It's well-known that Gaussian convolutions are separable. So are convolutions of first-order and second-order Gaussian derivatives. The former can be made steerable by multiplying ∂G/∂x by cos([itex]\theta[/itex]), multiplying ∂G/∂y by sin([itex]\theta[/itex]), and subtracting the result (please correct me if my math is wrong). I'm wondering if it's possible to perform a steerable convolution of a second-order mixed Gaussian derivative, i.e. ∂(adsbygoogle = window.adsbygoogle || []).push({}); ^{2}G/∂x∂y. A non-steerable convolution of that form is definitely separable - first convolve the input by ∂G/∂x and then convolve the result by ∂G/∂y.

This is not a homework assignment. I'm researching computer vision and image processing in my spare time. Second-order mixed Gaussian derivatives are apparently useful in determining saddle points in images. Thanks in advance for your help!

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# Steerable Gaussian Mixed-Derivative Convolution - Separable?

Can you offer guidance or do you also need help?

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