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Proceedings Paper

Fruit shape classification using support vector machine
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Paper Abstract

A new method along with shape descriptor using support vector machine 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 and are compared to different classifiers. This method using support vector machine as classifier performs better than traditional approaches that is verified by some experiments.

Paper Details

Date Published: 10 September 2007
PDF: 9 pages
Proc. SPIE 6764, Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision, 67640Z (10 September 2007); doi: 10.1117/12.735235
Show Author Affiliations
Jiangsheng Gui, Zhejiang Univ. (China)
Xiuqin Rao, Zhejiang Univ. (China)
Yibin Ying, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 6764:
Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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