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
A survey of moment-based techniques for unoccluded object representation and recognition
CVGIP: Graphical Models and Image Processing
A new class of Zernike moments for computer vision applications
Information Sciences: an International Journal
Face detection by neural network trained with Zernike moments
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Face detection by neural network trained with Zernike moments
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Hand-based verification and identification using palm-finger segmentation and fusion
Computer Vision and Image Understanding
On-line signature authentication using Zernike moments
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
A zernike moment phase-based descriptor for local image representation and matching
IEEE Transactions on Image Processing
Algorithms for fast computation of Zernike moments and their numerical stability
Image and Vision Computing
Content-based emblem retrieval using Zernike moments
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
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Zernike Moments are useful tools in pattern recognition and image analysis due to their orthogonality and rotation invariance property. However, direct computation of these moments is very expensive, limiting their use especially at high orders. There have been some efforts to reduce the computational cost by employing quantized polar coordinate systems, which also reduce the accuracy of the moments. In this paper, we propose an efficient algorithm to accurately calculate Zernike moments at high orders. To preserve accuracy, we do not use any form of coordinate transformation and employ arbitrary precision arithmetic. The computational complexity is reduced by detecting the common terms in Zernike moments with different order and repetition. Experimental results show that our method is more accurate than other methods and it has comparable computational complexity especially in case of using large images and high order moments.