基于支持向量机的纸张缺陷识别研究

2010 WASE International Conference on Information Engineering Pub Date : 2010-08-14 DOI:10.1109/ICIE.2010.49

Qiu Shubo, Gu Shuai, Zhang Tongxing

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引用次数: 6

摘要

支持向量机(SVM)是一种非常流行的算法，基于SVM，开发了一个纸张缺陷识别系统。在纸张缺陷图像分割阶段，提出了一种基于支持向量机的算法;在纸张缺陷特征提取阶段，采用多类支持向量机对纸张缺陷进行分类。实验结果表明，该系统的识别速度更快，平均识别率为97%，性能明显优于BP神经网络算法。

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Research on Paper Defects Recognition Based on SVM

Support Vector Machine (SVM) is a very popular arithmetic, based on SVM, developed a paper defects recognition system. In the stage of paper defects image segmentation, proposed a algorithm based on the SVM, While in the stage of paper defects feature extraction, applied a multi-class SVM to classify the paper defects. Experimental results show that the proposed system yields faster recognition speed and the average recognition rate of 97%,which performance is significantly better than BP neural network algorithm.

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2010 WASE International Conference on Information Engineering

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