# Automatic Leaf Extraction from Outdoor Images

arXiv: https://arxiv.org/abs/1709.06437 · HTML: https://mlsanity.com/paper/1709.06437/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

## Built on by (most cited first, 3 of 3)

- [Self-Supervised Leaf Segmentation under Complex Lighting Conditions](https://mlsanity.com/paper/2203.15943/) (2022)
- [Computer-aided Interpretable Features for Leaf Image Classification](https://mlsanity.com/paper/2106.08077/) (2021)
- [Look how they have grown: Non-destructive Leaf Detection and Size Estimation of Tomato Plants for 3D Growth Monitoring](https://mlsanity.com/paper/2304.03610/) (2023)

## Abstract (arXiv)

Automatic plant recognition and disease analysis may be streamlined by an image of a complete, isolated leaf as an initial input. Segmenting leaves from natural images is a hard problem. Cluttered and complex backgrounds: often composed of other leaves are commonplace. Furthermore, their appearance is highly dependent upon illumination and viewing perspective. In order to address these issues we propose a methodology which exploits the leaves venous systems in tandem with other low level features. Background and leaf markers are created using colour, intensity and texture. Two approaches are investigated: watershed and graph-cut and results compared. Primary-secondary vein detection and a protrusion-notch removal are applied to refine the extracted leaf. The efficacy of our approach is demonstrated against existing work.