Moment Invariants for Image Symmetry Estimation and Detection

We give a general framework of statistical aspects of the problem of understanding and a description of image symmetries, by utilizing the theory of moment invariants. In particular, we examine the issues of joint symmetry estimation and detection. These questions are formulated as the statistical decision and estimation problems since we cope with images observed in the presence of noise. The estimation/detection procedures are based on the minimum L2-distance between the reconstructed image function and the reconstruction of its hypothesized symmetrical version. Our reconstruction algorithms are relying on a class of radial orthogonal moments. The proposed symmetry estimation and detection techniques reveal some statistical optimality properties. Our technical developments are based on the statistical theory of nonparametric testing and semi-parametric inference.