  Image moments are weighted sums over pixel values in a given image and are
used in object detection and localization. Raw image moments are derived
directly from the image and are fundamental in deriving moment invariants
quantities. The current general algorithm for raw image moments is
computationally expensive and the number of multiplications needed scales with
the number of pixels in the image. For an image of size (N,M), it has O(NM)
multiplications. In this paper we outline an algorithm using the Discrete Radon
Transformation for computing the raw image moments of a grayscale image. It
reduces two dimensional moment calculations to linear combinations of one
dimensional moment calculations. We show that the number of multiplications
needed scales as O(N + M), making it faster then the most widely used algorithm
of raw image moments.
