Comments (5)
Thank you for reporting this problem.
self->peakThreshold is relative to the cornerness measure. This would coincide with the Laplacian scale score for a Laplacian-based detector, but would differ for Harris corner points or Hessian. Thus using self->peakThrehsold would not be correct for Harris-Laplace or Hesisan-Laplace.
The correct solution is to add a new parameter to the covariant detector class covdet to specify this parameter independently as an additional threshold.
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Sure, that sounds good too. Unfortunately, the scale of my data is very different than typical so this threshold is highly relevant for me. Thanks.
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I implemented a patch in the devel branch. Could you check it out to see if that address your problem?
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I'm not seeing it in the list of commits, has it been pushed to github?
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Ah, ok, now I see it. I've taken a look and that should fix it, thanks!
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