Drawing the Line: Deep Segmentation for Extracting Art from Ancient Etruscan Mirrors

TL;DR

This paper presents an automated deep learning approach for segmenting engravings on damaged Etruscan mirrors, improving accuracy and objectivity while reducing manual effort in art analysis.

Contribution

It introduces a novel combination of photometric-stereo scanning, patch-level predictions, data augmentation, and self-supervised learning for effective segmentation with limited data.

Findings

16% improvement over baseline in pseudo-F-Measure

Achieves performance comparable to human annotators

Outperforms existing binarization methods

Abstract

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.