mlsanityData to 14 Oct 2026

CS-TRD: a Cross Sections Tree Ring Detection method

arXiv 2305.10809 ↗First posted 18 May 2023cs.CV q-bio.PEcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 32 references that are in the map. First posted on arXiv 18 May 2023.

Builds on

3 of 32 references.

References · 3

  1. 2020U2-Net: Going Deeper with Nested U-Structure for Salient Object Detectioncited by 412
  2. 2022Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sectionscited by 7
  3. 2020Automatic Tree Ring Detection using Jacobi Setscited by 5

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulationcited by 4
  2. 2025DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detectorcited by 2