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Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification

Abstract : With the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia.
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Contributor : Guillaume Cerutti <>
Submitted on : Monday, October 14, 2013 - 3:23:06 PM
Last modification on : Tuesday, November 19, 2019 - 2:37:07 AM
Document(s) archivé(s) le : Friday, April 7, 2017 - 10:47:23 AM


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Guillaume Cerutti, Laure Tougne, Julien Mille, Antoine Vacavant, Didier Coquin. Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification. Computer Vision and Image Understanding, Elsevier, 2013, 117 (10), pp.1482-1501. ⟨10.1016/j.cviu.2013.07.003⟩. ⟨hal-00872878⟩



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