What's the best way to use findHomography

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Sure I've tested CV_LMEDS and as expected, the result is worst (too much outliers). So the way I use it is good ?

How are you detecting those keypoints ?

These keypoints are stars in astronomical images. So I use my own algorithm. It is working well as I can circle all detected stars.

You'll have to tune RANSAC using confidence level parameter. (findHomography python version has ransacReprojThreshold parameter ) or pre-filter data before using RANSAC.
Also , if ordering of points is random you'll get crazy results.

"EDIT: I forgot to say that line x from the file 1 is not necessary the same point that in line x in the file 2. Maybe this is the problem ? (But I thought that RANSAC was good to get through this). But how I can match them ?" I have to check but I think you are right : srcPoints(i) is matched with dstPoints(i) ->it is the best match. RANSAC won't change matching couple. Why do you need findHomography? Haven't you got image pose ?

C++ version has also double ransacReprojThreshold as parameter. By default this is 3. I've already tested with 1 or 0.1 with no sucess. So your last sentence means that ordering points is necessary ? So for now I do not know how I can do and my question is: if I order the 10 best points for example, what is the use of RANSAC algorithm ??? There will be no outliers.

outlier means outliers couple. A couple is constructed using a matching procedure. With star I thing you can use color (or grayscale level). Can you post your image? Haven't you got image pose (theta and phi)?

@LBerger In fact I search stars in reference image, then I search stars in the second image. I have two lists. I sort these lists by brightness of stars.

But, of course, the order in the two lists can change a bit. Then a stars can be detected in an image while not in the other, etc .... So I want to compare the n reference stars to the other and compute the homography matrix in order to apply the transformation matrix to the second image. At the end, you can stack both.

I think you should write your own matching procedure. Takes 20 most bright stars in firrst images. try to match 3 or four stars randomly in second images ( 20 most bright stars too) then check if line between couple intersect or not and if distance between couple is similar. If no intersection try to match more stars using all list and all line must be similar (not exactly of course)

I think this method will fail if there is rotation. have you got rotation?

Yes I do have rotation, and sometimes distorsions. So I guess the solution is to use triangle (at least) similarity algorithm before giving list to findHomography.