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How can I do match a illumination image (1024*768) to captured image (2048*2048) by a camera?

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Shuai Nie
Shuai Nie on 9 Jan 2020
Commented: Selva Karna on 9 Jan 2020
I want to match the pixels of illuminaiton image with the captured image by a camera.
The issue is two images have different resolutions, also captured images have a distortion. Camera only capture the middle part of the illumination.
this is the illumination imageillumination.png
this is the captured image
camera (1).png
as an example, I would like to know the pixel (10,10) in illumination image corresponding to which pixels in captured image!
As I am new in matlab, can someone give me some tips?

Answers (1)

Selva Karna
Selva Karna on 9 Jan 2020
For matching:
Tech 1:
*Extract Harise Features
*SIFT Features
Tech 2:
*Template matching


Selva Karna
Selva Karna on 9 Jan 2020
Method 1:
I1 = rgb2gray(imread('viprectification_deskLeft.png'));
I2 = rgb2gray(imread('viprectification_deskRight.png'));
points1 = detectHarrisFeatures(I1);
points2 = detectHarrisFeatures(I2);
[features1,valid_points1] = extractFeatures(I1,points1);
[features2,valid_points2] = extractFeatures(I2,points2);
indexPairs = matchFeatures(features1,features2);
Retrieve the locations of the corresponding points for each image.
matchedPoints1 = valid_points1(indexPairs(:,1),:);
matchedPoints2 = valid_points2(indexPairs(:,2),:);
figure; showMatchedFeatures(I1,I2,matchedPoints1,matchedPoints2);
Method 2:
I1 = imread('cameraman.tif');
I2 = imresize(imrotate(I1,-20),1.2);
points1 = detectSURFFeatures(I1);
points2 = detectSURFFeatures(I2);
[f1,vpts1] = extractFeatures(I1,points1);
[f2,vpts2] = extractFeatures(I2,points2);
indexPairs = matchFeatures(f1,f2) ;
matchedPoints1 = vpts1(indexPairs(:,1));
matchedPoints2 = vpts2(indexPairs(:,2));
figure; showMatchedFeatures(I1,I2,matchedPoints1,matchedPoints2);
legend('matched points 1','matched points 2');
Selva Karna
Selva Karna on 9 Jan 2020
Method 3:
boxImage = imread('stapleRemover.jpg');
title('Image of a Box');
sceneImage = imread('clutteredDesk.jpg');
title('Image of a Cluttered Scene');
boxPoints = detectSURFFeatures(boxImage);
scenePoints = detectSURFFeatures(sceneImage);
title('100 Strongest Feature Points from Box Image');
hold on;
plot(selectStrongest(boxPoints, 100));
title('300 Strongest Feature Points from Scene Image');
hold on;
plot(selectStrongest(scenePoints, 300));
[boxFeatures, boxPoints] = extractFeatures(boxImage, boxPoints);
[sceneFeatures, scenePoints] = extractFeatures(sceneImage, scenePoints);
boxPairs = matchFeatures(boxFeatures, sceneFeatures);
matchedBoxPoints = boxPoints(boxPairs(:, 1), :);
matchedScenePoints = scenePoints(boxPairs(:, 2), :);
showMatchedFeatures(boxImage, sceneImage, matchedBoxPoints, ...
matchedScenePoints, 'montage');
title('Putatively Matched Points (Including Outliers)');
[tform, inlierBoxPoints, inlierScenePoints] = ...
estimateGeometricTransform(matchedBoxPoints, matchedScenePoints, 'affine');
showMatchedFeatures(boxImage, sceneImage, inlierBoxPoints, ...
inlierScenePoints, 'montage');
title('Matched Points (Inliers Only)');
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
newBoxPolygon = transformPointsForward(tform, boxPolygon);
hold on;
line(newBoxPolygon(:, 1), newBoxPolygon(:, 2), 'Color', 'y');
title('Detected Box');

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