Semantic Segmentation for Corrosion Detection in Archaeological Artefacts before Restoration-Reference-Cited by-同舟云学术

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Semantic Segmentation for Corrosion Detection in Archaeological Artefacts before Restoration

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)
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Author:

Stoean Ruxandra¹,Bacanin Nebojsa²,Ionescu Leonard³,Boicea Marinela⁴,Garau Alina-Maria³,Ghitescu Cristina-Camelia³

Affiliation:

1. University of Craiova,Faculty of Sciences,Department of Computer Science,Romania

2. Singidunum University,Faculty of Informatics and Computing,Belgrade,Serbia

3. Romanian Institute of Science and Technology,Artificial Intelligence and Machine Learning Team,Cluj-Napoca,Romania

4. Restoration and Conservation Lab, Oltenia Museum, History and Archaeology Section,Craiova,Romania

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9700161/9700170/09700245.pdf?arnumber=9700245

Reference27 articles.

1. Surface study of inhibitor films formed by polyvinyl alcohol and silver nanoparticles on stainless steel in hydrochloric acid solution using convolutional neural networks

2. Investigation of Polymer Coatings Formed by Polyvinyl Alcohol and Silver Nanoparticles on Copper Surface in Acid Medium by Means of Deep Convolutional Neural Networks

3. Convolutional Neural Network Learning Versus Traditional Segmentation for the Approximation of the Degree of Defective Surface in Titanium for Implantable Medical Devices

4. Fastai: A Layered API for Deep Learning

5. Deep residual learning for image recognition;he;CoRR,2015

Cited by 2 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. HEU-Net: hybrid attention residual block-based network with external skip connections for metal corrosion semantic segmentation;The Visual Computer;2023-06-22

2. On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs;2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC);2022-09

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