Hello,
I'm currently working on a project where I have to correlate several similar images taken from a DSLR camera. Those images may vary on factors such as focal distance, orientation, level of exposure and noise.
My approach consists in using SIFT or SURF for keypoint detection, followed by the FLANN library for keypoint matching. However, the images may have resolutions that go as far as 18 megapixels, which makes the process of keypoint detection too slow. Both SIFT and SURF take more than 4 seconds to detect keypoints in higher resolutions. I've tried to relax the parameters of both algorithms but still the processing time is too high, which may ruin the user experience.
I've read somewhere that a possible approach would be to divide each image into a certain number of sub-images and then try to correlate respective sub-regions between different images. However, as the focal distance and orientation may vary I find this approach ineffective in the context of my project.
Anyone have a suggestion on a possible approach to make the correlation process of high resolution images more efficient?