Automatic Leaf Extraction from Outdoor Images
This work addresses a domain-specific challenge for plant recognition and disease analysis, but it is incremental as it builds on existing segmentation techniques.
The paper tackled the problem of segmenting leaves from cluttered outdoor images by exploiting venous systems and low-level features, achieving improved results compared to existing methods.
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.