# Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation

arXiv: https://arxiv.org/abs/2308.11291 · HTML: https://mlsanity.com/paper/2308.11291/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [Understanding Autoencoders with Information Theoretic Concepts](https://mlsanity.com/paper/1804.00057/) (2018)

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

- [PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification](https://mlsanity.com/paper/2504.01214/) (2025)
- [TopoLoRA-SAM: Topology-Aware Parameter-Efficient Adaptation of Foundation Segmenters for Thin-Structure and Cross-Domain Binary Semantic Segmentation](https://mlsanity.com/paper/2601.02273/) (2026)
- [Multi-Scale Visual Prompting for Lightweight Small-Image Classification](https://mlsanity.com/paper/2512.03663/) (2025)
- [Cyclical Temporal Encoding and Hybrid Deep Ensembles for Multistep Energy Forecasting](https://mlsanity.com/paper/2512.03656/) (2025)

## Abstract (arXiv)

The quality of a wood log in the wood industry depends heavily on the presence of both outer and inner defects, including inner knots that are a result of the growth of tree branches. Today, locating the inner knots require the use of expensive equipment such as X-ray scanners. In this paper, we address the task of predicting the location of inner defects from the outer shape of the logs. The dataset is built by extracting both the contours and the knots with X-ray measurements. We propose to solve this binary segmentation task by leveraging convolutional recurrent neural networks. Once the neural network is trained, inference can be performed from the outer shape measured with cheap devices such as laser profilers. We demonstrate the effectiveness of our approach on fir and spruce tree species and perform ablation on the recurrence to demonstrate its importance.