%FINDHOMOGRAPHY  Calculates a fundamental matrix from the corresponding points in two images
%
%    F = cv.findHomography(srcPoints, dstPoints)
%    [F, mask] = cv.findHomography(...)
%    [...] = cv.findHomography(..., 'OptionName', optionValue, ...)
%
% ## Input
% * __srcPoints__ Coordinates of the points in the original plane, a numeric
%        array of size 1xNx2 or cell array of two-element vectors.
% * __dstPoints__ Coordinates of the points in the target plane, a numeric
%        array of size 1xNx2 or cell array of two-element vectors.
%
% ## Output
% * __H__ Homography matrix.
% * __mask__ Mask array.
%
% ## Options
% * __Method__ Method used to computed a homography matrix. The following
%        methods are possible:
% * __0__ a regular method using all the points. (default)
% * __Ransac__ RANSAC-based robust method.
% * __LMedS__ Least-Median robust method.
% * __RansacReprojThreshold__ Maximum allowed reprojection error to treat a
%        point pair as an inlier (used in the RANSAC method only). That is,
%        if `|| dstPoints_i - convertPointsToHomogeneous(H*srcPoints_i) ||`
%        `> RansacReprojThreshold` then the point i is considered an outlier.
%        If srcPoints and dstPoints are measured in pixels, it usually makes
%        sense to set this parameter somewhere in the range of 1 to 10.
%        default 3.0.
%
% The functions find and return the perspective transformation  between the
% source and the destination planes:
%
%    s_i * [x_i'; y_i; 1] ~ H * [X_i; Y_i; 1]
%
% so that the back-projection error is minimized. If the parameter method
% is set to the default value 0, the function uses all the point pairs to
% compute an initial homography estimate with a simple least-squares
% scheme.
%
% However, if not all of the point pairs `(srcPoints_i, dstPoints_i)` fit the
% rigid perspective transformation (that is, there are some outliers), this
% initial estimate will be poor. In this case, you can use one of the two
% robust methods. Both methods, RANSAC and LMeDS , try many different
% random subsets of the corresponding point pairs (of four pairs each),
% estimate the homography matrix using this subset and a simple
% least-square algorithm, and then compute the quality/goodness of the
% computed homography (which is the number of inliers for RANSAC or the
% median re-projection error for LMeDs). The best subset is then used to
% produce the initial estimate of the homography matrix and the mask of
% inliers/outliers.
%
% Regardless of the method, robust or not, the computed homography matrix
% is refined further (using inliers only in case of a robust method) with
% the Levenberg-Marquardt method to reduce the re-projection error even
% more.
%
% The method RANSAC can handle practically any ratio of outliers but it
% needs a threshold to distinguish inliers from outliers. The method LMeDS
% does not need any threshold but it works correctly only when there are
% more than 50% of inliers. Finally, if there are no outliers and the noise
% is rather small, use the default method (method=0).
%
% The function is used to find initial intrinsic and extrinsic matrices.
% Homography matrix is determined up to a scale. Thus, it is normalized so
% that h33 = 1.
%
% See also cv.getAffineTransform cv.getPerspectiveTransform
% cv.estimateRigidTransform cv.warpPerspective cv.perspectiveTransform
%