A fast algorithm for the computation of axial moments and its application to the orthogonal fitting of curves
This paper describes a fast algorithm to compute local axial moments used in the detection of objects of interest in images. The basic idea is the elimination of redundant operations while computing axial moments for two neighboring angles of orientation. The main result is that the complexity of the recursive computation of axial moments becomes independent of the total number of computed moments at a given point, i.e., it is of the order O(N) where N is the size of the data set. This result is of great importance in computer vision since many feature extraction methods rely on the computation of axial moments. The use of this algorithm for fast object skeletonization in images by orthogonal regression fitting is described in detail, with the experimental results confirming the theoretical computational complexity.
fast algorithm axial moment recursive computation orthogonal regression primitive kernel function curve fitting