{"title":"A Brief Analysis of the Iterative Next Boundary Detection Network for Tree Rings Delineation in Images of Pinus taeda","authors":"Henry Marichal, Gregory Randall","doi":"arxiv-2408.14343","DOIUrl":null,"url":null,"abstract":"This work presents the INBD network proposed by Gillert et al. in CVPR-2023\nand studies its application for delineating tree rings in RGB images of Pinus\ntaeda cross sections captured by a smartphone (UruDendro dataset), which are\nimages with different characteristics from the ones used to train the method.\nThe INBD network operates in two stages: first, it segments the background,\npith, and ring boundaries. In the second stage, the image is transformed into\npolar coordinates, and ring boundaries are iteratively segmented from the pith\nto the bark. Both stages are based on the U-Net architecture. The method\nachieves an F-Score of 77.5, a mAR of 0.540, and an ARAND of 0.205 on the\nevaluation set. The code for the experiments is available at\nhttps://github.com/hmarichal93/mlbrief_inbd.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Quantitative Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.14343","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This work presents the INBD network proposed by Gillert et al. in CVPR-2023
and studies its application for delineating tree rings in RGB images of Pinus
taeda cross sections captured by a smartphone (UruDendro dataset), which are
images with different characteristics from the ones used to train the method.
The INBD network operates in two stages: first, it segments the background,
pith, and ring boundaries. In the second stage, the image is transformed into
polar coordinates, and ring boundaries are iteratively segmented from the pith
to the bark. Both stages are based on the U-Net architecture. The method
achieves an F-Score of 77.5, a mAR of 0.540, and an ARAND of 0.205 on the
evaluation set. The code for the experiments is available at
https://github.com/hmarichal93/mlbrief_inbd.