On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Accuracy of Zernike Moments for Image Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the computational aspects of Zernike moments
Image and Vision Computing
Circularly orthogonal moments for geometrically robust image watermarking
Pattern Recognition
Character Reconstruction with Radial-Harmonic-Fourier Moments
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Fast and numerically stable methods for the computation of Zernike moments
Pattern Recognition
Algorithms for fast computation of Zernike moments and their numerical stability
Image and Vision Computing
Pattern Recognition and Image Analysis
Recognitive Aspects of Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional cubic convolution
IEEE Transactions on Image Processing
Accurate Computation of Zernike Moments in Polar Coordinates
IEEE Transactions on Image Processing
Accurate Calculation of Image Moments
IEEE Transactions on Image Processing
Orthogonal Rotation-Invariant Moments for Digital Image Processing
IEEE Transactions on Image Processing
Error analysis and accurate calculation of rotational moments
Pattern Recognition Letters
Analysis of algorithms for fast computation of pseudo Zernike moments and their numerical stability
Digital Signal Processing
Accurate calculation of Zernike moments
Information Sciences: an International Journal
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Orthogonal rotation invariant moments (ORIMs) are among the best region based shape descriptors. Being orthogonal and complete, they possess minimum information redundancy. The magnitude of moments is invariant to rotation and reflection and with some geometric transformation, they can be made translation and scale invariant. Apart from these characteristics, they are robust to image noise. These characteristics of ORIMs make them suitable for many pattern recognition and image processing applications. Despite these characteristics, the ORIMs suffer from many digitization errors, thus they are incapable of representing subtle details in image, especially at high orders of moments. Among the various errors, the image discretization error, geometric and numerical integration errors are the most prominent ones. This paper investigates the contribution and effects of these errors on the characteristics of ORIMs and performs a comparative analysis of these errors on the accurate computation of the three major ORIMs: Zernike moments (ZMs), Pseudo Zernike moments (PZMs) and orthogonal Fourier-Mellin moments (OFMMs). Detailed experimental analysis reveals some interesting results on the performance of these moments.