Bark Recognition to Improve Leaf-based Classification in Didactic Tree Species Identification

Sarah Bertrand 1 Guillaume Cerutti 2 Laure Tougne 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
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, Centre de coopération internationale en recherche agronomique pour le développement [CIRAD] : UMR51
Abstract : In this paper, we propose a botanical approach for tree species classification through automatic bark analysis. The proposed method is based on specific descriptors inspired by the characterization keys used by botanists, from visual bark texture criteria. The descriptors and the recognition system are developed in order to run on a mobile device, without any network access. Our obtained results show a similar rate when compared to the state of the art in tree species identification from bark images with a small feature vector. Furthermore, we also demonstrate that the consideration of the bark identification significantly improves the performance of tree classification based on leaf only
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Communication dans un congrès
VISAPP 2017 - 12th International Conference on Computer Vision Theory and Applications, Feb 2017, Porto, Portugal
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https://hal.archives-ouvertes.fr/hal-01486591
Contributeur : Sarah Bertrand <>
Soumis le : vendredi 10 mars 2017 - 10:33:36
Dernière modification le : vendredi 16 juin 2017 - 01:09:42

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  • HAL Id : hal-01486591, version 1

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Sarah Bertrand, Guillaume Cerutti, Laure Tougne. Bark Recognition to Improve Leaf-based Classification in Didactic Tree Species Identification. VISAPP 2017 - 12th International Conference on Computer Vision Theory and Applications, Feb 2017, Porto, Portugal. <hal-01486591>

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