Convolutional neural network-based pure paint pigment identification using hyperspectral images

Chen, Ailin; Jesus, Rui; Vilarigues

http://hdl.handle.net/10400.21/14157

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Authors

Chen, Ailin

Jesus, Rui

Vilarigues

Abstract(s)

This research presents the results of the implementation of deep learning neural networks in the identification of pure pigments of heritage artwork, namely paintings. Our paper applies an inno vative three-branch deep learning model to maximise the correct identification of pure pigments. The model proposed combines the feature maps obtained from hyperspectral images through multi ple convolutional neural networks, and numerical, hyperspectral metric data with respect to a set of reference reflectances. The results obtained exhibit an accurate representation of the pure pre dicted pigments which are confirmed through the use of analytical techniques. The model presented outperformed the compared coun terparts and is deemed to be an important direction, not only in terms of utilisation of hyperspectral data and concrete pigment data in heritage analysis, but also in the application of deep learning in other fields.

Keywords

Hyperspectral imaging Deep learning Convolutional neural networks Visualisation Pigment identification

URI

http://hdl.handle.net/10400.21/14157

Citation

CHEN, Ailin; JESUS, Rui; VILARIGUES, Marcia – Convolutional neural network-based pure paint pigment identification using hyperspectral images. In MMAsia '21: ACM Multimedia Asia. Gold Coast, Australia: Association for Computing Machinery, 2021. Pp. 1-7.