基于Zernike矩的马铃薯薯形检测

Potato shape detection based on Zernike moments

目前基于机器视觉的马铃薯薯形检测的形状特征单一,相关研究较少,为了进一步探索合适的形状特征参数及检测方法,该文将Zernike矩作为特征参数并利用支持向量机实现了马铃薯薯形的检测分类,准确度较高。首先用截取最佳图像的方法对马铃薯图像进行归一化,使得归一化后的图像具有平移和尺度不变性,然后从归一化的图像中计算具有旋转不变性的Zernike矩参数,通过特征筛选确定分类的19个Zernike特征参数,最后将这些特征输入到支持向量机中,用高斯径向基核函数(RBF)和Sigmoid核函数构建混合核函数,完成马铃薯薯形检测分类,对薯形良好和畸形的检测准确率达93%和100%,能够准确剔除畸形马铃薯并满足实际检测的要求。

Up to now,the shape feature of potato shape detection based on machine vision is single with little relative investigation.Taking Zernike moments and support vector machine as shape detecting feature and classifier respectively, an approach to potato shape detection and classification,which yielded a relatively higher accuracy,was proposed in this paper.The image was first normalized by using best image segmentation method to obtain scale and translation invariance.The rotation invariant Zernike features were then extracted from the normalized images,among which 19 features were selected.At last,shape classification was accomplished by inputting the selected features into support vector machine classifier.A new mixed kernel function of RBF and Sigmoid kernel function was proposed,resulting in 93%and 100%detection accuracy for the perfect and malformation potatoes,respectively.