Summary of Drawing the Line: Deep Segmentation For Extracting Art From Ancient Etruscan Mirrors, by Rafael Sterzinger et al.
Drawing the Line: Deep Segmentation for Extracting Art from Ancient Etruscan Mirrors
by Rafael Sterzinger, Simon Brenner, Robert Sablatnig
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors propose an automated method for tracing engravings on Etruscan mirrors, which are crucial for understanding ancient times. The traditional manual process is time-consuming and prone to subjective errors due to damage sustained by the mirrors. To address these challenges, the researchers combine photometric-stereo scanning with deep segmentation networks. They also develop per-patch predictions, data augmentations, and self-supervised learning techniques to effectively utilize limited available data. Compared to a baseline, the proposed method improves predictive performance by around 16%. The approach achieves similar performance to human annotators for complete mirrors and outperforms existing binarization methods. This streamlined methodology enhances objectivity, reduces workload, and contributes to the examination of historical artifacts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Etruscan mirrors are important art pieces that can teach us about ancient times. To study them, researchers need to manually trace the engravings on the backside of the mirrors. However, this task is hard because many mirrors are damaged, which makes it harder to be objective. The authors of this paper came up with a way to automate this process using computer technology. They use special cameras and machine learning algorithms to create detailed images of the engravings. This new method can help reduce the time and effort needed to study these important historical artifacts. |
Keywords
» Artificial intelligence » Machine learning » Self supervised
