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 associating each contour to a circular density. Densities are rotated by subtracting the mean or mode preferred direction. Distance measures between densities are used to produce a hierarchical clustering method to cluster the leaves. We illustrate our approach with a motivating small dataset as well as a larger dataset.

翻译：在植物学这一生物学领域中，叶片形状识别是一项重要任务。表征叶片形状的一种方法是通过质心轮廓距离（CCD）。每个CCD路径可能具有不同的分辨率，因此通过将每个轮廓与圆形密度相关联进行归一化处理。通过减去均值或众数优选方向对密度进行旋转。利用密度间的距离度量构建层次聚类方法，以实现叶片聚类。我们通过一个具有启发性的小型数据集以及一个较大规模的数据集对本方法进行了说明。