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Tree leaves extraction in natural images: comparative study of preprocessing tools and segmentation methods.
Summary
This study compares tree leaf segmentation methods for mobile apps. The Guided Active Contour method excels in extracting leaves from natural images, outperforming other techniques.
Area of Science:
- Computer Vision
- Image Processing
- Botanical Imaging
Background:
- Accurate tree leaf extraction is crucial for botanical identification apps.
- Previous research has explored various segmentation techniques for this purpose.
Purpose of the Study:
- To comparatively evaluate diverse image segmentation methods for tree leaf extraction.
- To assess the impact of segmentation choices on mobile application performance.
- To investigate preprocessing enhancements for improved leaf extraction.
Main Methods:
- Comparative analysis of 14 unsupervised and 6 supervised segmentation methods.
- Testing on a dataset of 232 smartphone-acquired tree leaf images with natural backgrounds.
- Evaluation of preprocessing techniques including user input strokes and color distance maps.
Main Results:
- The Guided Active Contour method demonstrated superior performance across most evaluation criteria.
- Preprocessing tools, particularly user interaction, showed potential for performance enhancement.
- The study established an online benchmark for evaluating segmentation algorithms.
Conclusions:
- The Guided Active Contour method is highly effective for tree leaf segmentation in natural images.
- User-guided preprocessing significantly improves segmentation accuracy.
- The developed benchmark facilitates future research in botanical image analysis.
