Machupicchu's profile - activity

I guess I am still not formulating it well enough. I can assure you it is used in academics domain (university) to to such kind of evaluation (detection), therefore I am sure this approach is valid. The idea is that we use the geometrical info in the keypoints to try realingning them to a supposedly matching image. If alignment is good->match if no good-> no match maybe you see better what i mean now ?

I didn't explain clearly maybe: the distinction is intended to be made by the percentage of error with the homography , i.e. if too much error (threshold) then no match -> not able to realign properly, otherwsise match

Hi,

I ve wirtten some code to compute SIFT decriptors from images and then compute the homography matrix from the 'good matches'. My goal is to distinguish between 2 classes (different and same) images.

[H, mask ]= findHomography(...)

However the values returned in mask are almost the same (number of inliers between 4-8) for both 'same' and 'different'.

My procedure was to check the number of inliers in mask ... but is that the correct way? Or should I apply the Homography to my image and rerun RANSAC and retest something like the so-called 'back-projection error'? If yes can you provide some hints about the threshold to use?

Best regards. O.