This work provides novel approaches to knot diameter and DKB coordinate regression in wood logs by exploiting Convolutional Neural Networks (CNNs). One of the proposed procedures, concerning the subsequent application of a CNN for status classification to CT voxel sub-blocks containing consecutive portions of each knot, yields promising results and is computationally fast enough to be employed in in-line applications, both in terms of diameter regression and status estimation.

Convolutional Neural Networks for Knot Measurement in Tomographic Images of Wood Logs

Giovannini, Stefano
2019/2020

Abstract

This work provides novel approaches to knot diameter and DKB coordinate regression in wood logs by exploiting Convolutional Neural Networks (CNNs). One of the proposed procedures, concerning the subsequent application of a CNN for status classification to CT voxel sub-blocks containing consecutive portions of each knot, yields promising results and is computationally fast enough to be employed in in-line applications, both in terms of diameter regression and status estimation.
2019-10-15
knots, wood, CT, tomography, CNN, dead knot border, diameter
File in questo prodotto:
File Dimensione Formato  
stefano_giovannini_tesi.pdf

accesso aperto

Dimensione 2.9 MB
Formato Adobe PDF
2.9 MB Adobe PDF Visualizza/Apri

The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/24613