On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Shape Analysis Using Moments and Fourier Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
A survey of moment-based techniques for unoccluded object representation and recognition
CVGIP: Graphical Models and Image Processing
Orthogonal Moment Features for Use With Parametric and Non-Parametric Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moment-Based Image Normalization With High Noise-Tolerance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of MPEG-7 shape descriptors against other shape descriptors
Multimedia Systems
Radial Zernike Moment Invariants
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
WSEAS Transactions on Information Science and Applications
An approach for on-line signature authentication using Zernike moments
Pattern Recognition Letters
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Zernike moments are one of the most commonly implemented feature extractors among the family of moment invariants. Their popularity stems from the fact that they are robust in the presence of noise. Their rotational invariance property is inherited from the angular dependence of Zernike polynomials; however the scale and translation invariance cannot be explicitly achieved. One of the indirect approaches to achieve scale and translation invariance is through expressing Zernike moments using centralized and normalized regular moments. In this paper Zernike moments are expressed in Cartesian coordinates to explicitly make them invariant to scale, translation and rotation directly without the need to use the regular moments. These Cartesian Zernike moment invariants are extensively tested using several gray level images. The results clearly demonstrate the effectiveness of the Cartesian Zernike moment invariants and superiority over the indirect approach of expressing Zernike moments using the regular moments.