Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet and ridgelet transforms for pattern recognition and denoising
Wavelet and ridgelet transforms for pattern recognition and denoising
Translation-invariant denoising using multiwavelets
IEEE Transactions on Signal Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
Image denoising with complex ridgelets
Pattern Recognition
Pattern recognition with SVM and dual-tree complex wavelets
Image and Vision Computing
On the Combination of Ridgelets Descriptors for Symbol Recognition
Graphics Recognition. Recent Advances and New Opportunities
Ridgelet-based fake fingerprint detection
Neurocomputing
Rotation- and scale-invariant texture classification using log-polar and ridgelet transforms
Machine Graphics & Vision International Journal
Invariant pattern recognition using contourlets and AdaBoost
Pattern Recognition
An Agent-Based Paradigm for Free-Hand Sketch Recognition
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
An optimal image watermarking approach based on a multi-objective genetic algorithm
Information Sciences: an International Journal
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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In this paper, we propose a rotation-invariant descriptor for pattern recognition by using ridgelets, wavelet cycle-spinning, and the Fourier transform. Ridgelets have been developed recently and have many advantages over wavelets in applications to image processing. However, the current implementation of ridgelets cannot be applied to pattern recognition directly. In order to overcome this problem, we have successfully extracted ridgelet features within the circle surrounding the pattern we are trying to recognize. Wavelet cycle-spinning and Fourier spectrum magnitudes are used to achieve rotation invariance. The main motivation of using ridgelets is that we have a much better tool for the extraction of features based on line singularities as compared to point singularities as in the case of wavelets. Based on this observation, important features can be extracted. Our experiments show that our proposed descriptor is very robust to Gaussian noise and it achieves high recognition rates.