A New Quantitative Method for the Non-Invasive Documentation of Morphological Damage in Paintings Using RTI Surface Normals
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Summary
Quantitative RTI eases the transition of extending human vision into the realm of measuring change over time and can detect a morphological damage slightly smaller than 0.3 mm, which would be difficult to detect with the eye, considering the painting size.
- Type
- article
- Published
- 2014-07-01
- Cited by
- 49
- References
- 30
- Access
- Open access
- OpenAlex
- https://openalex.org/W25010699
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3183004
Keywords
Political science, Humanities, Philosophy
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- Fast Image Restoration for Spatially Varying Defocus Blur of Imaging Sensor
- QUANTITATIVE MULTISPECTRAL IMAGING FOR THE DETECTION OF PARCHMENT AGEING CAUSED BY LIGHT: A COMPARISON WITH ATR-FTIR, GC-MS AND TGA ANALYSES
- Alchemy in 3D: A digitization for a journey through matter
- Quantitative imaging to study new conservation materials
- Portable non-invasive imaging method for monitoring the conservation of frescoes
- Robust Image Restoration for Motion Blur of Image Sensors
- Direct Analysis in Real Time Mass Spectrometry for the Nondestructive Investigation of Conservation Treatments of Cultural Heritage
- A stabilizer-free non-polar dispersion for the deacidification of contemporary art on paper
- Analyzing the Heterogeneous Hierarchy of Cultural Heritage Materials: Analytical Imaging.
- Strategies for Virtual Sales Leaders to Increase Productivity of Remote Employees
- Defocus Blur Detection and Estimation from Imaging Sensors
- Cultural Connotation and Integration of Painting and Media in Contemporary Art
- Revisiting Reflectance Transformation Imaging (RTI): A Tool for Monitoring and Evaluating Conservation Treatments
- Deformation analysis of Leonardo da Vinci's “Adorazione dei Magi” through temporal unrelated 3D digitization
- Objective and Subjective Evaluation of Virtual Relighting from Reflectance Transformation Imaging Data
- Ensemble Dictionary Learning for Single Image Deblurring via Low-Rank Regularization
- State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis
- Crack Detection in Single- and Multi-Light Images of Painted Surfaces using Convolutional Neural Networks
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