Fast and scalable computations of 2D image moments ?
Image moments are used in image analysis for object modelling and matching. The moment computation of a two-dimensional (2D) image involves a significant amount of multiplication and addition in a direct method. In this paper, we use the suffix sum functions to compute the gray-level image moments instead of using a direct method. This new method can reduce drastically the number of multiplications required. We first derive the mathematical relationships between moment computations and suffix sums. Based on the derived mathematical relationships, four new parallel algorithms for computing image moments are derived on various computational models. By integrating the advantages of both optical transmission and electronic computation, the 2D image moments can be computed in constant time on a 2D array with reconfigurable optical buses. The performance comparison shows that the proposed method is fast and efficient. In addition, three scalable and cost optimal algorithms are derived on the AROB, the hypercube computer and the EREW PRAM model.
image moments suffix sums reconfigurable optical buses moment invariants scalable algorithm pattern recognition