The gradient of a scalar/vector function gives the vector/tensor of greatest change. I am looking for the inverse concept, which gives me the direction of least change.
Inverting the gradient vector/tensor will obviously not work since it corresponds to the direction of greatest negative change.
I am aware that this direction has to be orthogonal to the gradient. However there are infinitely many vectors orthogonal to the gradient in 3D+. This is where I am stuck.
The intent is applying this to a regular grid through finite differencing.
Best Answer
I figured out a solution. The trick is to compute the structure tensor (i.e. second moment matrix, see https://en.wikipedia.org/wiki/Structure_tensor ) from the gradient field. The eigenvector corresponding to the minimum eigenvalue is the direction of least change.