| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.76 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
The application of our research is in the art world where the scarcity of available analytical data from a particular artist or physical access for its acquisition is restricted. This poses a fundamental problem for the purpose of conservation, restoration or authentication of historical artworks. We address part of this problem by providing a practical method to generate hyperspectral data from readily available RGB imagery of artwork by means of a two-step process using deep neural networks. The particularities of our approach include the generation of learnable colour mixtures and reflectances from a reduced collection of prior data for the mapping and reconstruction of hyperspectral features on new images. Further analysis and correction of the prediction are achieved by a second network that reduces the error by producing results akin to those obtained by a hyperspectral camera. Our method has been used to study a collection of paintings by Amadeo de Souza-Cardoso where successful results were obtained. CCS CONCEPTS • Computing methodologies → Neural networks; Artificial intelligence; • Applied computing → Arts and humanities.
Descrição
Palavras-chave
Neural networks Hyperspectral imaging Image visualisation Colour analysis
Contexto Educativo
Citação
CHEN, Ailin; JESUS, Rui; VILARIGUES, Márcia – Hyperspectral image reconstruction of heritage artwork using RGB images and deep neural networks. In CBMI '22: Proceedings of the 19th International Conference on Content-based Multimedia Indexing. Graz, Austria: Association for Computing Machinary, 2022. ISBN 978-1-4503-9720-9. Pp. 97-102.
Editora
Association for Computing Machinary