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Conference Papers Year : 2017

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

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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Dates and versions

hal-01486591 , version 1 (10-03-2017)

Identifiers

  • HAL Id : hal-01486591 , version 1

Cite

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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