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What's the best feature matcher for pairs of very similar images?

asked 2014-03-23 04:35:48 -0600

a.francesconi gravatar image

updated 2014-03-23 05:05:32 -0600

berak gravatar image

For several months I worked with a toolbox that uses BRISK detector + ORB descriptor + BruteForce matcher in order to get matches from a "particular" pair of images. The difference between the traditional usage of such kind of algorithms is that my image pair are very similar between each other. In fact, they differ for a very little horizontal translation (and a little rotation, too). Like this one:

image description

Now, the combination of BRISK+ORB often produces good results, but there are cases in which the computation returns some matches that are affected by a small error, in terms of distance from the real location of the correspondent point in the second image (lets say, for example, 5 pixels).

In some cases this error could be treated as irrelevant, in other cases it produces a very bad input for the subsequent step. I'm wondering if there is a combination of (fast and lightweight) detector, descriptor and matcher that fits better my needs.

A NOTE: originally, I implemented just BRISK detector + descriptor and a BruteForce matcher and results were better. The problem is that BRISK descriptor is a very memory-consuming algorithm, I cannot implement it on devices with low RAM. This is why I switched to ORB.

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answered 2014-04-08 07:19:16 -0600

Kevin B. gravatar image

You can use these methods to improve your matches:

  • distance value (DMatch object)
  • ratio test (David Lowed, see paper)
  • cross check test (you can use the OpenCV or do it yourself)
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Asked: 2014-03-23 04:35:48 -0600

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Last updated: Apr 08 '14