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Learning Algorithms for Digital Reconstruction of Van Gogh’s Drawings. / Zeng, Yuan; Tang, Jiexiong; van der Lubbe, Jan; Loog, Marco.

EuroMed 2016 - 6th International Conference - Proceedings: Digital Heritage - Progress in Cultural Heritage: Documentation, Preservation, and Protection. ed. / Marinos Ioannides; Eleanor Vink; Antonia Moropoulou; Monika Hagedorn-Saupe; Antonella Fresa; Gunnar Liestøl; Vlatka Rajcic; Pierre Grussenmeyer. Vol. 1 Cham : Springer, 2016. p. 322-333 (Lecture Notes in Computer Science; Vol. 10058).

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Zeng, Y, Tang, J, van der Lubbe, J & Loog, M 2016, Learning Algorithms for Digital Reconstruction of Van Gogh’s Drawings. in M Ioannides, E Vink, A Moropoulou, M Hagedorn-Saupe, A Fresa, G Liestøl, V Rajcic & P Grussenmeyer (eds), EuroMed 2016 - 6th International Conference - Proceedings: Digital Heritage - Progress in Cultural Heritage: Documentation, Preservation, and Protection. vol. 1, Lecture Notes in Computer Science, vol. 10058, Springer, Cham, pp. 322-333, EuroMed 2016, Nicosia, Cyprus, 31/10/16. https://doi.org/10.1007/978-3-319-48496-9_26

APA

Zeng, Y., Tang, J., van der Lubbe, J., & Loog, M. (2016). Learning Algorithms for Digital Reconstruction of Van Gogh’s Drawings. In M. Ioannides, E. Vink, A. Moropoulou, M. Hagedorn-Saupe, A. Fresa, G. Liestøl, V. Rajcic, & P. Grussenmeyer (Eds.), EuroMed 2016 - 6th International Conference - Proceedings: Digital Heritage - Progress in Cultural Heritage: Documentation, Preservation, and Protection (Vol. 1, pp. 322-333). (Lecture Notes in Computer Science; Vol. 10058). Springer. https://doi.org/10.1007/978-3-319-48496-9_26

Vancouver

Zeng Y, Tang J, van der Lubbe J, Loog M. Learning Algorithms for Digital Reconstruction of Van Gogh’s Drawings. In Ioannides M, Vink E, Moropoulou A, Hagedorn-Saupe M, Fresa A, Liestøl G, Rajcic V, Grussenmeyer P, editors, EuroMed 2016 - 6th International Conference - Proceedings: Digital Heritage - Progress in Cultural Heritage: Documentation, Preservation, and Protection. Vol. 1. Cham: Springer. 2016. p. 322-333. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-48496-9_26

Author

Zeng, Yuan ; Tang, Jiexiong ; van der Lubbe, Jan ; Loog, Marco. / Learning Algorithms for Digital Reconstruction of Van Gogh’s Drawings. EuroMed 2016 - 6th International Conference - Proceedings: Digital Heritage - Progress in Cultural Heritage: Documentation, Preservation, and Protection. editor / Marinos Ioannides ; Eleanor Vink ; Antonia Moropoulou ; Monika Hagedorn-Saupe ; Antonella Fresa ; Gunnar Liestøl ; Vlatka Rajcic ; Pierre Grussenmeyer. Vol. 1 Cham : Springer, 2016. pp. 322-333 (Lecture Notes in Computer Science).

BibTeX

@inproceedings{2c0ced51d2384f0a830b6f50823cc85b,
title = "Learning Algorithms for Digital Reconstruction of Van Gogh{\textquoteright}s Drawings",
abstract = "Many works of Van Gogh{\textquoteright}s oeuvre, such as letters, drawings and paintings, have been severely degraded due to light exposure. Digital reconstruction of faded color can help to envisage how the artist{\textquoteright}s work may have looked at the time of creation. In this paper, we study the reconstruction of Vincent van Gogh{\textquoteright}s drawings by means of learning schemes and on the basis of the available reproductions of these drawings. In particular, we investigate the use of three machine learning algorithms, k-nearest neighbor (kNN) estimation, linear regression (LR), and convolutional neural networks (CNN), for learning the reconstruction of these faded drawings. Experimental results show that the reconstruction performance of the kNN method is slightly better than those of the CNN. The reconstruction performance of the LR is much worse than those of the kNN and the CNN.",
keywords = "Van Gogh{\textquoteright}s drawing, Drawing reconstruction, Reproduction, Machine learning",
author = "Yuan Zeng and Jiexiong Tang and {van der Lubbe}, Jan and Marco Loog",
year = "2016",
doi = "10.1007/978-3-319-48496-9_26",
language = "English",
isbn = "978-3-319-48495-2",
volume = "1",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "322--333",
editor = "Marinos Ioannides and Eleanor Vink and Antonia Moropoulou and Monika Hagedorn-Saupe and Antonella Fresa and Gunnar Liest{\o}l and Vlatka Rajcic and Pierre Grussenmeyer",
booktitle = "EuroMed 2016 - 6th International Conference - Proceedings",
note = "EuroMed 2016 : International Conference on Digital Heritage ; Conference date: 31-10-2016 Through 05-11-2016",

}

RIS

TY - GEN

T1 - Learning Algorithms for Digital Reconstruction of Van Gogh’s Drawings

AU - Zeng, Yuan

AU - Tang, Jiexiong

AU - van der Lubbe, Jan

AU - Loog, Marco

PY - 2016

Y1 - 2016

N2 - Many works of Van Gogh’s oeuvre, such as letters, drawings and paintings, have been severely degraded due to light exposure. Digital reconstruction of faded color can help to envisage how the artist’s work may have looked at the time of creation. In this paper, we study the reconstruction of Vincent van Gogh’s drawings by means of learning schemes and on the basis of the available reproductions of these drawings. In particular, we investigate the use of three machine learning algorithms, k-nearest neighbor (kNN) estimation, linear regression (LR), and convolutional neural networks (CNN), for learning the reconstruction of these faded drawings. Experimental results show that the reconstruction performance of the kNN method is slightly better than those of the CNN. The reconstruction performance of the LR is much worse than those of the kNN and the CNN.

AB - Many works of Van Gogh’s oeuvre, such as letters, drawings and paintings, have been severely degraded due to light exposure. Digital reconstruction of faded color can help to envisage how the artist’s work may have looked at the time of creation. In this paper, we study the reconstruction of Vincent van Gogh’s drawings by means of learning schemes and on the basis of the available reproductions of these drawings. In particular, we investigate the use of three machine learning algorithms, k-nearest neighbor (kNN) estimation, linear regression (LR), and convolutional neural networks (CNN), for learning the reconstruction of these faded drawings. Experimental results show that the reconstruction performance of the kNN method is slightly better than those of the CNN. The reconstruction performance of the LR is much worse than those of the kNN and the CNN.

KW - Van Gogh’s drawing

KW - Drawing reconstruction

KW - Reproduction

KW - Machine learning

U2 - 10.1007/978-3-319-48496-9_26

DO - 10.1007/978-3-319-48496-9_26

M3 - Conference contribution

SN - 978-3-319-48495-2

VL - 1

T3 - Lecture Notes in Computer Science

SP - 322

EP - 333

BT - EuroMed 2016 - 6th International Conference - Proceedings

A2 - Ioannides, Marinos

A2 - Vink, Eleanor

A2 - Moropoulou, Antonia

A2 - Hagedorn-Saupe, Monika

A2 - Fresa, Antonella

A2 - Liestøl, Gunnar

A2 - Rajcic, Vlatka

A2 - Grussenmeyer, Pierre

PB - Springer

CY - Cham

T2 - EuroMed 2016

Y2 - 31 October 2016 through 5 November 2016

ER -

ID: 11755041