Fruit shape classification using Zernike moments

Jiangsheng Gui; Weida Zhou

doi:10.1117/12.866405

19 August 2010 Fruit shape classification using Zernike moments

Jiangsheng Gui, Weida Zhou

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 782015 (2010) https://doi.org/10.1117/12.866405
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

Abstract

A new method along with Zernike moments for classify fruit shape is developed, the image is first subjected to a normalization process using its regular moments to obtain scale and translation invariance, the rotation invariant Zernike features are then extracted from the scale and translation normalized images and the numbers of features are decided by primary component analysis (PCA), at last, these features are input to support vector machine (SVM) classifier. This method performs better than traditional approaches because of their orthogonal base and rotation invariance of the defined features on them, which is verified by experiments on Zernike moments and Fourier descriptors.

Citation Download Citation

Jiangsheng Gui and Weida Zhou "Fruit shape classification using Zernike moments", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 782015 (19 August 2010); https://doi.org/10.1117/12.866405

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