Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.08826 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912069418221568 |
|---|---|
| author | Bombini, Alessandro Bofías, Fernando García-Avello Giambi, Francesca Ruberto, Chiara |
| author_facet | Bombini, Alessandro Bofías, Fernando García-Avello Giambi, Francesca Ruberto, Chiara |
| contents | In this contribution, we define (and test) a pipeline to perform virtual painting recolouring using raw data of X-Ray Fluorescence (XRF) analysis on pictorial artworks. To circumvent the small dataset size, we generate a synthetic dataset, starting from a database of XRF spectra; furthermore, to ensure a better generalisation capacity (and to tackle the issue of in-memory size and inference time), we define a Deep Variational Embedding network to embed the XRF spectra into a lower dimensional, K-Means friendly, metric space.
We thus train a set of models to assign coloured images to embedded XRF images. We report here the devised pipeline performances in terms of visual quality metrics, and we close on a discussion on the results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08826 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards virtual painting recolouring using Vision Transformer on X-Ray Fluorescence datacubes Bombini, Alessandro Bofías, Fernando García-Avello Giambi, Francesca Ruberto, Chiara Computer Vision and Pattern Recognition Machine Learning Applied Physics I.4.m; J.2 In this contribution, we define (and test) a pipeline to perform virtual painting recolouring using raw data of X-Ray Fluorescence (XRF) analysis on pictorial artworks. To circumvent the small dataset size, we generate a synthetic dataset, starting from a database of XRF spectra; furthermore, to ensure a better generalisation capacity (and to tackle the issue of in-memory size and inference time), we define a Deep Variational Embedding network to embed the XRF spectra into a lower dimensional, K-Means friendly, metric space. We thus train a set of models to assign coloured images to embedded XRF images. We report here the devised pipeline performances in terms of visual quality metrics, and we close on a discussion on the results. |
| title | Towards virtual painting recolouring using Vision Transformer on X-Ray Fluorescence datacubes |
| topic | Computer Vision and Pattern Recognition Machine Learning Applied Physics I.4.m; J.2 |
| url | https://arxiv.org/abs/2410.08826 |