Suchergebnisse

International audience ; 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.

International audience ; 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.

International audience ; This study is part of a regeneration program of the coconut grove of French Polynesia where most coconut palm trees of the Tuamotu archipelago were planted in the 1980's following the various hurricanes that had struck islands. The French Polynesia government acquired one-meter pansharpened RGB Ikonos images over the Tuamotu archipelago. To exploit these data, a pilot study is conducted on the island of Tikehau, well-known from the specialists and easily accessible from Tahiti. A Maximum Likelihood (ML) classification is performed to segment the high vegetation in images. Thus, a Support Vector Machines (SVM) classification allows the high vegetation to be classified in different patterns. And finally, a robust segmentation process based on markers controlled watershed segmentation is proposed to extract tree crows. Through the ground mission, the trees detection accuracy is estimated which is then used to compute the number of trees the closest to the reality by applying a weighted factor to the number of trees located in each class.

International audience ; This study is part of a regeneration program of the coconut grove of French Polynesia where most coconut palm trees of the Tuamotu archipelago were planted in the 1980's following the various hurricanes that had struck islands. The French Polynesia government acquired one-meter pansharpened RGB Ikonos images over the Tuamotu archipelago. To exploit these data, a pilot study is conducted on the island of Tikehau, well-known from the specialists and easily accessible from Tahiti. A Maximum Likelihood (ML) classification is performed to segment the high vegetation in images. Thus, a Support Vector Machines (SVM) classification allows the high vegetation to be classified in different patterns. And finally, a robust segmentation process based on markers controlled watershed segmentation is proposed to extract tree crows. Through the ground mission, the trees detection accuracy is estimated which is then used to compute the number of trees the closest to the reality by applying a weighted factor to the number of trees located in each class.

International audience ; This study is part of a regeneration program of the coconut grove of French Polynesia where most coconut palm trees of the Tuamotu archipelago were planted in the 1980's following the various hurricanes that had struck islands. The French Polynesia government acquired one-meter pansharpened RGB Ikonos images over the Tuamotu archipelago. To exploit these data, a pilot study is conducted on the island of Tikehau, well-known from the specialists and easily accessible from Tahiti. A Maximum Likelihood (ML) classification is performed to segment the high vegetation in images. Thus, a Support Vector Machines (SVM) classification allows the high vegetation to be classified in different patterns. And finally, a robust segmentation process based on markers controlled watershed segmentation is proposed to extract tree crows. Through the ground mission, the trees detection accuracy is estimated which is then used to compute the number of trees the closest to the reality by applying a weighted factor to the number of trees located in each class.

International audience ; This study is part of a regeneration program of the coconut grove of French Polynesia where most coconut palm trees of the Tuamotu archipelago were planted in the 1980's following the various hurricanes that had struck islands. The French Polynesia government acquired one-meter pansharpened RGB Ikonos images over the Tuamotu archipelago. To exploit these data, a pilot study is conducted on the island of Tikehau, well-known from the specialists and easily accessible from Tahiti. A Maximum Likelihood (ML) classification is performed to segment the high vegetation in images. Thus, a Support Vector Machines (SVM) classification allows the high vegetation to be classified in different patterns. And finally, a robust segmentation process based on markers controlled watershed segmentation is proposed to extract tree crows. Through the ground mission, the trees detection accuracy is estimated which is then used to compute the number of trees the closest to the reality by applying a weighted factor to the number of trees located in each class.