Ancient coins' surface inspection with web-based neural RTI visualization

Leonardo Righetto; Enrico Gobbetti; Federico Ponchio; Arianna Traviglia; Michela De Bernardin; Andrea Giachetti

doi:10.1117/12.2674888

9 August 2023 Ancient coins' surface inspection with web-based neural RTI visualization

Leonardo Righetto, Enrico Gobbetti, Federico Ponchio, Arianna Traviglia, Michela De Bernardin, Andrea Giachetti

Proceedings Volume 12620, Optics for Arts, Architecture, and Archaeology (O3A) IX; 126200D (2023) https://doi.org/10.1117/12.2674888
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

The use of neural encodings has the potential to replace the commonly used polynomial fitting in the analysis of artwork surface based on Reflectance Transformation Imaging (RTI), as it has proved to result in more compact encoding with better relight quality, but it is still not widely used due to the lack of efficient implementations available to practitioners. In this work, we describe an optimized system to encode/decode neural relightable images providing interactive visualization in a web interface allowing multi-layer visualization and annotation. To develop it, we performed several experiments testing different decoder architectures and input processing pipelines, evaluating the quality of the results on specific benchmarks to find the optimal tradeoff between relighting quality and efficiency. A specific decoder has been then implemented for the web and integrated into an advanced visualisation tool. The system has been tested for the analysis of a group of ancient Roman bronze coins that present scarce readability and varying levels of preservation and that have been acquired with a multispectral light dome. Their level of corrosion and degradation, which in some cases hinders the recognition of the images, numerals, or text represented on them, makes the system testing particularly challenging and complex. Testing on such a real case scenario, however, enables us to determine the actual improvement that this new RTI visualization tool can offer to numismatists in their ability to identify the coins.

Citation Download Citation

Leonardo Righetto, Enrico Gobbetti, Federico Ponchio, Arianna Traviglia, Michela De Bernardin, and Andrea Giachetti "Ancient coins' surface inspection with web-based neural RTI visualization", Proc. SPIE 12620, Optics for Arts, Architecture, and Archaeology (O3A) IX, 126200D (9 August 2023); https://doi.org/10.1117/12.2674888

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KEYWORDS

Visualization

RGB color model

Education and training

Cultural heritage

Inspection

Reflectivity

Ultraviolet radiation

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