What question did this study set out to answer?

The main aim is to improve leaf shape recognition through clustering based on centroid contour distances.

February 2, 2026Model Assisted Statistics and Applications0 citations

Leaf Clustering Using Circular Densities

Key Points

The main aim is to improve leaf shape recognition through clustering based on centroid contour distances.
Characterization of leaf shapes using centroid contour distances (CCD).
Normalization of CCD paths by associating contours with circular densities.
Application of distance measures to perform hierarchical clustering.
Utilization of both a small and large dataset to illustrate the approach.
Demonstrated effective clustering of leaf shapes based on shape characteristics.
Achieved meaningful hierarchical organization of leaf types in the datasets.

Abstract

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.

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