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10 September 2007 Fruit shape classification using support vector machine
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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.
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Jiangsheng Gui, Xiuqin Rao, and Yibin Ying "Fruit shape classification using support vector machine", Proc. SPIE 6764, Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision, 67640Z (10 September 2007);

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