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Recently, I am thinking about a problem regarding image projection. I have a larger picture as bellow. enter image description here

I want to find the transformation matrix, x'=Hx. When the above image pass the transformation matrix H, I will get another smaller picture as bellow.

enter image description here

I have tried to use SIFT to find four major points and use these points to implement DLT algorithm. However, after executing SIFT, I find that there are many matching error as bellow. The distRatio is 0.5

enter image description here

I have thought for almost two weeks. Is there any different ideas which can be use to solve this problem. Thank you very much.

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  • $\begingroup$ Have you had a look to findHomography? You can apply then also a rotation using warpAffine(). $\endgroup$
    – madduci
    Dec 3, 2014 at 15:12
  • $\begingroup$ Oh! Thanks for reminding me. I will spend some time looking to findHomography and warpAffine(). $\endgroup$
    – Kuo
    Dec 4, 2014 at 0:57

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The logo you are looking at is symmetric. For example, you could rotate it by 120 degrees and get the same pattern. If you take a look, many of the "errors" are not really errors. The same pattern appears in your image many times. It is called self-similarity.

You could do several things:

  • Change descriptors to avoid confusion between features that are similar in an image.
  • Instead of finding one match for keypoint, find 3 matches and test multiple combinations by fitting a transformation and checking which fits better.
  • Limit the transformation by rotation angle, mirror, etc. For example, angle must not be larger than 60 degrees
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  • $\begingroup$ Thank you very much for your reply. I have already thought some parts of your comment before, but I have any concrete idea. And I have the rotated this picture 90 degrees. There are still some error. I think that SIFT is rotation invariant. Moreover, this picture is a special one. I have other different loge with is not symmetric and those results also have some error. $\endgroup$
    – Kuo
    Dec 3, 2014 at 14:27

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