I have a general task of discriminating images of shaded cylinders that significantly vary in 3d orientation, and almost insignificantly in size and shape.

I have treated it as a regression problem on a dataset of these oriented cylinders, but my problem is with feature computation for each sample.

Basically, what is a good way to estimate the gradient orientation from the shading of cylinders on these images?

Having limited experience, I have trouble understanding what linear or non-linear filters I should apply, so that they would be gradient-sensitive and compute orientation discriminatively for this task.

The sample image is given here(each cylinder image in a dataset is distinct): http://i.imgur.com/7GpbSE7.png

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    $\begingroup$ Basically you have a 2D image representing a 3D cylinder? And you want the orientation of the cylinder in 3D. I understand correctly? $\endgroup$ – visoft Oct 29 '13 at 15:01
  • $\begingroup$ @visoft Yes, exactly. Each cylinder in a dataset is shaded from the same light sourse. $\endgroup$ – neurohacker Oct 31 '13 at 12:24
  • $\begingroup$ The solution is posted here at analagous [stackoverflow question][1] I asked there too. [1]: stackoverflow.com/a/19379502/1547477 $\endgroup$ – neurohacker Jan 14 '14 at 11:37

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