Comparative study of segmentation methods for tree leaves extraction

Authors:
Manuel Grand-Brochier;Antoine Vacavant;Guillaume Cerutti;Kevin Bianchi;Laure Tougne
Affiliations:
CNRS, Univ. Lumière Lyon 2, Bron, France;CNRS, Univ. Lumière Lyon 2, Clermont-Fd, France;CNRS, Univ. Lumière Lyon 2, Bron, France;CNRS and Keosys Company, Saint-Herbain, France;CNRS, Univ. Lumière Lyon 2, Bron, France
Venue:
Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications
Year:
2013

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Abstract

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 the guided active contour 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 automatic and four semi-automatic) following twenty evaluation criteria.