeastSquares(2); plot(x,y,'r-')
Fit a line to the points using the MSAC algorithm. Define the sample size, the maximum distance for inliers, the fit function, and the distance evaluation function. Call
ransac to run the MSAC algorithm.
sampleSize = 2; % number of points to sample per trial maxDistance = 2; % max allowable distance for inliers fitLineFcn = @(points) polyfit(points(:,1),points(:,2),1); % fit function using polyfit evalLineFcn = ... % distance evaluation function @(model, points) sum((points(:, 2) - polyval(model, points(:,1))).^2,2); [modelRANSAC, inlierIdx] = ransac(points,fitLineFcn,evalLineFcn, ... sampleSize,maxDistance);
Refit a line to the inliers using
modelInliers = polyfit(points(inlierIdx,1),points(inlierIdx,2),1);
Display the final fit line. This line is robust to the outliers that
ransac identified and ignored.
inlierPts = points(inlierIdx,:); x = [min(inlierPts(:,1)) max(inlierPts(:,1))]; y = modelInliers(1)*x + modelInliers(2); plot(x, y, 'g-') legend('Noisy points','Least squares fit','Robust fit'); hold off
data— Data to be modeled
Data to be modeled, specified as an m-by-n matrix. Each row corresponds to a data point in the set to be modeled. For example, to model a set of 2-D points, specify the point data as an m-by-2 matrix.
fitFcn— Function to fit a subset of
Function to fit a subset of
as a function handle. The function must be of the form:
model = fitFcn(data)
If it is possible to fit multiple models to the data, then
the model parameters as a cell array.
distFcn— Function to compute distances from model
Function to compute distances from the model to the data, specified as a function handle. The function must be of the form:
distances = distFcn(model,data)
model is an n-element
array, then distances must be an m-by-n matrix.
distances must be an m-by-1
maxDistance— Maximum distance for inlier points
Maximum distance from the fit curve to an inlier point, specified as a positive scalar. Any points further away than this distance are considered outliers. The RANSAC algorithm creates a fit from a small sample of points, but tries to maximize the number of inlier points. Lowering the maximum distance improves the fit by putting a tighter tolerance on inlier points.
comma-separated pairs of
the argument name and
Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
'ValidateModelFcn'— Function to validate model
Function to validate model, specified as the comma-separated
pair consisting of '
ValidateModelFcn' and a function
handle. The function returns
true if the model
is accepted based on criteria defined in the function. Use this function
to reject specific fits. The function must be of the form:
isValid = validateModelFcn(model,varargin)
If no function is specified, all models are assumed to be valid.
'MaxSamplingAttempts'— Maximum number of sampling attempts
Maximum number of attempts to find a sample that yields a valid model, specified as the
comma-separated pair consisting of
MaxSamplingAttempts' and an integer.
'MaxNumTrials'— Maximum number of random trials
1000(default) | integer
Maximum number of random trials, specified as the comma-separated pair consisting of
MaxNumTrials' and an integer. A single trial
uses a minimum number of random points from
fit a model. Then, the trial checks the number of inliers within the
maxDistance from the model. After all trials,
the model with the highest number of inliers is selected. Increasing the
number of trials improves the robustness of the output at the expense of
'Confidence'— Confidence of final solution
99(default) | scalar from 0 to 100
Confidence that the final solution finds the maximum number of inliers for the model fit,
specified as the comma-separated pair consisting of
Confidence' and a scalar from 0 to 100.
Increasing this value improves the robustness of the output at the
expense of additional computation.
model— Best fit model
Best fit model, returned as the parameters defined in the
This model maximizes the number of inliers from all the sample attempts.
 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.