Refines coordinates of corresponding points

[newPoints1, newPoints2] = cv.correctMatches(F, points1, points2)

Input

• F 3x3 fundamental matrix.
• points1 first set of 2D points. A numeric Nx2/Nx1x2/1xNx2 array or a cell array of 2-element vectors {[x,y], ...} (floating-point precision).
• points2 second set of 2D points. Same size and type as points1.

Output

• newPoints1 The optimized points1. Similar in shape to points1 (either Nx2/1xNx2 numeric array or cell array of 2D points).
• newPoints2 The optimized points2.

The function implements the Optimal Triangulation Method (see [Hartley2004] for details). For each given point correspondence points1[i] <-> points2[i], and a fundamental matrix F, it computes the corrected correspondences newPoints1[i] <-> newPoints2[i] that minimize the geometric error:

d(points1[i], newPoints1[i])^2 + d(points2[i], newPoints2[i])^2

(where d(a,b) is the geometric distance between points a and b) subject to the epipolar constraint

newPoints2' * F * newPoints1 = 0

References

[Hartley2004]:

R.I. Hartley and A. Zisserman. "Multiple View Geometry in Computer Vision" Cambridge University Press, 2004.