一种粘连谷物图像分割及杂质识别算法开发

本文提出了一种自动分割粘连谷物并识别杂质的算法。该算法首先使用基于形态学多尺度分解的分水岭算法对粘连颗粒进行分割,接着提取各个颗粒的形态特征和颜色特征,然后计算上述样本颗粒的特征值与完好谷物的特征值之间的马氏距离,并比较它们的马氏距离与设定的阈值,来识别混杂在谷物中的杂质。通过对5种谷粒(普通大米、粗米、糙米、普通大麦、糯麦)的实验,结果表明该算法取得了较好的分割与识别效果,为谷物质量分级的评定提供了一种快速有效地检测谷物产品杂质率的方法。

Development of Splitting and Identification Algorithm of Touching Kernels in Digital Images

An improved automatic method of separation and identification of touching kernels and foreign materials in digital images was proposed.At first,the image was filtered and converted into a binary image.Then the touching kernels were separated by using watershed algorithm based on morphological multiscale decomposition(MSD).Next,the morphological and color features from each segmented component and standard kernel were extracted for calculation of Mahalanobis distance between each segmented component’s features and those of standard kernels.Finally foreign materials were identified by comparing Mahalanobis distance with the given threshold.Five kinds of kernels(common rice,rough rice,brown rice,common barley and glutinous barley) were tested and the experimental results showed that the proposed algorithm could separate touching kernels effectively and identify foreign material correctly.