基于机器视觉的麻核桃分类算法设计

麻核桃的分类有助于产品销售,传统分类方式仅限于人工操作。为实现麻核桃的自动化分类,设计一种麻核桃分类算法,该算法通过构建核桃像素概率分布模型实现。根据核桃不同视图,利用同类核桃构建像素概率分布模型以及惩戒模型。利用矩阵乘积方式将待测核桃样本与两个模型分别进行对比,并将计算结果作为测试样本种类归属的判据,并以此对核桃进行分类。利用3 000个核桃样本,建立一个包含9 000张图片的数据集,对算法的性能进行评估。经过测试,在3次交叉测试实验中,该算法取得了97.36%的识别率。实验结果表明,在麻核桃分类识别中,该方法具有较好的应用前景。

Design of Hemp Walnut classification algorithmbased on machine vision

The classification of hemp walnuts helps in the sale of products, and the traditional classification is limited to manual operations. In order to realize the automatic classification of hemp walnut, a hemp walnut classification algorithm was designed, which was realized by constructing a walnut pixel probability distribution model. Specifically, based on different views of the walnut, a pixel probability distribution model is constructed using the same type of walnut. The walnut to be tested is compared with the model, and the ratio of the weight inside and outside the contour of the walnut to be tested is taken as the similarity between the walnut and the model, and the walnut is classified. Through the establishment of a data set of 9 000 images, which contains three types of walnuts, lion head, tiger head and official hat, the performance of the algorithm is tested. After testing, the algorithm achieved an average recognition rate of 97.36% in 3 cross-test experiments.The experimental results show that this method has a good application prospect of the classification and identification of hemp walnut.