estimateGeometricTransform2D

Estimate 2-D geometric transformation from matching point pairs

collapse all in page

Syntax

tform = estimateGeometricTransform2D(matchedPoints1,matchedPoints2,transformType)
[tform,inlierIndex] = estimateGeometricTransform2D(___)
[tform,inlierIndex,status] = estimateGeometricTransform2D(___)
[___] = estimateGeometricTransform2D(___, Name,Value)

Description

example

tform = estimateGeometricTransform2D(matchedPoints1,matchedPoints2,transformType) estimates a 2-D geometric transformation between two images by mapping the inliers in the matched points from one image matchedPoints1 to the inliers in the matched points from another image matchedPoints2.

[tform,inlierIndex] = estimateGeometricTransform2D(___) additionally returns a vector specifying each matched point pair as either an inlier or an outlier using the input arguments from the previous syntax.

[tform,inlierIndex,status] = estimateGeometricTransform2D(___) additionally returns a status code indicating whether or not the function could estimate a transformation and, if not, why it failed. If you do not specify the status output, the function instead returns an error for conditions that cannot produce results.

[___] = estimateGeometricTransform2D(___, Name,Value) specifies additional options using one or more name-value pair arguments in addition to any combination of arguments from previous syntaxes. For example, 'Confidence',99 sets the confidence value for finding the maximum number of inliers to 99.

Examples

collapse all

Open Live Script

Read an image and display it.

original = imread('cameraman.tif');
imshow(original)
title('Base Image')

Distort and display the transformed image.

distorted = imresize(original,0.7); 
distorted = imrotate(distorted,31);
figure
imshow(distorted)
title('Transformed Image')

Detect and extract features from the original and the transformed images.

ptsOriginal  = detectSURFFeatures(original);
ptsDistorted = detectSURFFeatures(distorted);
[featuresOriginal,validPtsOriginal] = extractFeatures(original,ptsOriginal);
[featuresDistorted,validPtsDistorted] = extractFeatures(distorted,ptsDistorted);

Match and display features between the images.

index_pairs = matchFeatures(featuresOriginal,featuresDistorted);
matchedPtsOriginal  = validPtsOriginal(index_pairs(:,1));
matchedPtsDistorted = validPtsDistorted(index_pairs(:,2));
figure 
showMatchedFeatures(original,distorted,matchedPtsOriginal,matchedPtsDistorted)
title('Matched SURF Points With Outliers');

Exclude the outliers, estimate the transformation matrix, and display the results.

[tform,inlierIdx] = estimateGeometricTransform2D(matchedPtsDistorted,matchedPtsOriginal,'similarity');
inlierPtsDistorted = matchedPtsDistorted(inlierIdx,:);
inlierPtsOriginal  = matchedPtsOriginal(inlierIdx,:);

figure 
showMatchedFeatures(original,distorted,inlierPtsOriginal,inlierPtsDistorted)
title('Matched Inlier Points')

Use the estimated transformation to recover and display the original image from the distorted image.

outputView = imref2d(size(original));
Ir = imwarp(distorted,tform,'OutputView',outputView);
figure 
imshow(Ir); 
title('Recovered Image');

Input Arguments

collapse all

Matched points from the first image, specified as either a KAZEPoints object, cornerPoints object, SURFPoints object, MSERRegions object, ORBPoints object, BRISKPoints object, or an M-by-2 matrix in which each row is a pair of [x,y] coordinates and M is the number of matched points.

Matched points from the second image, specified as either a KAZEPoints object, cornerPoints object, SURFPoints object, MSERRegions object, ORBPoints object, BRISKPoints object, or an M-by-2 matrix in which each row is a pair of [x,y] coordinates and M is the number of matched points.

Transformation type, specified as a character string. You can set the transform type to 'rigid', 'similarity', 'affine', or 'projective'. Each transform type requires a minimum number of matched pairs of points to estimate a transformation. You can generally improve the accuracy of a transformation by using a larger number of matched pairs of points. This table shows the type of object associated with each transformation type and the minimum number of matched pairs of points the transformation requires.

Transformation Typetform ObjectMinimum Number of Matched Pairs of Points
'rigid'rigid2d2
'similarity'affine2d2
'affine'affine2d3
'projective'projective2d4

Data Types: string

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is 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 Name1,Value1,...,NameN,ValueN.

Example: 'Confidence',99 sets the confidence value for finding the maximum number of inliers to 99.

Maximum number of random trials, specified as the comma-separated pair consisting of 'MaxNumTrials' and a positive integer. This value specifies the number of randomized attempts the function makes to find matching point pairs. Specifying a higher value causes the function to perform additional computations, which increases the likelihood of finding inliers.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Confidence of finding the maximum number of inliers, specified as the comma-separated pair consisting of 'Confidence' and a positive numeric scalar in the range (0, 100). Increasing this value causes the function to perform additional computations, which increases the likelihood of finding a greater number of inliers.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Maximum distance from a point to the projection of the corresponding point, specified as the comma-separated pair consisting of 'MaxDistance' and a positive numeric scalar. 'MaxDistance' specifies the maximum distance, in pixels, that a point can differ from the projected location of its corresponding point to be considered an inlier. The corresponding projection is based on the estimated transform.

The function checks for a transformation from matchedPoints1 to matchedPoints2, and then calculates the distance between the matched points in each pair after applying the transformation. If the distance between the matched points in a pair is greater than the 'MaxDistance' value, then the pair is considered an outlier for that transformation. If the distance is less than 'MaxDistance', then the pair is considered an inlier.

A matched point show in image 1 and image 2. Image one shows the point from image 2 projected back onto image 1.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Output Arguments

collapse all

Geometric transformation, returned as a rigid2d, affine2d, or projective2d object.

The returned geometric transformation matrix maps the inliers in matchedPoints1 to the inliers in matchedPoints2. The function returns an object specific to the transformation type specified by the transformType input argument.

transformTypetform
'rigid'rigid2d
'similarity'affine2d
'affine'affine2d
'projective'projective2d

Status code, returned as 0, 1, or 2. The status code indicates whether or not the function could estimate the transformation and, if not, why it failed.

ValueDescription
0No error
1matchedPoints1 and matchedPoints2 inputs do not contain enough points
2Not enough inliers found

If you do not specify the status code output, the function returns an error if it cannot produce results.

Data Types: int32

Index of inliers, returned as an M-by-1 logical vector, where M is the number of point pairs. Each element contains either a logical 1 (true), indicating that the corresponding point pair is an inlier, or a logical 0 (false), indicating that the corresponding point pair is an outlier.

Algorithms

The function excludes outliers using the M-estimator sample consensus (MSAC) algorithm. The MSAC algorithm is a variant of the random sample consensus (RANSAC) algorithm. Results may not be identical between runs due to the randomized nature of the MSAC algorithm.

References

[1] Hartley, Richard, and Andrew Zisserman. Multiple View Geometry in Computer Vision. 2nd ed. Cambridge, UK ; New York: Cambridge University Press, 2003.

[2] Torr, P.H.S., and A. Zisserman. “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry.” Computer Vision and Image Understanding 78, no. 1 (April 2000): 138–56. https://doi.org/10.1006/cviu.1999.0832.

Extended Capabilities

See Also

Functions

Objects

Topics

Introduced in R2020b

Computer Vision Toolbox Documentation

Support