SIFT feature detection with heavy vignette images

1
votes

I am trying to match features between pairs of images taken with an endoscopic camera. I see very poor performance in the number of features that match when the image is translated (even though the overlap is still quite high).

A couple of questions

  • Might this low number of features matching come from vignetting that is present in the images? (SIFT descriptors describe gradients and if there is a constant vignette gradient, does this corrupt the descriptors?)
  • Could the camera calibration be poor?
  • Do you have any additional suggestions for improving the matching?

Here's what I am doing: - Images are remapped based on camera calibration done with a checkerboard pattern - Features are detected with SIFT (VLFeat) - Features are matched with a geometric verification step (RANSAC with fairly high threshold)

Here are two examples: (red = features found by not matched; green = features that matched after geometric verification) Small translation = reasonable matching enter image description here

Large translation = poor matching enter image description here

1
computer-visionfeature-detectionsiftvlfeat
When there is a just a translation you can try to work with optical flow. Here are some ideas: stackoverflow.com/questions/23954874/… – PeterNL

1 Answers

0
votes

  • I don't think vignetting is your problem.

  • If "remapping" based on your calibration is supposed to account for lens distoritions, this can of course produce problems if the parameters are estimated wrong. Also, if distorition is very strong, the sampling during remapping might introduce problems. Additionally, if you use an epipolar matrix for outlier filtering, all distortions have to be accounted for.

  • There seems to be some blur that might come from the remapping or camera motion. This can definitely mess up the results. Comparing the background structures of Image 22 and Image 9 I wonder what exactly is to be matched there. It doesn't look at all like a translation, more like some kind of random illumination. Maybe you can give some insight on what exactly the images show.

Cheers, Jo