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Leaf Clustering Using Circular Densities

Luis E. Nieto-Barajas

Model Assisted Statistics and Applications2026https://doi.org/10.1177/15741699261416880article
ABDC C
Weight
0.50

What the paper says

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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https://doi.org/https://doi.org/10.1177/15741699261416880

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@article{luis2026,
  title        = {{Leaf Clustering Using Circular Densities}},
  author       = {Luis E. Nieto-Barajas},
  journal      = {Model Assisted Statistics and Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/15741699261416880},
}

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Leaf Clustering Using Circular Densities

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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