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Proceedings Paper

Fruit shape classification using Zernike moments
Author(s): Jiangsheng Gui; Weida Zhou
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Paper 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.

Paper Details

Date Published: 20 August 2010
PDF: 7 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 782015 (20 August 2010); doi: 10.1117/12.866405
Show Author Affiliations
Jiangsheng Gui, Zhejiang Sci-Tech Univ. (China)
Zhejiang Univ. (China)
Weida Zhou, Zhejiang Sci-Tech Univ. (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

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