[IPOL announce] new article: CNN-based Method for Segmenting Tree Bark Surface Singularites

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2022/369/

Florian Delconte, Phuc Ngo, Bertrand Kerautret, Isabelle 
Debled-Rennesson, Van-Tho Nguyen, and Thiery Constant,
CNN-based Method for Segmenting Tree Bark Surface Singularites,
Image Processing On Line, 12 (2022), pp. 1–26.
https://doi.org/10.5201/ipol.2022.369

Abstract
The analysis of trunk shape and, in particular, the geometric structures 
on the bark surface are of main interest for different applications 
linked to the wood industry or biological studies. Bark singularities 
are often external records of the history of the development of internal 
elements. The actors of the forest sector grade the trees by considering 
these singularities through standards. In this paper, we propose a 
method using terrestrial LiDAR data to automatically segment 
singularities on tree surfaces. It is based on the construction of a 
relief map combined with a convolutional neural network. The algorithms 
and the source code are available with an online demonstration allowing 
to test the defect detection without any software installation.