{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:17:30Z","timestamp":1761175050202,"version":"build-2065373602"},"reference-count":15,"publisher":"Polish Information Processing Society","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.15439\/2025f7486","type":"proceedings-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:44:23Z","timestamp":1761119063000},"page":"327-332","source":"Crossref","is-referenced-by-count":0,"title":["AI classifier of defects in Artworks captured by active infrared thermography"],"prefix":"10.15439","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1921-2340","authenticated-orcid":true,"given":"Peter","family":"Mal\u00edk","sequence":"first","affiliation":[{"name":"Institute of Informatics, Slovak Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Orlej","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Information Technologies, Slovak University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Branislav","family":"Faj\u010d\u00e1k","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Information Technologies, Slovak University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1993-4589","authenticated-orcid":true,"given":"Massimo","family":"Rippa","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems \u201cEduardo Caianiello\u201d, National Research Council"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"6175","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"1] Rippa, M., Vigorito, M.R., Russo, M.R. et al. Active Thermogra-\nphy for Non-invasive Inspection of Wall Painting: Novel Approach\nBased on Thermal Recovery Maps. J Nondestruct Eval 42, 63 (2023).\nhttps:\/\/doi.org\/10.1007\/s10921-023-00972-8","DOI":"10.1007\/s10921-023-00972-8"},{"key":"ref2","doi-asserted-by":"publisher","unstructured":"Rippa, M., Pagliarulo, V., Lanzillo, A. et al. Active Thermography for\nNon-invasive Inspection of an Artwork on Poplar Panel: Novel Approach\nUsing Principal Component Thermography and Absolute Thermal Contrast. J Nondestruct Eval 40, 21 (2021). https:\/\/doi.org\/10.1007\/s10921-021-00755-z","DOI":"10.1007\/s10921-021-00755-z"},{"key":"ref3","unstructured":"Rippa, M., Casciello, M., Mormile, P., Balbi, B., Vitulli, S., and Vigorito,\nM. R. (2023). Infrared Imaging Analysis for in-situ inspection of a\nwork of art: combined SWIR-MWIR methods for a painting on wood\ninvestigation. https:\/\/www.ndt.net\/article\/art2023\/papers\/art2023_p67.pdf"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"C. Sun, A. Shrivastava, S. Singh and A. Gupta, \"Revisiting\nUnreasonable Effectiveness of Data in Deep Learning Era,\" in\n2017 IEEE International Conference on Computer Vision (ICCV),\nVenice, Italy, 2017, pp. 843-852, https:\/\/dx.doi.org\/10.1109\/ICCV.2017.97. url:\nhttps:\/\/doi.ieeecomputersociety.org\/10.1109\/ICCV.2017.97","DOI":"10.1109\/ICCV.2017.97"},{"key":"ref5","unstructured":"Hestness, J., Narang, S., Ardalani, N., Diamos, G.F., Jun, H., Kianinejad,\nH., Patwary, M.M., Yang, Y., and Zhou, Y. (2017). Deep Learning Scaling\nis Predictable, Empirically. ArXiv, abs\/1712.00409."},{"key":"ref6","doi-asserted-by":"publisher","unstructured":"Mumuni, A. and Mumuni, F. (2022). Data augmentation: a\ncomprehensive survey of modern approaches. Array, 16, 100258.\nhttps:\/\/doi.org\/10.1016\/j.array.2022.100258","DOI":"10.1016\/j.array.2022.100258"},{"key":"ref7","unstructured":"Maas, Andrew L., Hannun, Awni Y. and Ng, Andrew Y. Rectifier Nonlinearities Improve Neural Network Acoustic Models. In: Proceedings of the\n30th International Conference on Machine Learning (ICML). 2013, pp.\n2\u20133. https:\/\/ai.stanford.edu\/~amaas\/papers\/relu_hybrid_icml2013_final.pdf"},{"key":"ref8","doi-asserted-by":"publisher","unstructured":"Mezina, A., Burget, R. and Kotrly, M. A deep learning approach for\nanomaly detection in X-ray images of paintings. npj Herit. Sci. 13, 127\n(2025). https:\/\/doi.org\/10.1038\/s40494-025-01724-9","DOI":"10.1038\/s40494-025-01724-9"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"Z. Zhang et al., \"Reducing Bias in AI-Based Analysis of Visual Artworks,\" in IEEE BITS the Information Theory Magazine, vol. 2, no. 1,\npp. 36-48, 1 Oct. 2022, https:\/\/dx.doi.org\/10.1109\/MBITS.2022.3197102. keywords:\nPainting;Art;Image color analysis;Cultural differences;Feature extraction;Visualization;Imaging;Machine learning;Best practices;Systematics,","DOI":"10.1109\/MBITS.2022.3197102"},{"key":"ref10","unstructured":"Perera, Walpola Layantha; Messemer, Heike; Heinz, Matthias; Kretzschmar, Michael (2020): Detecting Treasures in Museums with Artificial\nIntelligence. Workshop Gemeinschaften in Neuen Medien (GeNeMe)\n2020. Dresden: TUDpress"},{"key":"ref11","unstructured":"Angheluta, L. M., and Chirosca, A. (2020). Physical degradation detection on artwork surface polychromies using deep learning models. Rom.\nRep. Phys, 72(3), 805."},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"Girbacia, F. (2024). An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage. Electronics, 13(18), 3738.\nhttps:\/\/doi.org\/10.3390\/electronics13183738","DOI":"10.3390\/electronics13183738"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Fontanella, F., Colace, F., Molinara, M., Di Freca, A. S., and Stanco,\nF. (2020). Pattern recognition and artificial intelligence techniques for\ncultural heritage. Pattern Recognition Letters, 138, 23-29.","DOI":"10.1016\/j.patrec.2020.06.018"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Li, J., 2021, April. Application of artificial intelligence in cultural\nheritage protection. In Journal of Physics: Conference Series (Vol. 1881,\nNo. 3, p. 032007). IOP Publishing.","DOI":"10.1088\/1742-6596\/1881\/3\/032007"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Mishra, M. and Louren\u00e7o, P.B., 2024. Artificial intelligence-assisted\nvisual inspection for cultural heritage: State-of-the-art review. Journal of\nCultural Heritage, 66, pp.536-550.","DOI":"10.1016\/j.culher.2024.01.005"}],"event":{"name":"20th Conference on Computer Science and Intelligence Systems (FedCSIS)","theme":"Computer Science and Intelligence Systems","location":"Krak\u00f3w, Poland","acronym":"FedCSIS","number":"20","start":{"date-parts":[[2025,9,14]]},"end":{"date-parts":[[2025,9,17]]}},"container-title":["Annals of Computer Science and Information Systems","Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)"],"original-title":[],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:47:20Z","timestamp":1761119240000},"score":1,"resource":{"primary":{"URL":"https:\/\/annals-csis.org\/Volume_43\/drp\/7486.html"}},"subtitle":[],"proceedings-subject":"Computer Science and Information Systems","short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":15,"URL":"https:\/\/doi.org\/10.15439\/2025f7486","relation":{},"ISSN":["2300-5963"],"issn-type":[{"value":"2300-5963","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}