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estimateUncalibratedRectification

Uncalibrated stereo rectification

Syntax

[T1,T2] = estimateUncalibratedRectification(F,inlierPoints1,inlierPoints2,imagesize)

Description

example

[T1,T2] = estimateUncalibratedRectification(F,inlierPoints1,inlierPoints2,imagesize) returns projective transformations for rectifying stereo images. This function does not require either intrinsic or extrinsic camera parameters. The input points can be M-by-2 matrices of M number of [x y] coordinates, or SURFPoints, MSERRegions, or cornerPoints object. F is a 3-by-3 fundamental matrix for the stereo images.

Examples

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This example shows how to compute the fundamental matrix from corresponding points in a pair of stereo images.

Load the stereo images and feature points which are already matched.

I1 = imread('yellowstone_left.png');
I2 = imread('yellowstone_right.png');
load yellowstone_inlier_points;

Display point correspondences. Notice that the matching points are in different rows, indicating that the stereo pair is not rectified.

showMatchedFeatures(I1, I2,inlier_points1,inlier_points2,'montage');
title('Original images and matching feature points');

Compute the fundamental matrix from the corresponding points.

f = estimateFundamentalMatrix(inlier_points1,inlier_points2,...
    'Method','Norm8Point');

Compute the rectification transformations.

[t1, t2] = estimateUncalibratedRectification(f,inlier_points1,...
    inlier_points2,size(I2));

Rectify the stereo images using projective transformations t1 and t2.

[I1Rect,I2Rect] = rectifyStereoImages(I1,I2,t1,t2);

Display the stereo anaglyph, which can also be viewed with 3-D glasses.

figure;
imshow(stereoAnaglyph(I1Rect,I2Rect));

Input Arguments

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Fundamental matrix for the stereo images, specified as a 3-by-3 fundamental matrix. The fundamental matrix satisfies the following criteria:

If P1, a point in image 1, corresponds to P2, a point in image 2, then:
[P2,1] *F * [P1,1]' = 0

F must be double or single.

Coordinates of corresponding points in image one, specified as an M-by-2 matrix of M number of [x y] coordinates, or as a SURFPoints, MSERRegions, or cornerPoints object.

Coordinates of corresponding points in image one, specified as an M-by-2 matrix of M number of [x y] coordinates, or as a SURFPoints, MSERRegions, or cornerPoints object.

Second input image size, specified as a double, single, or integer value and in the format returned by the size function. The size of input image 2 corresponds to inlierPoints2.

Output Arguments

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Projective transformation, returned as a 3-by-3 matrix describing the projective transformations for input image T1.

Projective transformation, returned as a 3-by-3 matrix describing the projective transformations for input image T2.

Tips

  • An epipole may be located in the first image or the second image. Applying the output uncalibrated rectification of T1 (or T2) to image 1 (or image 2) may result in an undesired distortion. You can check for an epipole within an image by applying the isEpipoleInImage function.

References

[1] Hartley, R. and A. Zisserman, "Multiple View Geometry in Computer Vision," Cambridge University Press, 2003.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Introduced in R2012b