A fast recursive algorithm for the computation of axial moments

This paper describes a fast algorithm to compute local axial moments used for the detection of objects of interest in images. The basic idea is grounded on the elimination of redundant operations while computing axial moments for two neighboring angles of orientation. The main result is that the complexity of recursive computation of axial moments becomes independent of the total number of computed moments in a given point, i.e. it is of the order O(N) where N is the data size. This result is of great importance in computer vision since many feature extraction methods are based on the computation of axial moments. The experimental results confirm the time complexity and accuracy predicted by the theoretical analysis.

[1]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[2]  Demetri Psaltis,et al.  Recognitive Aspects of Moment Invariants , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Mehdi Hatamian,et al.  A real-time two-dimensional moment generating algorithm and its single chip implementation , 1986, IEEE Trans. Acoust. Speech Signal Process..

[4]  Paul J. Zsombor-Murray,et al.  Fast algorithm for the computation of moment invariants , 1987, Pattern Recognit..

[5]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Jun Shen,et al.  Fast computation of moment invariants , 1991, Pattern Recognit..

[7]  Jun Shen,et al.  Two-dimensional local moment, surface fitting and their fast computation , 1994, Pattern Recognit..

[8]  Martin D. Levine,et al.  Annular symmetry operators: a method for locating and describing objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[9]  Fritz Albregtsen,et al.  Fast and exact computation of Cartesian geometric moments using discrete Green's theorem , 1996, Pattern Recognit..

[10]  Vito Di Gesù,et al.  Local operators to detect regions of interest , 1997, Pattern Recognit. Lett..

[11]  Roman M. Palenichka Fast Recursive Computation of Local Axial Moments by Using Primitive Kernel Functions , 1999, ACPC.

[12]  Adam Krzyzak,et al.  Learning and Design of Principal Curves , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Vito Di Gesù,et al.  A fast recursive algorithm to compute local axial moments , 2001, Signal Process..