A new segmentation-assisted interpolation method for creating maps in the study of artworks

Abstract

The generation of spatial distribution maps of chemical elements and compounds has become a crucial technique in materials research, particularly in the analysis of artworks. However, data acquisition in this context is often limited by the low number of measured points relative to the visual complexity of the artwork. As a result, interpolation methods are employed to infer unmeasured data. The most widely used method, Minimum Hypercube Distance (MHD), although statistically validated, exhibits significant limitations, as demonstrated in this study. We identified errors of up to 100% in some cases, exposing the method's vulnerability in regions lacking sufficient data. To address these challenges, we propose a novel segmentation-assisted interpolation method. By integrating semantic segmentation, this approach improves the accuracy and interpretability of the resulting maps, allowing for the precise identification of unmeasured areas and the expert-guided replication of data from similar regions. This new methodology enhances the robustness of artwork analysis, providing more reliable tools for the study and preservation of artworks and ancient monuments.