Leaf clustering using circular densities
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866911407514058752 |
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| author | Nieto-Barajas, Luis E. |
| author_facet | Nieto-Barajas, Luis E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_10547 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Leaf clustering using circular densities Nieto-Barajas, Luis E. Applications 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. |
| title | Leaf clustering using circular densities |
| topic | Applications |
| url | https://arxiv.org/abs/2211.10547 |