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首页> 外文期刊>Journal of Computational Methods in Sciences and Engineering >Detection of crack eggs by image processing and soft-margin support vector machine
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Detection of crack eggs by image processing and soft-margin support vector machine

机译：通过图像处理和软保证金支持向量机检测裂化蛋

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摘要

This paper presented a non-destructive approach for detection of intact and crack eggs using transmission imaging combined with support vector machine classifier. Two hundred brown chicken eggs, including 100 intact and 100 crack eggs were collected as samples. Transmission vision system was developed to capture the sample images. Green color component and edge algorithm based on confidence were then used to transform the color image into edge image for next analysis. Features (mean, variance and third moment) characterizing the differences between crack and intact eggs were extracted through analyz-ing projection functions as input vectors of the detection model. The detection model in this paper was conducted by support vector machine (SVM). Cracked and Intact eggs could be distinguished by SVM using the statistics parameters. Experimental results showed that the overall identification accuracy in training and test sets were 94% and 93% using 10-fold cross validation approach, respectively.
机译：本文介绍了使用透射成像与支持向量机分类器的透射成像检测完整和裂化卵的非破坏性方法。将两百棕色的鸡蛋，包括100个完整和100个裂纹蛋收集为样品。开发了传输视觉系统以捕获样本图像。然后使用基于置信度的绿色分量和边缘算法将彩色图像转换为下一个分析的边缘图像。通过分析投影用作检测模型的输入向量，提取表征裂缝和完整卵之间的差异的特征（平均值，方差和第三矩）。本文中的检测模型由支持向量机（SVM）进行。使用统计参数，SVM可以区分破裂和完整的蛋。实验结果表明，培训和试验集中的整体鉴定精度分别使用10倍交叉验证方法94％和93％。

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