ir. Laurens Meeus

Conference Paper
open access

Translations and Open Science - Exploring how translation technologies can support multilingualism in scholarly communication

Susanna Fiorini, Arda Tezcan (UGent) , Tom Vanallemeersch, Sara Szoc, Kristin Migdisi, Laurens Meeus (UGent) and Lieve Macken (UGent)

(2023) In INTERNATIONAL CONFERENCE Human-informed Translation and Interpreting Technology (HiT-IT 2023) p.41-51

Add to list

Conference Paper
P1
open access

Assisting classical paintings restoration : efﬁcient paint loss detection and descriptor-based inpainting using shared pretraining

Laurens Meeus (UGent) , Shaoguang Huang (UGent) , Nina Žižakić (UGent) , Xianghui Xie, Bart Devolder, Hélène Dubois (UGent) , Maximiliaan Martens (UGent) and Aleksandra Pizurica (UGent)

(2021) Optics, Photonics and Digital Technologies for Imaging Applications VI. In Proceedings of SPIE 11353.

Add to list

Conference Paper
P1
open access

A two-stream neural network architecture for the detection and analysis of cracks in panel paintings

Roman Sizyakin (UGent) , Bruno Cornelis, Laurens Meeus (UGent) , Viacheslav Voronin and Aleksandra Pizurica (UGent)

(2021) Optics, Photonics and Digital Technologies for Imaging Applications VI. In Proceedings of SPIE 11353.

Add to list

Journal Article
A1
open access

Crack detection in paintings using convolutional neural networks

Roman Sizyakin (UGent) , Bruno Cornelis, Laurens Meeus (UGent) , Hélène Dubois (UGent) , Maximiliaan Martens (UGent) , Viacheslav Voronin and Aleksandra Pizurica (UGent)

(2020) IEEE ACCESS. 8(1). p.74535-74552

Add to list

Conference Paper
C1
open access

Autoencoder-learned local image descriptor for image inpainting

Nina Žižakić (UGent) , Izumi Ito, Laurens Meeus (UGent) and Aleksandra Pizurica (UGent)

(2019) Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019). In CEUR workshop proceedings 2491.

Add to list

Conference Paper
C1
open access

Partial convolution based multimodal autoencoder for art investigation

Xianghui Xie, Laurens Meeus (UGent) and Aleksandra Pizurica (UGent)

(2019) Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019). In CEUR workshop proceedings 2491.

Add to list

Deep learning for paint loss detection with a multiscale, translation invariant network

Laurens Meeus (UGent) , Shaoguang Huang (UGent) , Bart Devolder, Hélène Dubois (UGent) and Aleksandra Pizurica (UGent)

(2019) Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019). p.158-162

Add to list

Conference Paper
C1
open access

A deep learning approach to crack detection in panel paintings

Roman Sizyakin, Bruno Cornelis, Laurens Meeus (UGent) , Maximiliaan Martens (UGent) , Viacheslav Voronin and Aleksandra Pizurica (UGent)

(2018) Image Processing for Art Investigation (IP4AI). p.40-42

Add to list

Conference Paper
C3
open access

Deep Learning for Paint Loss Detection: A Case Study on the Ghent Altarpiece

Laurens Meeus (UGent) , Shaoguang Huang (UGent) , Bart Devolder, Maximiliaan Martens (UGent) and Aleksandra Pizurica (UGent)

(2018) Image Processing for Art Investigation (IP4AI). p.30-32

Add to list

Conference Paper
C1
open access

Paint loss detection via kernel sparse representation

Shaoguang Huang (UGent) , Laurens Meeus (UGent) , Bruno Cornelis, Bart Devolder, Maximiliaan Martens (UGent) and Aleksandra Pizurica (UGent)

(2018) Image Processing for Art Investigation (IP4AI) : proceedings. p.24-26

Academic Bibliography

ir. Laurens Meeus

Translations and Open Science - Exploring how translation technologies can support multilingualism in scholarly communication

Assisting classical paintings restoration : efﬁcient paint loss detection and descriptor-based inpainting using shared pretraining

A two-stream neural network architecture for the detection and analysis of cracks in panel paintings

Crack detection in paintings using convolutional neural networks

Autoencoder-learned local image descriptor for image inpainting

Partial convolution based multimodal autoencoder for art investigation

Deep learning for paint loss detection with a multiscale, translation invariant network

A deep learning approach to crack detection in panel paintings

Deep Learning for Paint Loss Detection: A Case Study on the Ghent Altarpiece

Paint loss detection via kernel sparse representation

Contents

Support

Contact