DelagrangeJauvinRochon2014

Référence

Delagrange, S., Jauvin, C., Rochon, P. (2014) PypeTree: A tool for reconstructing tree perennial tissues from point clouds. Sensors, 14(3):4271-4289. (Scopus )

Résumé

The reconstruction of trees from point clouds that were acquired with terrestrial LiDAR scanning (TLS) may become a significant breakthrough in the study and modelling of tree development. Here, we develop an efficient method and a tool based on extensive modifications to the skeletal extraction method that was first introduced by Verroust and Lazarus in 2000. PypeTree, a user-friendly and open-source visual modelling environment, incorporates a number of improvements into the original skeletal extraction technique, making it better adapted to tackle the challenge of tree perennial tissue reconstruction. Within PypeTree, we also introduce the idea of using semi-supervised adjustment tools to address methodological challenges that are associated with imperfect point cloud datasets and which further improve reconstruction accuracy. The performance of these automatic and semi-supervised approaches was tested with the help of synthetic models and subsequently validated on real trees. Accuracy of automatic reconstruction greatly varied in terms of axis detection because small (length < 3.5 cm) branches were difficult to detect. However, as small branches account for little in terms of total skeleton length, mean reconstruction error for cumulated skeleton length only reached 5.1% and 1.8% with automatic or semi-supervised reconstruction, respectively. In some cases, using the supervised tools, a perfect reconstruction of the perennial tissue could be achieved. © 2014 by the authors; licensee MDPI, Basel, Switzerland.

Format EndNote

Vous pouvez importer cette référence dans EndNote.

Format BibTeX-CSV

Vous pouvez importer cette référence en format BibTeX-CSV.

Format BibTeX

Vous pouvez copier l'entrée BibTeX de cette référence ci-bas, ou l'importer directement dans un logiciel tel que JabRef .

@ARTICLE { DelagrangeJauvinRochon2014,
    AUTHOR = { Delagrange, S. and Jauvin, C. and Rochon, P. },
    TITLE = { PypeTree: A tool for reconstructing tree perennial tissues from point clouds },
    JOURNAL = { Sensors },
    YEAR = { 2014 },
    VOLUME = { 14 },
    PAGES = { 4271-4289 },
    NUMBER = { 3 },
    ABSTRACT = { The reconstruction of trees from point clouds that were acquired with terrestrial LiDAR scanning (TLS) may become a significant breakthrough in the study and modelling of tree development. Here, we develop an efficient method and a tool based on extensive modifications to the skeletal extraction method that was first introduced by Verroust and Lazarus in 2000. PypeTree, a user-friendly and open-source visual modelling environment, incorporates a number of improvements into the original skeletal extraction technique, making it better adapted to tackle the challenge of tree perennial tissue reconstruction. Within PypeTree, we also introduce the idea of using semi-supervised adjustment tools to address methodological challenges that are associated with imperfect point cloud datasets and which further improve reconstruction accuracy. The performance of these automatic and semi-supervised approaches was tested with the help of synthetic models and subsequently validated on real trees. Accuracy of automatic reconstruction greatly varied in terms of axis detection because small (length < 3.5 cm) branches were difficult to detect. However, as small branches account for little in terms of total skeleton length, mean reconstruction error for cumulated skeleton length only reached 5.1% and 1.8% with automatic or semi-supervised reconstruction, respectively. In some cases, using the supervised tools, a perfect reconstruction of the perennial tissue could be achieved. © 2014 by the authors; licensee MDPI, Basel, Switzerland. },
    ADDRESS = { Institute of Temperate Forest Sciences (ISFORT), University of Quebec in Outaouais (UQO), 58 Rue Principale, Ripon, QC J0V1V0, Canada },
    COMMENT = { Export Date: 31 March 2014 Source: Scopus },
    KEYWORDS = { Botanical trees, Colonisation algorithm, L-System, Skeleton, Terrestrial LiDAR Scanning (TLS), Tree reconstruction, Validation procedure },
    OWNER = { Luc },
    TIMESTAMP = { 2014.03.31 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84896452876&partnerID=40&md5=8205e407a428199a5b51b443edc80162 },
}

********************************************************** *************************** FRQNT ************************ **********************************************************

Un regroupement stratégique du

********************************************************** ***************** Facebook Twitter *********************** **********************************************************

Abonnez-vous à
l'Infolettre du CEF!

********************************************************** ***************** Pub - ABC CBA 2020 ****************** **********************************************************

31 mai au 4 juin 2020

********************************************************** ***************** Pub - Symphonies_Boreales ****************** **********************************************************

********************************************************** ***************** Boîte à trucs *************** **********************************************************

CEF-Référence
La référence vedette !

Jérémie Alluard (2016) Les statistiques au moments de la rédaction 

  • Ce document a pour but de guider les étudiants à intégrer de manière appropriée une analyse statistique dans leur rapport de recherche.

Voir les autres...