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The combination of Zernike moments and curvelet-like transform can bring the most significant feature coefficients in pattern recognition. Instead of using Zernike moments in the image, we apply Zernike moments on every corona of curvelet-like transform. This combination brings special properties when we can represent the shape of each the corona through Zernike moments. More especially, we use orientation of wedge of curvelet-like transform at specific scale for Zernike moments instead of using uniformly partition angle as in normal Zernike moments. The experiment on classification of sub-cellular location protein images with these coefficients has shown the advance points in comparing to normal Zernike moments in whole image. | Multi-coronas Zernike moments on curvelet-like transform and application to pattern recognition
