RMSE for SIFT Algorithm

I work with the vl_sift algorithm.How can I calculate RMSE to match two images? second image is projective transform to the first image.

I1 = imread('filename');

T = projective2d([0.89 1.24 0.002;

-0.95 1 0.0025;

0 0 1]);

I2 = imwarp(I1(400:800,400:800),T);

Answer 1

[yanqi liu]

sir，please check the follow code to get some information，same as vl_feat

for more information,you can read the book 《计算机视觉与深度学习实战》，Thank you！

clc; clear all; close all;
I = imread('cameraman.tif');
I=imresize(I, [1e3 1e3], 'bilinear');
T = projective2d([0.89   1.24  0.002;
    -0.95  1     0.0025;
    0      0     1]);
J = imwarp(I(400:800,400:800),T);

% Reading the reference Image
C=J;
boxImage = C;
figure; imshow(boxImage);
title('Image of a Box');

%Reading the target image
II = I;
CC=II;
sceneImage = CC;

figure;
imshow(sceneImage);
title('Image of a Cluttered Scene');

%Detect feature images
boxPoints = detectSURFFeatures(boxImage);
scenePoints = detectSURFFeatures(sceneImage);
%Visualize the strongest feature points found in the reference image.
figure;
imshow(boxImage);
title('100 Strongest Feature Points from Box Image');
hold on;
plot(boxPoints.selectStrongest(100));

%Visualize the strongest feature points found in the target image.
figure;
imshow(sceneImage);
title('300 Strongest Feature Points from Scene Image');
hold on;
plot(scenePoints.selectStrongest(300));

%Extract feature descriptors at the interest points in both images.
[boxFeatures, boxPoints] = extractFeatures(boxImage, boxPoints);
[sceneFeatures, scenePoints] = extractFeatures(sceneImage, scenePoints);
%Match the features using their descriptors.
boxPairs = matchFeatures(boxFeatures, sceneFeatures);
%Display putatively matched features.
matchedBoxPoints = boxPoints(boxPairs(:, 1), :);
matchedScenePoints = scenePoints(boxPairs(:, 2), :);
figure;
title('Putatively Matched Points (Including Outliers)');
%This is line 46 where the compiler is indicating an error*
showMatchedFeatures(boxImage, sceneImage, matchedBoxPoints,...
    matchedScenePoints,'montage');

%estimateGeometricTransform calculates the transformation relating the matched points,
%while eliminating outliers. This transformation allows us to localize the object in the scene.
[tform, inlierBoxPoints, inlierScenePoints] = estimateGeometricTransform(matchedBoxPoints, ...
    matchedScenePoints, 'affine');
%Display the matching point pairs with the outliers removed
figure;
showMatchedFeatures(boxImage, sceneImage, inlierBoxPoints,...
    inlierScenePoints, 'montage');
title('Matched Points (Inliers Only)');

%Get the bounding polygon of the reference image.
boxPolygon = [1, 1;...                   % top-left
    size(boxImage, 2), 1;...                 % top-right
    size(boxImage, 2), size(boxImage, 1);... % bottom-right
    1, size(boxImage, 1);...                 % bottom-left
    1, 1];                   % top-left again to close the polygon
% Transform the polygon into the coordinate system of the target image. The transformed polygon indicates the location of the object in the scene.
newBoxPolygon = transformPointsForward(tform, boxPolygon);
%Display the detected object.
figure();
imshow(sceneImage);
hold on;
line(newBoxPolygon(:, 1), newBoxPolygon(:, 2), 'Color', 'y');
title('Detected Box');

[Mary]

Thank you so much dear friend. But I needed the RMSE error. Regards