CS-TRD: a Cross Sections Tree Ring Detection method
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866909286350716928 |
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| author | Marichal, Henry Passarella, Diego Randall, Gregory |
| author_facet | Marichal, Henry Passarella, Diego Randall, Gregory |
| contents | This work describes a Tree Ring Detection method for complete Cross-Sections of Trees (CS-TRD) that detects, processes and connects edges corresponding to the tree's growth rings. The method depends on the parameters for the Canny Devernay edge detector (sigma), a resize factor, the number of rays, and the pith location. The first five are fixed by default. The pith location can be marked manually or using an automatic pith detection algorithm. Besides the pith localization, CS-TRD is fully automated and achieves an F-Score of 89% in the UruDendro dataset (of Pinus taeda) and 97% in the Kennel dataset (of Abies alba) without specialized hardware requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_10809 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CS-TRD: a Cross Sections Tree Ring Detection method Marichal, Henry Passarella, Diego Randall, Gregory Computer Vision and Pattern Recognition Populations and Evolution This work describes a Tree Ring Detection method for complete Cross-Sections of Trees (CS-TRD) that detects, processes and connects edges corresponding to the tree's growth rings. The method depends on the parameters for the Canny Devernay edge detector (sigma), a resize factor, the number of rays, and the pith location. The first five are fixed by default. The pith location can be marked manually or using an automatic pith detection algorithm. Besides the pith localization, CS-TRD is fully automated and achieves an F-Score of 89% in the UruDendro dataset (of Pinus taeda) and 97% in the Kennel dataset (of Abies alba) without specialized hardware requirements. |
| title | CS-TRD: a Cross Sections Tree Ring Detection method |
| topic | Computer Vision and Pattern Recognition Populations and Evolution |
| url | https://arxiv.org/abs/2305.10809 |