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

Hi,

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 image

this is the captured image

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?

[KALYAN ACHARJYA]

(1024*768) to captured image (2048*2048) by a camera?

Is the both images having different dimensions? right?

[Shuai Nie]

yes

Answer 1

[Selva Karna]

Shuai Nie

For matching:

Tech 1:

*Extract Harise Features

*SIFT Features

Tech 2:

*Template matching

[Shuai Nie]

Could you provide more details?

[Selva Karna]

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]

Method 3:

boxImage = imread('stapleRemover.jpg');

figure;

imshow(boxImage);

title('Image of a Box');

sceneImage = imread('clutteredDesk.jpg');

figure;

imshow(sceneImage);

title('Image of a Cluttered Scene');

boxPoints = detectSURFFeatures(boxImage);

scenePoints = detectSURFFeatures(sceneImage);

figure;

imshow(boxImage);

title('100 Strongest Feature Points from Box Image');

hold on;

plot(selectStrongest(boxPoints, 100));

figure;

imshow(sceneImage);

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), :);

figure;

showMatchedFeatures(boxImage, sceneImage, matchedBoxPoints, ...

matchedScenePoints, 'montage');

title('Putatively Matched Points (Including Outliers)');

[tform, inlierBoxPoints, inlierScenePoints] = ...

estimateGeometricTransform(matchedBoxPoints, matchedScenePoints, 'affine');

figure;

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);

figure;

imshow(sceneImage);

hold on;

line(newBoxPolygon(:, 1), newBoxPolygon(:, 2), 'Color', 'y');

title('Detected Box');