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. 2021 Mar 12;7(3):53.
doi: 10.3390/jimaging7030053.

Analysis of Diagnostic Images of Artworks and Feature Extraction: Design of a Methodology

Annamaria Amura  1 , Alessandro Aldini  1 , Stefano Pagnotta  2 , Emanuele Salerno  3 , Anna Tonazzini  3 , Paolo Triolo  4
Affiliations

Analysis of Diagnostic Images of Artworks and Feature Extraction: Design of a Methodology

Annamaria Amura et al. J Imaging. .

Abstract

Digital images represent the primary tool for diagnostics and documentation of the state of preservation of artifacts. Today the interpretive filters that allow one to characterize information and communicate it are extremely subjective. Our research goal is to study a quantitative analysis methodology to facilitate and semi-automate the recognition and polygonization of areas corresponding to the characteristics searched. To this end, several algorithms have been tested that allow for separating the characteristics and creating binary masks to be statistically analyzed and polygonized. Since our methodology aims to offer a conservator-restorer model to obtain useful graphic documentation in a short time that is usable for design and statistical purposes, this process has been implemented in a single Geographic Information Systems (GIS) application.

Keywords: cultural heritage; diagnostic images; documentation; feature extraction; geographic information systems (GIS); image analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Methodology lifecycle.
Figure 2
Figure 2
Phase I, stage 1—Ecce Homo by Bernardo Strozzi, oil on canvas, 1620–1622, 105 × 75 cm2: (a) Standard RGB; (b) UV-induced fluorescence; (c) NIR reflectography; (d–f) Respective spectral Channels of the acquired images. Images captured by Paolo Triolo, under permission of the Ministry of Cultural Heritage and Activities and Tourism, National Gallery of Palazzo Spinola, Genova, Italy.
Figure 3
Figure 3
Phase I, stage 2: (a) Output channels obtained by principal component analysis (PCA) from the entire data set in Figure 2; (b) Output channels obtained by independent component analysis (ICA) from the entire data set in Figure 2; (c) Output channels obtained by ICA from the multispectral cube with no IR data in Figure 2a,b,d,e.
Figure 4
Figure 4
Phase I stages 3–4: (a,e,i) Images processed from the previous stage (in Figure 3) and chosen to identify regions of interest (ROIs); (b,f,j) Corresponding binarized versions; (c,g,k) Image polygonization, raster to vector conversion. Phase II stage 5: (d,h,l) Characterization of the extracted polygons.
Figure 5
Figure 5
Thematic map-specific colors are assigned to the ROIs (statistical data are omitted).
See this image and copyright information in PMC

References

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