On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
Orthogonal Moment Features for Use With Parametric and Non-Parametric Classifiers
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
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complete Sets of Complex Zernike Moment Invariants and the Role of the Pseudoinvariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
RETRACTED: Invariance image analysis using modified Zernike moments
Pattern Recognition Letters
Geometric Invariance in image watermarking
IEEE Transactions on Image Processing
Invariant image watermark using Zernike moments
IEEE Transactions on Circuits and Systems for Video Technology
Hough transform based fast skew detection and accurate skew correction methods
Pattern Recognition
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
Face recognition using Zernike and complex Zernike moment features
Pattern Recognition and Image Analysis
Analysis of algorithms for fast computation of pseudo Zernike moments and their numerical stability
Digital Signal Processing
Discriminative Zernike and Pseudo Zernike Moments for Face Recognition
International Journal of Computer Vision and Image Processing
Accurate calculation of Zernike moments
Information Sciences: an International Journal
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Zernike moments which are superior to geometric moments because of their special properties of image reconstruction and immunity to noise, suffer from several discretization errors. These errors lead to poor quality of reconstructed image and wide variations in the numerical values of the moments. The predominant factor, as observed in this paper, is due to the discrete integer implementation of the steps involved in moment calculation. It is shown in this paper that by modifying the algorithms to include discrete float implementation, the quality of the reconstructed image improves significantly and the first-order moment becomes zero. Low-order Zernike moments have been found to be stable under linear transformations while the high-order moments have large variations. The large variations in high-order moments, however, do not greatly affect the quality of the reconstructed image, implying that they should be ignored when numerical values of moments are used as features. The 11 functions based on geometric moments have also been found to be stable under linear transformations and thus these can be used as features. Pixel level analysis of the images has been carried out to strengthen the results.