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some questions about kernel transform #35
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Hi, we used the transposed convolution in this way: https://i.stack.imgur.com/f2RiP.gif The stride is the distance between the control points (blue). We then use a kernel to interpolate the values between those control points to obtain a dense transformation by using the transposed convolution. I hope this could answer your question. |
@RobinSandkuehler |
Hi, the control points are the parameter that are optimized and not generated by a convolution with a displacement. The transposed convolution is used to generate the dense transformation field out of the control points. So we have only one direction C->B. |
@RobinSandkuehler Thank you again for your kind answers. I understand. |
Hi,
`
def _compute_flow_3d(self):
`
I found on the Internet that transposed convolution doesn’t reverse the standard convolution by values, rather by dimensions only.
And I have also read other bspline implementations like https://github.com/C4IR/FAIR.m/blob/master/kernel/transformations/splineTransformation2D.m.
They get spline coefficients and then compute the displacement.
I am wondering if your implementation has some theories behind.
Thank you very much if you could answer my questions!
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