OpenCV "filter2d" is a crosscorrelation, but not like MASM::CrossCorrelation.vi

[EngrStudent]

Question:

What transformations on inputs or settings, assuming equal-sized 2d "src" and "kernel", are required for the MASM::CrossCorrelation.vi to give output consistent with CpenCV::filter2d?

Details:

I have code that is slow that was originally written in Python with OpenCV.

At the heart of this is the OpenCV::filter2d function which, according to OpenCV is really a correlation.

It claims to be a correlation, not a convolution, and in fact gives guidance for how to turn it into a convolution.

How do I go about getting consistent results?  OpenCV has results with signed integer-valued intensities.  LabVIEW has a table with very small coefficients, relatively speaking (1e-7 vs integers [-255,255]).

References:

• https://stackoverflow.com/questions/26857829/does-filter2d-in-opencv-really-do-its-job
• https://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html#filter2d
• https://zone.ni.com/reference/en-XX/help/373601B-01/lvmasmtref/masm_cross_correlation/

Update:

• I'm going to try setting the input to type int8, and see how that impacts this.

[herringbone]

What do you think the resolution of the coefficients needs to be to match your previous codes performance?

[altenbach]

Do you have some simple input matrices and expected result? What size output do you want? (same?, doublexdouble?)

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@EngrStudent wrote:

Update:

• I'm going to try setting the input to type int8, and see how that impacts this.

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I8 seems ill advised, because I guaranteed you'll overflow unless all inputs are <<<11 (a single 11x11 is already close to the max (127) and then you do all the additions on top. Most advanced math functions are only implemented for DBL anyway.