A Novel Form of Affine Moment Invariants of Grayscale Images

Yuanbin Wang, Xingwei Wang, Bin Zhang, Ying Wang


We present a general method to derive robust invariants of grayscale images under affine geometric transformation.  In the literature, there are well studied affine moment invariants of grayscale images. The problem is that only few of the invariants are low orders. Higher order affine moment invariants are sensitive to noise and hard to implement. In this paper, we extend the traditional definition of the geometric moment by encapsulating the image functions by some wrapper functions. A general theorem to construct the affine invariants consisting of the extended moments of a grayscale image is presented. Using this method, different forms of low order affine moment invariants are constructed. The traditional affine moment invariants are a special type of the proposed new affine moment invariants. These forms of invariants are less sensitive to noise and easy to implement.

DOI: http://dx.doi.org/10.5755/j01.eee.19.1.3262


Affine moment invariants; computer vision; image recognition; moment methods

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Print ISSN: 1392-1215
Online ISSN: 2029-5731