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<p>Predynastic Egyptian stone vessels offer unique insight into the technological sophistication of early cultures. Thousands of stone vessels were created during the Predynastic Period. Many objects lack archaeological context due to looting and haphazard excavations. Numerous forgeries exist. This study aims to reduce the uncertainty and introduce a novel method for analyzing and classifying rotationally symmetric artifacts based on their 3D scans. We introduce a quality metric based on concentricity and circularity, hypothesizing that objects from different cultures were produced using distinct manufacturing techniques. We test the hypothesis on a dataset comprising 19 predynastic vessels from the Petrie Museum, 25 modern machine-made and 24 contemporary handcrafted vases, and numerous objects from private collections. We observe clustering by manufacturing quality and identify the use of precision tools. By comparing a 3D scan of an unprovenanced object to known quality classes, we can classify the object and identify possible forgeries.</p>

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A metrological method for manufacturing quality assessment and classification of ancient Egyptian stone vessels

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

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  • Max Fomitchev-Zamilov

摘要

Predynastic Egyptian stone vessels offer unique insight into the technological sophistication of early cultures. Thousands of stone vessels were created during the Predynastic Period. Many objects lack archaeological context due to looting and haphazard excavations. Numerous forgeries exist. This study aims to reduce the uncertainty and introduce a novel method for analyzing and classifying rotationally symmetric artifacts based on their 3D scans. We introduce a quality metric based on concentricity and circularity, hypothesizing that objects from different cultures were produced using distinct manufacturing techniques. We test the hypothesis on a dataset comprising 19 predynastic vessels from the Petrie Museum, 25 modern machine-made and 24 contemporary handcrafted vases, and numerous objects from private collections. We observe clustering by manufacturing quality and identify the use of precision tools. By comparing a 3D scan of an unprovenanced object to known quality classes, we can classify the object and identify possible forgeries.