A research on the possibility of restoring cultural assets of artificial intelligence through the application of artificial neural networks to roof tile(Wadang) (기와의 와당에 인공신경망 적용을 통한 인공지능의 문화재 복구 가능성에 대한 연구)

JunOh Kim (김준오); Byong-Kwon Lee (이병권)

doi:10.9708/jksci.2021.26.01.019

A research on the possibility of restoring cultural assets of artificial intelligence through the application of artificial neural networks to roof tile(Wadang)

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(1), pp.19-26
DOI : 10.9708/jksci.2021.26.01.019
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : November 26, 2020
Accepted : January 7, 2021
Published : January 29, 2021

JunOh Kim ¹, Byong-Kwon Lee ²

¹동국대학교
²서원대학교

Accredited

ABSTRACT

Cultural assets excavated in historical areas have their own characteristics based on the background of the times, and it can be seen that their patterns and characteristics change little by little according to the history and the flow of the spreading area. Cultural properties excavated in some areas represent the culture of the time and some maintain their intact appearance, but most of them are damaged/lost or divided into parts, and many experts are mobilized to research the composition and repair the damaged parts. The purpose of this research is to learn patterns and characteristics of the past through artificial intelligence neural networks for such restoration research, and to restore the lost parts of the excavated cultural assets based on Generative Adversarial Network(GAN)[1]. The research is a process in which the rest of the damaged/lost parts are restored based on some of the cultural assets excavated based on the GAN. To recover some parts of dammed of cultural asset, through training with the 2D image of a complete cultural asset. This research is focused on how much recovered not only damaged parts but also reproduce colors and materials. Finally, through adopted this trained neural network to real damaged cultural, confirmed area of recovered area and limitation.

KEYWORDS

GAN, Machine learning, Cultural properties, Restoration, Auto-encoder

Citation status

* References for papers published after 2023 are currently being built.

[confproc] Ian Goodfellow / 2014 / Generative Adversarial Nets / Advances in Neural Information Processing Systems 27

[book] Kang Woo-bang / 2003 / Top, (Rediscovery of Korean Beauty 5) / Sol

[book] / Encyclopedia of Korean Folk Culture

[confproc] Alex Krizhevsky / 2012 / ImageNet Classification with Deep Convolutional Neural Networks / Part of Advances in Neural Information Processing Systems 25

[journal] C Xing / 2016 / Stacked denoise autoencoder based feature extraction and classification for hyperspectral images / Journal of Sensors

[journal] Jyostna Devi Bodapati / 2019 / Feature Extraction and Classification Using Deep Convolutional Neural Networks / Journal of Cyber Security and Mobility 8(2) : 261~276

[confproc] Zhang, Wei / 1988 / Shift-invariant pattern recognition neural network and its optical architecture / Proceedings of Annual Conference of the Japan Society of Applied Physics

[journal] Pascal Vincent / 2010 / Stacked Denoising Autoencoders:Learning Useful Representations in a Deep Network with a Local Denoising Criterion / The Journal of Machine Learning Research

[other] / MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges

[confproc] Jiahong Ouyang / 2018 / Task-GAN for Improved GAN based Image Restoration / ICLR 2019 Conference Blind Submission

[other] David Berthelot / 2017 / BEGAN:Boundary Equilibrium Generative Adversarial Networks / Machine Learning, 1703.10717

[other] Tero Karras / 2017 / Progressive Growing of GANs for Improved Quality, Stability, and Variation, Neural and Evolutionary Computing (cs.NE) / Machine Learning, 1710.10196

[other] Tero Karras / 2018 / A Style-Based Generator Architecture for Generative Adversarial Networks, Neural and Evolutionary Computing / Machine Learning, 1812.04948

[other] Yuzhen Lu / 2016 / Food Image Recognition by Using Convolutional Neural Networks (CNNs) / Computer Vision and Pattern Recognition (cs.CV), 1612.00983

[journal] Ruben Villegas / 2018 / Decomposing Motion and Content for Natural Video Sequence Prediction / Computer Vision and Pattern Recognition

[confproc] Tero Karras / 2020 / Analyzing and improving the image quality of stylegan / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition : 8110~8119

This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

A research on the possibility of restoring cultural assets of artificial intelligence through the application of artificial neural networks to roof tile(Wadang)

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

A research on the possibility of restoring cultural assets of artificial intelligence through the application of artificial neural networks to roof tile(Wadang)

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.