Drawing the Line: Deep Segmentation for Extracting Art from Ancient Etruscan Mirrors
This work addresses a domain-specific problem for archaeologists and art historians by streamlining and objectifying the annotation process for ancient artifacts, though it is incremental as it builds on existing deep learning methods.
The paper tackles the labor-intensive and subjective task of manually tracing engravings from damaged Etruscan mirrors by automating it with photometric-stereo scanning and deep segmentation networks, improving predictive performance by around 16% in pseudo-F-Measure and achieving performance similar to human annotators.
Etruscan mirrors constitute a significant category within Etruscan art and, therefore, undergo systematic examinations to obtain insights into ancient times. A crucial aspect of their analysis involves the labor-intensive task of manually tracing engravings from the backside. Additionally, this task is inherently challenging due to the damage these mirrors have sustained, introducing subjectivity into the process. We address these challenges by automating the process through photometric-stereo scanning in conjunction with deep segmentation networks which, however, requires effective usage of the limited data at hand. We accomplish this by incorporating predictions on a per-patch level, and various data augmentations, as well as exploring self-supervised learning. Compared to our baseline, we improve predictive performance w.r.t. the pseudo-F-Measure by around 16%. When assessing performance on complete mirrors against a human baseline, our approach yields quantitative similar performance to a human annotator and significantly outperforms existing binarization methods. With our proposed methodology, we streamline the annotation process, enhance its objectivity, and reduce overall workload, offering a valuable contribution to the examination of these historical artifacts and other non-traditional documents.