Digitization and preservation of cultural heritage: The CEPROQHA approach

The humanity has always learned from the previous experiences for many reasons. The national heritage proves to be a great way to discover a nation's history. As a result, these priceless cultural items have a special attention. However, Since the wide adoption of new digital technologies, documenting, storing, and exhibiting cultural heritage assets became more affordable and reliable. These digital records are then used in several applications. Researchers saw the opportunity to use digital heritage recordings for long-term preservation. In this paper, we present the research progress in cultural heritage digital processing and preservation, highlighting the most impactful advances. Additionally, we present the CEPROQHA project which is based on a new approach aiming at achieving cost-effective acquisition and digital preservation for cultural heritage artifacts in Qatar. - 2017 IEEE.CEPROQHA3 is a cooperative research project between Qatar University and Brunel University, London, funded by the Qatar National Research Fund (QNRF) under the National Priorities Research Program(NPRP) 9th cycle. The project goal. ACKNOWLEDGEMENTS This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Buffer layer annealing effects on the magnetization reversal process in Pd/Co/Pd systems

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Influence of Pt thickness on magnetization reversal processes in (Pt/Co)3 multilayers with perpendicular anisotropy

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Towards an Inpainting Framework for Visual Cultural Heritage

Cultural heritage takes an important part in defining the identity and the history of a civilization or a nation. Valuing and preserving this heritage is thus a top priority for governments and heritage institutions. Through this paper, we present an image completion (inpainting) approach adapted for the curation and the completion of damaged artwork. Our approach uses a set of machine learning techniques such as Generative Adversarial Networks which are among the most powerful generative models that can be trained to generate realistic data samples. As we are focusing mostly on visual cultural heritage, the pipeline of our framework has many optimizations such as the use of clustering to optimize the training of the generative part to ensure a better performance across a variety of cultural data categories. The experimental results of our framework are promising and were validated on a dataset of paintings.This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Investigating 3D holoscopic visual content upsampling using super resolution for cultural heritage digitization

This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation).NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation)

An integrated framework for the interaction and 3D visualization of cultural heritage

Data availability: Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.1161: Multimedia Alternate RealitiesNPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation) (Cost-Effective High-Quality Preservation and Restoration of Qatar Cultural Heritage through Advanced Holoscopic 3D Imaging); Qatar National Research Funds Ref: CEPROQHA (3D Holoscopic Imaging and Big Data Analytics in the Cultural Heritage Domain)

Disordered DW motion described by the compressed exponential of Fatuzzo-Labrune model in ultrathin Au/Co/Au films

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Magnetization reversal dynamics, nucleation, pinning, and domain wall propagation in perpendicularly magnetized ultrathin cobalt films: Influence of the Co deposition rate

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