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estimateEssentialMatrix

Estimate essential matrix from corresponding points in a pair of images

Syntax

E = estimateEssentialMatrix(matchedPoints1,matchedPoints2,cameraParams)
E = estimateEssentialMatrix(matchedPoints1,matchedPoints2,cameraParams1,cameraParams2)
[E,inliersIndex] = estimateEssentialMatrix(matchedPoints1,matchedPoints2)
[E,inliersIndex,status] = estimateEssentialMatrix(matchedPoints1,matchedPoints2)
[E,inliersIndex,status] = estimateEssentialMatrix(___,Name,Value)

Description

example

E = estimateEssentialMatrix(matchedPoints1,matchedPoints2,cameraParams) returns the 3-by-3 essential matrix, E, using the M-estimator sample consensus (MSAC) algorithm. The input points can be M-by-2 matrices of M number of [x,y] coordinates, or a SURFPoints, MSERRegions, BRISKPoints, or cornerPoints object. The cameraParams object contains the parameters of the camera used to take the images.

E = estimateEssentialMatrix(matchedPoints1,matchedPoints2,cameraParams1,cameraParams2) returns the essential matrix relating two images taken by different cameras. cameraParams1 and cameraParams2 are cameraParameters objects containing the parameters of camera 1 and camera 2 respectively.

[E,inliersIndex] = estimateEssentialMatrix(matchedPoints1,matchedPoints2) additionally returns an M-by-1 logical vector, inliersIndex, used to compute the essential matrix. The function sets the elements of the vector to true when the corresponding point was used to compute the fundamental matrix. The elements are set to false if they are not used.

[E,inliersIndex,status] = estimateEssentialMatrix(matchedPoints1,matchedPoints2) additionally returns a status code to indicate the validity of points.

[E,inliersIndex,status] = estimateEssentialMatrix(___,Name,Value) uses additional options specified by one or more Name,Value pair arguments.

Examples

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Load precomputed camera parameters.

load upToScaleReconstructionCameraParameters.mat

Read and undistort two images.

imageDir = fullfile(toolboxdir('vision'),'visiondata',...
    'upToScaleReconstructionImages');
images = imageDatastore(imageDir);
I1 = undistortImage(readimage(images,1),cameraParams);
I2 = undistortImage(readimage(images,2),cameraParams);
I1gray = rgb2gray(I1);
I2gray = rgb2gray(I2);

Detect feature points each image.

imagePoints1 = detectSURFFeatures(I1gray);
imagePoints2 = detectSURFFeatures(I2gray);

Extract feature descriptors from each image.

features1 = extractFeatures(I1gray,imagePoints1,'Upright',true);
features2 = extractFeatures(I2gray,imagePoints2,'Upright',true);

Match features across the images.

indexPairs = matchFeatures(features1,features2);
matchedPoints1 = imagePoints1(indexPairs(:,1));
matchedPoints2 = imagePoints2(indexPairs(:,2));
figure
showMatchedFeatures(I1,I2,matchedPoints1,matchedPoints2);
title('Putative Matches')

Estimate the essential matrix.

[E,inliers] = estimateEssentialMatrix(matchedPoints1,matchedPoints2,...
 cameraParams);

Display the inlier matches.

inlierPoints1 = matchedPoints1(inliers);
inlierPoints2 = matchedPoints2(inliers);
figure
showMatchedFeatures(I1,I2,inlierPoints1,inlierPoints2);
title('Inlier Matches')

Input Arguments

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Coordinates of corresponding points in image 1, specified as an M-by-2 matrix of M of [x,y] coordinates, or as a SURFPoints, BRISKPoints, MSERRegions, or cornerPoints object. The matchedPoints1 input must contain at least five points, which are putatively matched by using a function such as matchFeatures.

Coordinates of corresponding points in image 1, specified as an M-by-2 matrix of M of [x,y] coordinates, or as a SURFPoints, MSERRegions, BRISKPoints,or cornerPoints object. The matchedPoints2 input must contain at least five points, which are putatively matched by using a function such as matchFeatures.

Camera parameters, specified as a cameraParameters or cameraIntrinsics object. You can return the cameraParameters object using the estimateCameraParameters function. The cameraParameters object contains the intrinsic, extrinsic, and lens distortion parameters of a camera.

Camera parameters for camera 1, specified as a cameraParameters or cameraIntrinsics object. You can return the cameraParameters object using the estimateCameraParameters function. The cameraParameters object contains the intrinsic, extrinsic, and lens distortion parameters of a camera.

Camera parameters for camera 2, specified as a cameraParameters or cameraIntrinsics object. You can return the cameraParameters object using the estimateCameraParameters function. The cameraParameters object contains the intrinsic, extrinsic, and lens distortion parameters of a camera.

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 single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'MaxNumTrials', 500

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Maximum number of random trials for finding outliers, specified as the comma-separated pair consisting of 'MaxNumTrials' and a positive integer. The actual number of trials depends on matchedPoints1, matchedPoints2, and the value of the Confidence parameter. To optimize speed and accuracy, select the number of random trials.

Desired confidence for finding the maximum number of inliers, specified as the comma-separated pair consisting of 'Confidence' and a percentage scalar value in the range (0,100). Increasing this value improves the robustness of the output but increases the amount of computations.

Sampson distance threshold, specified as the comma-separated pair consisting of 'MaxDistance' and a scalar value. The function uses the threshold to find outliers returned in pixels squared. The Sampson distance is a first-order approximation of the squared geometric distance between a point and the epipolar line. Increase this value to make the algorithm converge faster, but this can also adversely affect the accuracy of the result.

Output Arguments

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Essential matrix, returned as a 3-by-3 matrix that is computed from the points in the matchedPoints1 and matchedPoints2 inputs.

[P21]*EssentialMatrix*[P11]'=0

The P1 point in image 1, in normalized image coordinates, corresponds to the , P2 point in image 2.

In computer vision, the essential matrix is a 3-by-3 matrix which relates corresponding points in stereo images which are in normalized image coordinates. When two cameras view a 3-D scene from two distinct positions, the geometric relations between the 3-D points and their projections onto the 2-D images lead to constraints between image points. The two images of the same scene are related by epipolar geometry.

Data Types: double

Inliers index, returned as an M-by-1 logical index vector. An element set to true indicates that the corresponding indexed matched points in matchedPoints1 and matchedPoints2 were used to compute the essential matrix. An element set to false means the indexed points were not used for the computation.

Data Types: logical

Status code, returned as one of the following possible values:

statusValue
0:No error.
1:matchedPoints1 and matchedPoints2 do not contain enough points. At least five points are required.
2:Not enough inliers found. A least five inliers are required.

Data Types: int32

Tips

Use estimateEssentialMatrix when you know the camera intrinsics. You can obtain the intrinsics using the Camera Calibrator app. Otherwise, you can use the estimateFundamentalMatrix function that does not require camera intrinsics. The fundamental matrix cannot be estimated from coplanar world points.

References

[1] Kukelova, Z., M. Bujnak, and T. Pajdla Polynomial Eigenvalue Solutions to the 5-pt and 6-pt Relative Pose Problems. Leeds, UK: BMVC, 2008.

[2] Nister, D.. “An Efficient Solution to the Five-Point Relative Pose Problem.” IEEE Transactions on Pattern Analysis and Machine Intelligence.Volume 26, Issue 6, June 2004.

[3] Torr, P. H. S., and A. Zisserman. “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry.” Computer Vision and Image Understanding. Volume 78, Issue 1, April 2000, pp. 138-156.

Extended Capabilities

Introduced in R2016b