Hello all,

I'm interested in 2D discrete transforms (such as Discrete Wavelet Transforms, [Curvelets][1], Ridgelets, Beamlets etc.) that operate on a disk and:

 1. Are invariant to rotations **only**
 2. Output a transformed signal with relatively low informational entropy
 3. Are computationally relatively efficient (in terms of computational complexity) 

In other words, I'm interested in 2D discrete transforms that output the same transformation for arbitrary 2D rotations of the input, but that are not invariant to any other changes of the input. Ideally, these transforms should compress the input as much as possible in terms of information entropy (i.e. necessary bits to represent the output), and be "computable" in a practical sense.

As additional context to my question, I am planning on using such transforms in the domain of computer vision to train a classifier on instances of objects that might appear rotated arbitrarily around the image center point.


Thank you

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  [1]: http://www.curvelet.org/