On Image Analysis by the Methods of Moments
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
Multispectral Image Processing and Pattern Recognition
Multispectral Image Processing and Pattern Recognition
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
A filter bank method to construct rotationally invariant moments for pattern recognition
Pattern Recognition Letters
Selection of multiresolution rotationally invariant moments for image recognition
Mathematics and Computers in Simulation
On invariance analysis of Zernike moments in the presence of rotation with crop and loose modes
Multimedia Tools and Applications
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Rotationally invariant moments constitute important techniques applicable to a versatile number of pattern recognition applications. Although the moments are invariant with regard to spatial transformations, in practice, due to the finite screen resolution, the spatial transformation themselves affect the invariance. This phenomenon jeopardizes the quality of pattern recognition. Therefore, this paper presents an experimental analysis of the accuracy and efficiency of discrimination under the impact of the most important spatial transformations such as rotation and scaling. We evaluate experimentally the impact of the noise induced by the spatial transformations on the most popular basis functions such as Zernike polynomials, Mellin polynomials and wavelets. The analysis reveals that the wavelet based moment invariants constitute one of the best choices to construct noise resistant features.