In the biology field of botany, leaf shape recognition is an important task. One way of characterising the leaf shape is through the centroid contour distances (CCD). Each CCD path might have different resolution, so normalisation is done by considering that they are circular densities. Densities are rotated by subtracting the mean preferred direction. Distance measures between densities are used to produce a hierarchical clustering method to classify the leaves. We illustrate our approach with a real dataset.

翻译：在植物生物学领域,叶形识别是一项重要任务。叶形特征的描述方法之一是通过中子等距(CCD)来描述叶形。每个CCD路径可能有不同的分辨率,因此通过考虑它们是圆形密度来实现正常化。密度通过减去平均偏好方向来旋转。密度之间的距离测量法被用来生成一种分级组合法来对叶子进行分类。我们用一个真实的数据集来说明我们的方法。