Margariti, C., Lukesova, H. & Gomes, F. B. Advanced analytical techniques for heritage textiles. Herit. Sci. 12, 388 (2024).
Google Scholar
Yílmaz, S. & Nabiyev, V. V. Comprehensive survey of the solving puzzle problems. Comp. Sci. Rev. 50, 100586 (2023).
Google Scholar
Nedumpillil, N. N., Sankaran, A., Jose, S., George, S. & Thomas, S.Destructive Analytical Techniques for the Analysis of Historic Textiles, vol. 2, chap. 11, 215–234 (Wiley, 2022).
Margariti, C., Sava, G., Sava, T., Boudin, M. & Nosch, M.-L. Radiocarbon dating of archaeological textiles at different states of preservation. Herit. Sci. 11, 44 (2023).
Google Scholar
Magdy, M. Analytical techniques for the preservation of cultural heritage: Frontiers in knowledge and application. Crit. Rev. Anal. Chem. 52, 1171–1196 (2022).
Google Scholar
Pozzi, F. & Stephens, C. H. Advances in analytical methods for cultural heritage. Appl. Sci. 14, 7587 (2024).
Google Scholar
Gigilashvili, D., Lukesova, H. & Hardeberg, J. Y. Criteria for matching fragmented archaeological textiles: a survey. Archaeol. Text. Rev. 66, 64–75 (2024).
Gigilashvili, D. et al. Toward solving a puzzle of fragmented archeological textiles. J. Imaging Sci. Technol. 68, 1–16 (2024).
Google Scholar
Gorla, G., Nielsen, F., Montague, P. B., Kösegi, N. & Amigo, J. M. Challenges and spectra interpretability in textile sorting: NIR hyperspectral images and chemometrics. Spectrochimica Acta Part A: Mol. Biomolecular Spectrosc. 344, 126665 (2025).
Google Scholar
Dian, R., Li, S., Sun, B. & Guo, A. Recent advances and new guidelines on hyperspectral and multispectral image fusion. Inf. Fusion 69, 40–51 (2021).
Google Scholar
Frank, E. Lights, camera, archaeology: documenting archaeological textile impressions with reflectance transformation imaging (RTI). Text. Specialty Group Postprints 25, 11–42 (2015).
Mytum, H. & Peterson, J. R. The application of reflectance transformation imaging (RTI) in historical archaeology. Historical Archaeol. 52, 489–503 (2018).
Google Scholar
Ciortan, I. et al. A practical reflectance transformation imaging pipeline for surface characterization in cultural heritage. In Proceedings of the 14th Eurographics Workshop on Graphics and Cultural Heritage, GCH ’16, 127-136 (Eurographics Association, 2016).
Miles, J., Pitts, M., Pagi, H. & Earl, G. New applications of photogrammetry and reflectance transformation imaging to an Easter Island statue. Antiquity 88, 596–605 (2014).
Google Scholar
Siatou, A. et al. New methodological approaches in Reflectance Transformation Imaging applications for conservation documentation of cultural heritage metal objects. J. Cult. Herit. 58, 274–283 (2022).
Google Scholar
Ono, S., Matsuda, Y. & Mizuochi, T. Development of a multispectral RTI system to evaluate varnish cleaning. In Bridgeland, J. (ed.) ICOM-CC 18th Triennial Conference Preprints, Copenhagen, 4–8 September 2017, art. 0205 (International Council of Museums, 2017).
Min, J. et al. Reflectance transformation imaging for documenting changes through treatment of Joseon dynasty coins. Herit. Sci. 9, 105 (2021).
Google Scholar
Boute, R. et al. Revisiting Reflectance Transformation Imaging (RTI): a tool for monitoring and evaluating conservation treatments. In IOP conference series: materials science and engineering, vol. 364 (IOP, 2018).
Yahaghi, E., García, J. A. M., Movafeghi, A. & Mirzapour, M. Improving X-ray images of historically significant textiles. J. Cult. Herit. 66, 415–425 (2024).
Google Scholar
Morigi, M. & d’Errico, V. Application of X-ray computed tomography to cultural heritage diagnostics. Appl. Phys. A 100, 653–661 (2010).
Google Scholar
Lanteri, L. & Pelosi, C. 2D and 3D ultraviolet fluorescence applications on cultural heritage paintings and objects through a low-cost approach for diagnostics and documentation. In Optics for Arts, Architecture, and Archaeology VIII, vol. 11784, 156–165 (SPIE, 2021).
Picollo, M., Cucci, C., Casini, A. & Stefani, L. Hyper-spectral imaging technique in the cultural heritage field: new possible scenarios. Sensors 20, 2843 (2020).
Google Scholar
Fischer, C. & Kakoulli, I. Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications. Stud. Conserv. 51, 3–16 (2006).
Google Scholar
Daffara, C., Pampaloni, E., Pezzati, L., Barucci, M. & Fontana, R. Scanning multispectral IR Reflectography SMIRR: an advanced tool for art diagnostics. Acc. Chem. Res. 43, 847–856 (2010).
Google Scholar
Schreiner, M., Melcher, M. & Uhlir, K. Scanning electron microscopy and energy dispersive analysis: applications in the field of cultural heritage. Anal. Bioanal. Chem. 387, 737–747 (2007).
Google Scholar
Romani, A., Clementi, C., Miliani, C. & Favaro, G. Fluorescence spectroscopy: a powerful technique for the noninvasive characterization of artwork. Acc. Chem. Res. 43, 837–846 (2010).
Google Scholar
Jackson, J. B. et al. A Survey of terahertz applications in cultural heritage conservation science. IEEE Trans. Terahertz Sci. Technol. 1, 220–231 (2011).
Google Scholar
Malzbender, T., Gelb, D. & Wolters, H. Polynomial texture maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, 519–528 (CGIT, 2001).
Pitard, G. et al. Discrete modal decomposition: a new approach for the reflectance modeling and rendering of real surfaces. Mach. Vis. Appl. 28, 607–621 (2017).
Google Scholar
Gautron, P., Krivanek, J., Pattanaik, S. & Bouatouch, K. A novel hemispherical basis for accurate and efficient rendering. In Proceedings of the Fifteenth Eurographics Conference on Rendering Techniques, EGSR’04, 321-330 (Eurographics Association, 2004).
Dulecha, T. G., Fanni, F. A., Ponchio, F., Pellacini, F. & Giachetti, A. Neural reflectance transformation imaging. Vis. Comp. 36, 2161–2174 (2020).
Google Scholar
Hess, M., MacDonald, L. W. & Valach, J. Application of multi-modal 2D and 3D imaging and analytical techniques to document and examine coins on the example of two Roman silver denarii. Herit. Sci. 6, 5 (2018).
Google Scholar
Saha, S., Siatou, A., Mansouri, A. & Sitnik, R. Supervised segmentation of RTI appearance attributes for change detection on cultural heritage surfaces. Herit. Sci. 10, 173 (2022).
Google Scholar
Siatou, A. et al. A Methodological Approach for Multi-Temporal Tracking of Silver Tarnishing. In Proceedings of the 4th ACM International workshop on Structuring and Understanding of Multimedia heritAge Contents, 5–13 (ACM, 2022).
Siatou, A. Detection and characterization of appearance change by imaging and artificial vision for the conservation documentation of cultural heritage metal objects. Ph.D. thesis, Université Bourgogne Franche-Comté https://theses.hal.science/tel-04623231 (2023).
Cantó, A., Lerma, J. L., Martínez Valle, R. & Villaverde, V. Multi-light photogrammetric survey applied to the complex documentation of engravings in Palaeolithic rock art: the Cova de les Meravelles (Gandia, Valencia, Spain). Herit. Sci. 10, 169 (2022).
Google Scholar
Le Goïc, G. et al. Reflectance Transformation Imaging for the quantitative characterization of experimental fracture surfaces of bonded assemblies. Eng. Fail. Anal. 140, 106582 (2022).
Google Scholar
Nurit, M. et al. HD-RTI: an adaptive multi-light imaging approach for the quality assessment of manufactured surfaces. Comp. Ind. 132, 103500 (2021).
Google Scholar
Zendagui, A. et al. Quality assessment of dynamic virtual relighting from rti data: application to the inspection of engineering surfaces. In Fifteenth International Conference on Quality Control by Artificial Vision, vol. 11794 Article 117940E. (SPIE, 2021).
Righetto, L. et al. Ancient coins’ surface inspection with web-based neural RTI visualization. In Optics for Arts, Architecture, and Archaeology (O3A) IX, vol. 12620 Article 126200D (SPIE, 2023).
Righetto, L. et al. Effective Interactive Visualization of Neural Relightable Images in a Web-based Multi-layered Framework. In GCH 2023-Eurographics Workshop on Graphics and Cultural Heritage, 57–66 (GCH, 2023).
Coules, H., Orrock, P. & Seow, C. E. Reflectance Transformation Imaging as a tool for engineering failure analysis. Eng. Fail. Anal. 105, 1006–1017 (2019).
Google Scholar
Ding, Q.-K. & Liang, H.-E. Digital restoration and reconstruction of heritage clothing: a review. Herit. Sci. 12, 225 (2024).
Google Scholar
Jahnke, C. & Ling Huang, A.Textiles and the Medieval Economy: Production, Trade, and Consumption of Textiles, 8th-16th Centuries (Oxbow Books, 2014).
Schoeser, M. World Textiles (Thames & Hudson, 2022).
Eriksen, Å. The techniques of samitum. Based on a reconstruction of a silk from the Oseberg burial. In The Social Fabric: Deep Local to Pan Global; Proceedings of the Textile Society of America 16th Biennial Symposium. https://digitalcommons.unl.edu/tsaconf/1083 (Vancouver, BC, Canada, 2018).
Bonde, N. & Stylegar, F.-A. Between Sutton Hoo and Oseberg–dendrochronology and the origins of the ship burial tradition. Dan. J. Archaeol. 5, 19–33 (2016).
Google Scholar
Vedeler, M.The Oseberg Tapestries (Scandinavian Academic Press, 2019).
Righetto, L. et al. Efficient and user-friendly visualization of neural relightable images for cultural heritage applications. ACM J. Comput. Cult. Herit. 17, 54 (2024).
Gigilashvili, D., Lukesova, H., Gulbrandsen, C. F., Harijan, A. & Hardeberg, J. Y. Computational techniques for virtual reconstruction of fragmented archaeological textiles. Herit. Sci. 11, 259 (2023).
Google Scholar
Lester-Makin, A. & Owen-Crocker, G. R. (eds.) Textiles of the Viking North Atlantic: Analysis, Interpretation, Re-creation. No. 7 in Medieval and Renaissance Clothing and Textiles (Boydell Press, 2024).
Ngo, V. M., Helmer, S., Le-Khac, N.-A. & Kechadi, M.-T. Structural textile pattern recognition and processing based on hypergraphs. Inf. Retr. J. 24, 137–173 (2021).
Google Scholar
Abitbol, R., Shimshoni, I. & Ben-Dov, J. Machine Learning Based Assembly of Fragments of Ancient Papyrus. J. Comput. Cultural Herit. 14, 33 (2021).
Google Scholar
Villegas-Suarez, A. M., Lopez, C. & Sipiran, I. MatchMakerNet: Enabling Fragment Matching for Cultural Heritage Analysis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 1632–1641 (Paris, France, 2023).
Ren, P. et al. A survey of deep active learning. ACM Comput. Surv. 54, 180 (2021).
Amin, S. U., Hussain, A., Kim, B. & Seo, S. Deep learning based active learning technique for data annotation and improve the overall performance of classification models. Expert Syst. Appl. 228, 120391 (2023).
Google Scholar
Fiorucci, M. et al. Machine learning for cultural heritage: a survey. Pattern Recognit. Lett. 133, 102–108 (2020).
Google Scholar
Tasci, B. et al. InCR: Inception and concatenation residual block-based deep learning network for damaged building detection using remote sensing images. Int. J. Appl. Earth Observ. Geoinf. 123, 103483 (2023).
Wen, L., Li, X. & Gao, L. A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput. Appl. 32, 6111–6124 (2020).
Google Scholar
Cendre, R., Mansouri, A., Perrot, J.-L., Cinotti, E. & Marzani, F. Classification of lentigo maligna at patient-level by means of reflectance confocal microscopy data. Appl. Sci. 10, 2830 (2020).
Google Scholar
Ul Amin, S., Sibtain Abbas, M., Kim, B., Jung, Y. & Seo, S. Enhanced anomaly detection in pandemic surveillance videos: an attention approach with efficientNet-B0 and CBAM Integration. IEEE Access 12, 162697–162712 (2024).
Google Scholar
Sultani, W., Chen, C. & Shah, M. Real-World Anomaly Detection in Surveillance Videos. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6479–6488 (IEEE, 2018).
Ul Amin, S. et al. EADN: an efficient deep learning model for anomaly detection in videos. Mathematics 10, 1555 (2022).
Google Scholar
Ul Amin, S., Kim, B., Jung, Y., Seo, S. & Park, S. Video anomaly detection utilizing efficient spatiotemporal feature fusion with 3d convolutions and long short-term memory modules. Adv. Intell. Syst. 6, 2300706 (2024).
Google Scholar
Weinmann, M. Visual Features – From Early Concepts to Modern Computer Vision. In Farinella, G. M., Battiato, S. & Cipolla, R. (eds.) Advanced Topics in Computer Vision, Advances in Computer Vision and Pattern Recognition, 1–34 (Springer, 2013).
Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Proceedings of the 28th International Conference on Neural Information Processing Systems – Volume 2, NIPS’14, 3320-3328 https://doi.org/10.5555/2969033.2969197 (MIT Press, 2014).
He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 770–778 (IEEE, 2016).
Dosovitskiy, A. et al. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=YicbFdNTTy (2021).
Alrahhal, M. & Supreethi, K. Integrating machine learning algorithms for robust content-based image retrieval. Int. J. Inf. Technol. 16, 5005–5021 (2024).
Zheng, Q., Tian, X., Yang, M. & Wang, H. Differential learning: A powerful tool for interactive content-based image retrieval. Eng. Lett. 27, 202–215 (2019).
Simonyan, K. & Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations, ICLR (ICLR, 2015).
Sezavar, A., Farsi, H. & Mohamadzadeh, S. Content-based image retrieval by combining convolutional neural networks and sparse representation. Multimed. Tools Appl. 78, 20895–20912 (2019).
Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems25 (NIPS, 2012).
Huang, H., Wang, C., Wei, X. & Zhou, Y. Deep image clustering: a survey. Neurocomputing 599, 128101 (2024).
Google Scholar
Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C. & Dosovitskiy, A. Do vision transformers see like convolutional neural networks? Adv. neural Inf. Process. Syst. 34, 12116–12128 (2021).
Khawaja, M. A., George, S., Marzani, F., Hardeberg, J. Y. & Mansouri, A. An interactive method for adaptive acquisition in reflectance transformation imaging for cultural heritage. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 1698–1706 (IEEE, 2023).
Zhang, M. & S Drew, M. Efficient robust image interpolation and surface properties using polynomial texture mapping. EURASIP J. Image Video Process. 2014, 25 (2014).
Google Scholar
McGuigan, M. & Christmas, J. Automating RTI: Automatic light direction detection and correcting non-uniform lighting for more accurate surface normals. Computer Vis. Image Underst. 192, 102880 (2020).
Google Scholar
Manfredi, M. et al. A new quantitative method for the non-invasive documentation of morphological damage in paintings using RTI surface normals. Sensors 14, 12271–12284 (2014).
Google Scholar
Ponchio, F., Corsini, M. & Scopigno, R. A compact representation of relightable images for the web. In Proceedings of the 23rd International ACM Conference on 3D Web Technology, 1 (ACM, 2018).
Khan, A., Sohail, A., Zahoora, U. & Qureshi, A. S. A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53, 5455–5516 (2020).
Google Scholar
Tan, C. et al. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Part III, vol. 11141, 270–279 (Springer, 2018).
Deng, J. et al. Imagenet: A large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 248–255 (IEEE, 2009).
Greenacre, M. et al. Principal component analysis. Nat. Rev. Methods Prim. 2, 100 (2022).
Google Scholar
Saeed, N., Nam, H., Haq, M. I. U. & Muhammad Saqib, D. B. A survey on multidimensional scaling. ACM Comput. Surv. 51, 47 (2018).
Borg, I. & Groenen, P. J.Modern multidimensional scaling: Theory and applications (Springer Science & Business Media, 2007).
Maaten, L. vd & Hinton, G. Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Belkina, A. C. et al. Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat. Commun. 10, 5415 (2019).
Google Scholar
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. J Open Source Soft 3, 861 (2018).
Google Scholar
Mittal, M. et al. Dimensionality Reduction Using UMAP and TSNE Technique. In Second International Conference on Advances in Information Technology (ICAIT) (IEEE, 2024).
Saxena, A. et al. A review of clustering techniques and developments. Neurocomputing 267, 664–681 (2017).
Google Scholar
Aggarwal, C. C., Hinneburg, A. & Keim, D. A. On the Surprising Behavior of Distance Metrics in High Dimensional Space. In Database Theory — ICDT 2001, vol. 1973 of Lecture Notes in Computer Science, 420–434 (Springer, 2001).
Ding, L., Li, C., Jin, D. & Ding, S. Survey of spectral clustering based on graph theory. Pattern Recognit. 151, 110366 (2024).
Google Scholar
Kruskal, J. B. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1–27 (1964).
Google Scholar
Venna, J. & Kaski, S. Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study. In Dorffner, G., Bischof, H. & Hornik, K. (eds.) International Conference on Artificial Neural Networks (ICANN), Lecture Notes in Computer Science, 485–491 (Springer, 2001).
Kobak, D. & Berens, P. The art of using t-sne for single-cell transcriptomics. Nat. Commun. 10, 5416 (2019).
Google Scholar
