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Comparative Study of Segmentation Methods for Tree Leaves Extraction
- Source :
- ACM ICVS, Workshop VIGTA (International Workshop on Video and Image Ground Truth in computer vision Applications), ACM ICVS, Workshop VIGTA (International Workshop on Video and Image Ground Truth in computer vision Applications), Jul 2013, St Petersbourg, Russia. pp.1-10, ⟨10.1145/2501105.2501109⟩, VIGTA@ICVS
- Publication Year :
- 2013
- Publisher :
- HAL CCSD, 2013.
-
Abstract
- International audience; In this paper, we present a comparative study of segmentation methods, tested for an issue of tree leaves extraction. Approaches implemented include processes using thresholding, clustering, or even active contours. The observation criteria, such as the Dice index, Hamming measure or SSIM for example, allow us to highlight the performance obtained by our guided active coutour algorithm that is specially dedicated to tree leaf segmentation (G. Cerutti et al., Guiding Active Contours for Tree Leaf Segmentation and Identification. ImageCLEF2011). We currently offer a dedicated segmentation tree leaf benchmark, comparing fourteen segmentation methods (ten unsupervised and four supervised) following twenty evaluation criteria.
- Subjects :
- Segmentation-based object categorization
Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-space segmentation
020207 software engineering
Pattern recognition
02 engineering and technology
Image segmentation
Thresholding
Tree (data structure)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Segmentation
Computer vision
[INFO]Computer Science [cs]
Artificial intelligence
Cluster analysis
business
Hamming code
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ACM ICVS, Workshop VIGTA (International Workshop on Video and Image Ground Truth in computer vision Applications), ACM ICVS, Workshop VIGTA (International Workshop on Video and Image Ground Truth in computer vision Applications), Jul 2013, St Petersbourg, Russia. pp.1-10, ⟨10.1145/2501105.2501109⟩, VIGTA@ICVS
- Accession number :
- edsair.doi.dedup.....9218aa63390a2446cd98dadf9ca4b637