# Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulation

arXiv: https://arxiv.org/abs/2404.01952 · HTML: https://mlsanity.com/paper/2404.01952/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Automatic Tree Ring Detection using Jacobi Sets](https://mlsanity.com/paper/2010.08691/) (2020)
- [CS-TRD: a Cross Sections Tree Ring Detection method](https://mlsanity.com/paper/2305.10809/) (2023)

## Built on by (most cited first, 4 of 4)

- [DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector](https://mlsanity.com/paper/2504.16242/) (2025)
- [UruDendro4: A Benchmark Dataset for Automatic Tree-Ring Detection in Cross-Section Images of Pinus taeda L](https://mlsanity.com/paper/2511.20935/) (2025)
- [Automatic Pith Detection in Tree Cross-Section Images Using Deep Learning](https://mlsanity.com/paper/2512.00625/) (2025)
- [TRAS: An Interactive Software for Tracing Tree Ring Cross Sections](https://mlsanity.com/paper/2605.08025/) (2026)

## Abstract (arXiv)

A fully automated technique for wood pith detection (APD), relying on the concentric shape of the structure of wood ring slices, is introduced. The method estimates the ring's local orientations using the 2D structure tensor and finds the pith position, optimizing a cost function designed for this problem. We also present a variant (APD-PCL), using the parallel coordinates space, that enhances the method's effectiveness when there are no clear tree ring patterns. Furthermore, refining previous work by Kurdthongmee, a YoloV8 net is trained for pith detection, producing a deep learning-based approach to the same problem (APD-DL). All methods were tested on seven datasets, including images captured under diverse conditions (controlled laboratory settings, sawmill, and forest) and featuring various tree species (Pinus taeda, Douglas fir, Abies alba, and Gleditsia triacanthos). All proposed approaches outperform existing state-of-the-art methods and can be used in CPU-based real-time applications. Additionally, we provide a novel dataset comprising images of gymnosperm and angiosperm species. Dataset and source code are available at http://github.com/hmarichal93/apd.