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Computer Vision Based Nacre Thickness Measurement of Tahitian Pearls

Abstract : The Tahitian Pearl is the most valuable export product of French Polynesia contributing with over 61 million Euros to more than 50% of the total export income. To maintain its excellent reputation on the international market, an obligatory quality control for every pearl deemed for exportation has been established by the local government. One of the controlled quality parameters is the pearls nacre thickness. The evaluation is currently done manually by experts that are visually analyzing X-ray images of the pearls. In this article, a computer vision based approach to automate this procedure is presented. Even though computer vision based approaches for pearl nacre thickness measurement exist in the literature, the very specific features of the Tahitian pearl, namely the large shape variety and the occurrence of cavities, have so far not been considered. The presented work closes the. Our method consists of segmenting the pearl from X-ray images with a model-based approach, segmenting the pearls nucleus with an own developed heuristic circle detection and segmenting possible cavities with region growing. Out of the obtained boundaries, the 2-dimensional nacre thickness profile can be calculated. A certainty measurement to consider imaging and segmentation imprecision s is included in the procedure. The proposed algorithms are tested on 298 manually evaluated Tahitian pearls, showing that it is generally possible to automatically evaluate the nacre thickness of Tahitian pearls with computer vision. Furthermore the results show that the automatic measurement is more precise and faster than the manual one.
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https://hal-upf.archives-ouvertes.fr/hal-03132310
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Submitted on : Friday, February 5, 2021 - 1:42:04 AM
Last modification on : Friday, February 19, 2021 - 3:08:20 AM
Long-term archiving on: : Thursday, May 6, 2021 - 6:21:11 PM

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Martin Loesdau, Sébastien Chabrier, Alban Gabillon. Computer Vision Based Nacre Thickness Measurement of Tahitian Pearls. The International Conference on Quality Control by Artificial Vision 2017, May 2017, Tokyo, Japan. pp.103381C, ⟨10.1117/12.2266924⟩. ⟨hal-03132310⟩

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