摘要
以无裂纹蛋和裂纹蛋为测试对象,采用机器视觉技术和支持向量机等技术手段,分析无裂纹蛋和裂纹蛋在图像上的差异,提取特征参数,实现蛋壳裂纹的自动识别;针对蛋壳表面的亮斑,对预处理后的图像运行消除亮斑算法并进行区域标记。在此基础上,从5个不同视角提取13个能够表征无裂纹蛋和裂纹蛋的特征参数,分别是图像标记区域参数(区域标记数和标记点数)、几何特征参数(长轴和短轴)、基于Freeman链码的形状参数(形状数)、纹理特征参数(均值、标准偏差、平滑度、三阶矩、一致性、熵)和频谱特性参数(最大幅值和最大相位)。采用Adaboosting算子对上述特征参数进行优化,突出影响因子较大的参数组合,作为SVM的输入向量,建立蛋壳裂纹的识别模型。结果表明:该方法对蛋壳表面的亮斑、微小裂纹及普通裂纹均具有识别能力,模型正确率达97.5%,符合蛋品企业对蛋壳裂纹检测的精度要求。
With good eggs and crack eggs as experimental subjects,the machine vision and support vector machines(SVM)were used to study the differences between good eggs and crack eggs,and multi-feature parameters were extracted to achieve automatic recognition of crack eggs.Firstly,an algorithm would be run to eliminate bright spots on the preprocessed image of the surface of eggs before marking them by region.Secondly,13 characteristic parameters from five different domains to identify the good eggs and crack eggs were extracted,and these parameters were as follows:the marked region parameters of images(the number of markers and the marker area points),the geometric parameters(the major axis and the minor axis),the shape parameters based on Freeman chain code(the shape number),the texture parameters(the mean,the standard deviation,the smoothness,the third moment,the uniformity and the entropy)and the spectral parameters(the maximum amplitude and the maximum phase).Thirdly,to highlight the greater impact factors between 13 parameters and to shorten the detection time,adaboosting algorithm was used to optimize the above parameters,which was the input vector of SVM.Finally,the recognition model was built by SVM.The results indicated that the accuracy rate of the recognition model was 97.5%,which could meet the requirements of enterprises basically.
出处
《华中农业大学学报》
CAS
CSCD
北大核心
2015年第2期136-140,共5页
Journal of Huazhong Agricultural University
基金
国家自然科学基金项目(51105160)
华中农业大学博士启动基金项目(52902-0900206027)
关键词
蛋壳
裂纹
机器视觉
支持向量机
eggshell
crack
machine vision
support vector machine(SVM)
