Focus Point in The European Physical Journal Plus

This Focus Point invites contributions that showcase the most recent advances in the processing of close-range hyperspectral data of Cultural Heritage assets.

Technological improvements in spectroscopic imaging are leading to the necessity of relying more and more on computational approaches for processing large amounts of data in order to maximise the extraction of information and provide quantitative results. This is becoming increasingly relevant also in the domain of Cultural Heritage, where hyperspectral imaging methods are progressively acquiring more relevance for the analysis of artworks and antiquities held at Galleries, Libraries, Archives and Museums (GLAM).

Rather than on data acquisition or instrumental advances, papers to be included in this collection should focus on illustrating computational approaches that can be adopted to improve the quality of the images (e.g., noise removal, super-resolution, spectral unmixing etc.) and maximise the granularity of the results. Studies related to Machine Learning approaches for automatic classification, pattern recognition and multivariate clustering are particularly welcomed.

The Focus Point accepts also contributions on image processing approaches developed in other disciplines (e.g., medical imaging) that might be transferred to the field of Cultural Heritage and used for the study of cultural assets even if not tested yet, as long as the potential migration of techniques is sufficiently supported by clear motivations.

Overall, the Focus Point will include topic such as (but not limited to):

  • Image processing methods for hyperspectral data (including image enhancement)
  • Computational approaches for automatic classification, pattern recognition and multivariate clustering
  • Machine Learning techniques for automating processes of analysis (e.g. automatic pigments identification)
  • Examples on hidden image retrieval based on irregularity detection
  • Technology transfer in hyperspectral image processing from other domains to Cultural Heritage

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Manuscripts should be prepared following the instructions for the authors available on the journal's website and must not have been previously published or submitted for publication elsewhere. All manuscripts will be peer reviewed, and acceptance will be based on quality, relevance and originality.

The deadline for submission is: 31 October 2021.

Please consider that you can submit your manuscript as soon as it is ready. Accepted papers will be published without delay and collected in the focus point collection online. When your manuscript is ready, please submit it electronically at: http://www.editorialmanager.com/epjp

IMPORTANT: at Step 5 (Additional Information) of the submission form, please reply YES to the question 'Is this manuscript to be considered for a “focus point”?'. Then, please select: “FP: Advances in Hyperspectral Data Processing for Cultural Heritage".

The guest editors will coordinate the peer review process. The process will be supervised until final decision by the journal's editorial board. If you need any kind of assistance during the submission procedure or if you have any technical questions feel free to contact the Editorial Office at: This email address is being protected from spambots. You need JavaScript enabled to view it.

Regarding all scientific issues, please contact the guest editors directly.

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Guest Editors: 

Dr. Arianna Traviglia is the Coordinator of the IIT Centre for Cultural Heritage Technology (CCHT@Ca’Foscari). Her research is placed at the intersection of technology management and humanities and most of her work focuses on mediating the inclusion of technologies within the study and analysis of cultural heritage. She is part of the Executive Steering Committee of the International Computer Application and Quantitative Methods in Archaeology (CAA) association. She is also the co-Editor of the Journal of Computer Application in Archaeology (JCAA). She has chaired the 41st Computer Application and Quantitative Methods in Archaeology Conference (CAA2013 Perth Across space and time), co-organized the 2016 International Congress of Underwater Archaeology (IKUWA 6), the 2018 International Aerial Archaeology Group (AARG) conference, and chaired a number of sessions on several aspects of digital applications to archaeology major international conferences. She has also been member of several conferences organizing and scientific committees on digital CH topics. She is a member of the Management Committee of the COST Action CA15201 'Archaeological practices and knowledge work in the digital environment (Arkwork)' and one of its Core Team Members. Currently she is the PI of the H2020 CSA NETCHER project, focused on protection of endangered Cultural Heritage.

Dr. Alessia Artesani is a Post-Doc researcher at the Center for Cultural Heritage Technology working in characterization and protection of Cultural Heritage materials. With an educational background in Physics, she is specialized in development and application of optical techniques for the analysis of ancient and modern cultural heritage materials. She has extensive experience on spectroscopic imaging on paintings and spectral classification methods. She is also an expert of time-resolved photoluminescence methods, used for investigation on pictorial surfaces of modern and contemporary art. In her research career, she collaborated with various international teams on topics related to development of cryogenic device to investigate the luminescent signals of dosimetric materials. She delivered oral presentations in the most important national and international congresses on scientific methods applied to Cultural Heritage and she published in peer-reviewed international journals in the field of materials, chemists and applied physics, as well as contributing as peer-reviewer in international journals on the same topics.

Dr. Marco Fiorucci is Post-Doc researcher in Machine Learning at Centre for Cultural Heritage Technology. He is working mainly in the fields of Machine Learning, with particular emphasis on unsupervised representation learning and on geometric deep learning. He is a computer scientist with a solid broad background, spanning from Physics and Machine Learning to Graph Theory and Statistics, who has successfully worked on several different interdisciplinary projects both in academia and in industry. Recently, he has shifted his attention to the analysis of EO data (multispectral and hyperspectral data) for the detection of sub-surface archaeological sites and of hyperspectral data for pigment identification in painting. He has also worked on image classification and object detection since 2015, when he designed an Artificial Intelligence system for the automatic classification of product images in a commercial setting: his prototype has been presented to PepsiCo, Inc (New York). His research has been published in major Pattern Recognition and Machine Learning journals such as “Pattern Recognition” and “Pattern Recognition Letters” and in top journals of Complex Systems such as “Artificial Life”. He serves as reviewer for the Journal of Computer Applications in Archaeology, the journal of Pattern Recognition, Pattern Recognition Letters, Sensors and Applied Science.

Dr. Marina Ljubenovic is Post-Doc researcher at IIT Center for Cultural Heritage Technology. She obtained Bachelor and Master degrees in Telecommunications and Signal Processing at the Faculty of Technical Sciences, University of Novi Sad, Serbia. She received her PhD degree in Electrotechnics and Computer Sciences from the Institute Superior Tecnico, University of Lisbon. During her PhD, she was part of the Marie Curie Innovative Training Network (MacSeNet) and she was employed as an Early Stage Researcher at the Institute for Telecommunications in Lisbon. The focus of her PhD work was on image restoration, namely, denoising, super-resolution, and blind image deblurring. She developed several techniques for deblurring and denoising of special-class images and presented her work at workshops, international conferences, and peer-reviewed journals. After her PhD studies, she was a Post Doc Researcher at Vision Lab, University of Antwerp, Belgium. In Vision Lab, she was working on developing 3D imaging techniques with terahertz technology. Terahertz imaging is a new contact-free and non-destructive technique that can be used for scanning and analysing of soft materials (e.g., canvas, paper).  She developed several model-based and machine learning-based methods for super-resolution and deblurring of hyperspectral terahertz images. In her research career, she collaborated with various international teams and she was a visiting researcher at two research labs: Noiseless Imaging, Tampere University of Technology in Finland and Computer Technology Institute in Athens, Greece.

 

 

 

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