基于约束Delaunay三角网的茶鲜叶几何参数识别

为自动识别不同方位茶鲜叶的几何参数,在鲜叶轮廓点均匀化、稀疏化的基础上,引入约束Delaunay三角网,对叶片区域进行三角剖分,然后根据端点三角形、跨接三角形及交汇三角形的特点,计算叶片中轴线及主干中轴线,据此确定茶鲜叶的方位并对叶片进行排序,与此同时,还计算了每张叶片的长度、宽度、面积以及叶柄间距,提出反映大宗茶原料粗老度的几何特征指标,探讨了质量等级细分的方法。通过对150幅图像中174根茶鲜叶的识别结果表明,识别正确率达94.2%,平均每根鲜叶的处理时间为74.7 ms。

Geometric Parameters Recognition of Fresh Tea Leaf Based on Constrained Delaunay Triangulation

To automatically recognize the geometric parameters of fresh tea leaves with different orientations, constrained Delaunay triangulation was introduced to triangulate the area of fresh tea leaf after operations, which made the contour points distribute evenly and sparsely. Then, with the character of end triangle, adjacent triangle and connect triangle, median axis of tea leaves and tea stalks were calculated, thus the orientation of a fresh tea leaf was determined and the tea leaf was labeled. At the same time, the length, width, and area of each tea leaf was calculated, as well as distance between petioles. Based on above results, a geometric feature index concerning the oldness of fresh bulk tea was proposed, and a possible method to finely divide the grade of fresh tea was also discussed. Totally 150 images with 174 pieces of fresh tea leaves were used to validate the algorithm, and a recognition rate of 94.2% was obtained, and the average time to treat a single fresh tea leaf was 74.7ms.