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Image Segmentation Applied to the Investigation of Craquelure Domains in Paintings
Andrea Arsiccio, Amelia Carolina Sparavigna, Antonello Barresi
To cite this version:
References
Conclusions and Perspectives
Image Segmentation Applied to the Investigation of
Craquelure Domains in Paintings
Andrea Arsiccio, Amelia C. Sparavigna, Antonello A. Barresi
Department of Applied Science and Technology, Politecnico di Torino 24, corso Duca degli Abruzzi, Torino, Italy, 10129
❑ contact: [email protected]
7th European Drying Conference, July 10-12, 2019, Politecnico di Torino, Italy
Why Studying Craquelure Domains?
Old paintings are characterized by the presence of cracks patterns, formed during the drying process. This network of cracks, known as
craquelure, represents a key feature for:
➢ the authentication of artworks ➢ the discovery of forgery
Seven key features may be used to describe craquelure morphology [1]: 1. local and global direction of cracks (isotropic or anisotropic pattern) 2. cracks shape (curved or straight)
3. cracks spacing 4. cracks thickness
5. organization of the cracks network 6. connections between cracks
7. relationship between cracks directions and the weave or grain direction
8. of the support
Several scientific methods have been proposed for the analysis of art objects [2], and image analysis seems a promising tool in this field.
Image Segmentation for Paintings: Examples of Application
Craquelure is undesirable from the aesthetic point of view, but reveals important information
about the methods used by the artist.
An image analysis approach has been proposed for the analysis of the craquelure domains in paintings.
The proposed technique allows quantification of the size distribution of the cracks pattern, and of cracks orientation.
This information could help with the tasks of artist attribution and identification of forged
artworks.
[1] Bucklow S.L., 1997, The description of craquelure patterns, Stud. Conserv. 42, 129-140. [2] Craddock P., 2009, Scientific investigation of copies, fakes and forgeries.
Butterworth-Heinemann, Oxford.
[3] Arsiccio A., Sparavigna A.C., Pisano R. and Barresi A.A, 2019, Measuring and predicting pore size distribution of freeze-dried solutions, Drying Technol. 37, 435-447.
[4] Sparavigna A.C., 2019, A method based on image segmentation for the analysis of the orientation of rod-like objects. Zenodo. http://doi.org/10.5281/zenodo.2567394
[5] Bucklow S.L., 1997, A stylometric analysis of craquelure, Comput. Human. 31, 503-521.
[6] Bucklow S.L., 1999, The description and classification of craquelure. Stud. Conserv. 44, 233-244.
Identification of
painting school
Exploiting Image Segmentation…
Segmentation is an image processing method used for partitioning an
image into multiple sets of pixels, defined as its «super-pixels».
The image is first turned to a binary representation using a thresholding method.
Starting from the left/upper corner of this matrix, and moving along the rows and columns of the matrix, each black pixel is labelled with a sequential
integer number k. The labels of the nearest black pixels above (subscript A)
and on the left (subscript L) determine the label k of the black pixel being considered [3].
k = min (kA, kL)
A new representation can be eventually proposed, where a different color
tone is associated to each label and, therefore, to each super-pixel.
The size distribution of the craquelure domains can therefore be computed by computing the number of pixels in each super-pixel.
The edges orientation may also be investigated: the angle that each edge forms with the horizontal axis can be measured.
For doing this, the elements Ixx, Iyy and Ixy of a matrix of inertia can be evaluated for each segment, using the coordinates of the i-th pixel and of the centre of mass (subscript cm) [4],
𝐼𝑥𝑥 = σ𝑖(𝑦𝑖 − 𝑦𝑐𝑚)2, 𝐼𝑦𝑦 = σ𝑖(𝑥𝑖 − 𝑥𝑐𝑚)2, 𝐼𝑦𝑥 = 𝐼𝑥𝑦 = σ𝑖(𝑥𝑖 − 𝑥𝑐𝑚)(𝑦𝑖 − 𝑦𝑐𝑚) The orientation angle can eventually be calculated as,
𝜃 = 𝐼𝑥𝑦
𝐼𝑥𝑦 tan
−1 𝐼
𝑦𝑦/𝐼𝑥𝑥
Two areas A and B of Vermeer’s painting Girl with a pearl earring (Mauritshuis Museum, The Hague, Netherlands) have been selected.
The corresponding images have been binarized (black and white) and segmented (coloured domains).
The segmentation approach allowed the identification of substantially different cracks
patterns for the two regions, as clearly emerges from the corresponding size distributions.
The distribution of the edges orientation angles for area A could also be obtained. For area B, edges were too curved to allow for an accurate definition of orientation angles.
Each domain has been
labelled by a different colour
and measured. The resulting
size distribution is shown, as
number of pixels per domain.
Portrait of a young woman by Petrus
Christus (Gemäldegalerie, Berlin) was
also considered, and the region of the painting surrounding the nose of the woman was studied.
The distribution of the cracks
orientation angles could eventually be
obtained
The distribution of the edges orientation for the two paintings is different.
This is expected according to [5,6], where it was shown that the craquelure pattern
changes with painting style, for instance French, Flemish, Dutch and Italian, therefore
being an indicator of authorship.
Paper 119
