Hyperspectral imaging combined with data classification techniques as an aid for artwork authentication
Bibliographic Data
| ID | 4624697 |
|---|---|
| Authors | Adam Polak (0000-0001-6550-7716, University of Strathclyde, corresponding author), Timothy Kelman (University of Strathclyde), Paul Murray (0000-0002-6980-9276, University of Strathclyde), Stephen Marshall (0000-0003-1404-9254, University of Strathclyde), David J M Stothard (Fraunhofer UK Research), Nicholas Eastaugh, Francis Eastaugh |
| Year | 2017 |
| Volume | 26 |
| Pages | 1-11 |
| Publication date | 2017-07-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Cultural Heritage (JOURNAL) |
| Journal identifiers | ISSN: 1296-2074 • E-ISSN: 1778-3674 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.culher.2017.01.013 |
| OpenAlex | W2591916814 |
| Language | EN |
| Citations received | 15 |
| References cited | 23 |
In recent years various scientific practices have been adapted to the artwork analysis process. Although a set of techniques is available for art historians and scientists, there is a constant need for rapid and non-destructive methods to empower the art authentication process. In this paper hyperspectral imaging combined with signal processing and classification techniques are proposed as a tool to enhance the process for identification of art forgeries. Using bespoke paintings designed for this work, a spectral library of selected pigments was established and the viability of training and the application of classification techniques based on this data was demonstrated. Using these techniques for the analysis of actual forged paintings resulted in the identification of anachronistic paint, confirming the falsity of the artwork. This paper demonstrates the applicability of infrared (IR) hyperspectral imaging for artwork authentication
Art · Bespoke · Computer vision · Hyperspectral imaging · Multispectral image · Painting · Visual arts · Computer Science · Cultural Heritage Materials Analysis · Currency Recognition and Detection · Spectroscopy and Chemometric Analyses · Artificial Intelligence
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| Unique citing works | 15 |
|---|---|
| Citations per year | 1,88 |
| Citation span | 2018 - 2025 (8) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 14 |