Computational framework for the evaluation of the composition and degradation state of metal heritage assets by deep learning-Reference-Cited by-同舟云学术

Computational framework for the evaluation of the composition and degradation state of metal heritage assets by deep learning

Published:2023-11 Issue: Volume:64 Page:198-206
ISSN:1296-2074
Container-title:Journal of Cultural Heritage
language:en
Short-container-title:Journal of Cultural Heritage

Author:

Stoean Ruxandra^ORCID,Bacanin Nebojsa^ORCID,Stoean Catalin^ORCID,Ionescu Leonard,Atencia Miguel^ORCID,Joya Gonzalo

Funder

Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii

Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare

Ontario Ministry of Research, Innovation and Science

Publisher

Elsevier BV

Subject

Chemistry (miscellaneous),Conservation,Archeology,Spectroscopy,Materials Science (miscellaneous),General Economics, Econometrics and Finance

Reference20 articles.

1. Archaeometallurgical investigation of ancient artefacts’ degradation phenomenon;Dwivedi;npj Mater. Degrad.,2019

2. Spectroscopy investigations reveal unprecedented details in the corrosion of AISI 1012 UPN profiles installed in a modernist building of beginning of 20th century;Iribarren;J. Cult. Herit.,2020

3. The recovery, restoration and conservation of a “spatha” found in Capidava Village, Constanta County, Romania;Ionescu;Studia Univ. Cibiniensis. Ser. Hist.,2019

4. Restoration of a roman statuary group, representing the god jupiter;Ionescu;Studia Univ. Cibiniensis. Ser. Hist.,2019

5. The VIA annotation software for images, audio and video;Dutta,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automatic Control of Class Weights in the Semantic Segmentation of Corrosion Compounds on Archaeological Artefacts;Advances in Computational Intelligence;2023