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Art conservators make use of new sensors and modern techniques to study, conserve, and restore old and often damaged artworks. The key issue of the art restoration is the material analyses research – an identification of the used materials. Its aim is the location and the classification of inorganic and organic compounds using microanalytical methods, and description of painting layers and their morphology, where the layer is defined as consistent and distinguishable part of the painting profile. Our proposed system Nephele tries to facilitate the work of restorers in this field.
Several image analyzing modules are incorporated in the Nephele system. They were designed for processing input image data acquired during the material research: microscopic images of minute surface samples (0.3mm in diameter). They are taken off of the selected areas, embedded in a polyester resin, and grounded at a right angle to the surface plane to expose the painting layers of the artwork. The microscopic images are taken in several modalities. Stratigraphy (learning about painting layers) is usually studied in VIS and UV images, where the UV analysis makes use of the luminescence. Different materials have different luminescence, which can help distinguish materials not resolvable otherwise.
Image preprocessing
The ultimate goal of the image preprocessing is the identification and description of the individual material layers. Before the layer localization can start, the multimodal input data have to be brought into geometric alignment, because the VIS and UV image pairs of the sample are often geometrically misaligned due to manipulation errors etc. They can be mutually shifted and rotated in the scanning plane. The proposed image registration module of the system solves the spatial alignment of the image pairs. After the image rectification, the color layers can be estimated. The segmentation module performs segmentation of the cross-section from the noisy background and also preliminary layer segmentation based on both VIS and UV images. The construction of the full and correct segmentation turns out to be a very complex task, because expert knowledge is often necessary (certain materials cannot be neighbors, others are always together, etc.). After the layer segmentation, we have the set of base structures, which are homogenous and can be further described, analyzed and used for more sophisticated tasks such as image based retrieval or material classification.
Image retrieval
For better functionality of the Nephele database, effective tools are implemented to look-up relevant reports. One of them is content-based image retrieval (CBIR), which is recently very popular and is used as a part of multimedia systems in art galleries. The image retrieval exploits similarity of the query sample to the images contained in the archived reports. The visual similarity can point to the same author, used material, or technique.
| Details: | |
| Duration: | since 2004 |
| Contact person: | Miroslav Beneš |
| Involved people: | Barbara Zitová |
| Involved extern: | Janka Hradilová (AVU, ALMA), David Hradil (UACH, ALMA) |
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