Unable to find SURF features in an RGB and IR image

11 visualizaciones (últimos 30 días)
Faseeh el 20 de Oct. de 2015
Respondida: elnaz akbari el 14 de Sept. de 2017
I have used SURF algorithm to compare images and find matching features. It works fine normally. But, recently I started working on IR images and its result wasn't satisfactory. Currently I have two cameras, one RGB and the other one is IR. They are arranged in stereo mode. As both of these cameras are at some distance apart so, naturally there would be some transformation between them. So, I applied Stereo camera calibration to first align the images on the same axis i.e. my rotation problem was solved. Now the only problem remains is of translation. Stereo camera calibration provides a translation vector but it is almost useless because whenever I change the distance of the object being viewed and the cameras, i have to manually change the translation vector. So, to solve this problem I used DetectSURFfeatures. Now it should work fine but as in my case I have a RGB and an IR camera, this algorithm is unable to find enough SURF features to allow the transformation. Is their any solution to the problem ? Plz help Thank You
My code is as under
Note: parameters.mat is used load the variable stereoparams
variable indexpairs is an empty matrix
load parameters.mat
[J1, J2] = rectifyStereoImages(I1, I2, stereoParams, 'OutputView', 'Full');
ptsOriginal = detectSURFFeatures(rgb2gray(J1));
ptsDistorted = detectSURFFeatures(rgb2gray(J2));
[featuresOriginal, validPtsOriginal] = extractFeatures(rgb2gray(J1), ptsOriginal);
[featuresDistorted, validPtsDistorted] = extractFeatures(rgb2gray(J2), ptsDistorted);
indexPairs = matchFeatures(featuresOriginal, featuresDistorted);
matchedOriginal = validPtsOriginal(indexPairs(:,1));
matchedDistorted = validPtsDistorted(indexPairs(:,2));
title('Putatively matched points (including outliers)');
[tform, inlierDistorted, inlierOriginal] = estimateGeometricTransform(...
matchedDistorted, matchedOriginal, 'similarity');
title('Matching points (inliers only)');
Tinv = tform.invert.T;
ss = Tinv(2,1);
sc = Tinv(1,1);
scaleRecovered = sqrt(ss*ss + sc*sc)
thetaRecovered = atan2(ss,sc)*180/pi
outputView = imref2d(size(rgb2gray(J1)));
recovered = imwarp(rgb2gray(J2),tform,'OutputView',outputView);
figure, imshowpair(rgb2gray(J1),recovered)

Respuesta aceptada

Dima Lisin
Dima Lisin el 20 de Oct. de 2015
Editada: Dima Lisin el 20 de Oct. de 2015
Hi Faseeh,
It would help if you could post a sample pair of images.
This is not really surprising, because an IR and and an RGB image of the same scene may well look very different. You can try tweaking the parameters of the matchFeatures function, such as increasing the 'MaxRatio', which would make it easier to find matches. You can also tweak the parameters of detectSURFFeatures. Try reducing 'MetricThreshold' to 500 to get more points, or try increasing 'NumOctaves' to capture larger-scale features.
You can also try using other feature detectors, such as detectHarrisFeatures or detectMSERFeatures.

Más respuestas (3)

Faseeh el 21 de Oct. de 2015
Thank You I will try the above options. I have attached some images taken from RGB camera

Faseeh el 21 de Oct. de 2015
I found another solution. Instead of using one checkerboard. I used a cluster of checkerboard images and the result was quite good. Now it detects enough features for applying transformation. Their is some disparity in after transformation. I am now working on that. If you have some tips regarding that please guide me. Thank You

elnaz akbari
elnaz akbari el 14 de Sept. de 2017
Hi , I have same problem, I have two pictures , RGB and IR that I want to match them in Matlab but it doesn't work correct. could you please guide me ? Thank you

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