Tree leaves extraction in natural images: Comparative study of pre-processing tools and segmentation methods

Manuel Grand-Brochier 1 Antoine Vacavant 1 Guillaume Cerutti 2 Camille Kurtz 3 Jonathan Weber 4 Laure Tougne 5
2 VIRTUAL PLANTS - Modeling plant morphogenesis at different scales, from genes to phenotype
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique, UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
5 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In this paper, we propose a comparative study of various segmentation methods applied to the extraction of tree leaves from natural images. This study follows the design of a mobile application, developed by Cerutti et al. (published in ReVeS Participation-Tree Species Classification Using Random Forests and Botanical Features. CLEF 2012), to highlight the impact of the choices made for segmentation aspects. All the tests are based on a database of 232 images of tree leaves depicted on natural background from smartphones acquisitions. We also propose to study the improvements, in terms of performance, by using pre-processing tools such as the interaction between the user and the application through an input stroke, as well as the use of color distance maps. The results presented in this paper shows that the method developed by Cerutti et al. (denoted Guided Active Contour), obtains the best score for almost all observation criteria. Finally we detail our online benchmark composed of 14 unsupervised methods and 6 supervised ones.
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Manuel Grand-Brochier, Antoine Vacavant, Guillaume Cerutti, Camille Kurtz, Jonathan Weber, et al.. Tree leaves extraction in natural images: Comparative study of pre-processing tools and segmentation methods. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2015, 24 (5), pp.1549-1560. ⟨10.1109/TIP.2015.2400214⟩. ⟨hal-01176699⟩

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