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Automatic classification of ceramic sherds with relief motifs

机译:带浮雕图案的陶瓷搁板自动分类

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A large corpus of ceramic sherds dating from the High Middle Ages has been extracted in Saran (France). The sherds have an engraved frieze made by the potter with a carved wooden wheel. These relief patterns can be used to date the sherds in order to study the diffusion of ceramic production. The aim of the ARCADIA project was to develop an automatic classification of this archaeological heritage. The sherds were scanned using a three-dimensional (3-D) laser scanner. After projecting the 3-D point cloud onto a depth map, the local variance highlighted the shallow relief patterns. The saliency region focused on the motif was extracted by a density-based spatial clustering of FAST points. An adaptive thresholding was then applied to the depth to obtain a binary pattern close to manual sampling. The five most representative types of motif were classified by training an SVM model with a pyramid histogram of visual words descriptor. Compared with other state-of-the-art methods, the proposed approach succeeded in classifying up to 84% of the binary patterns on a dataset of 377 scanned sherds. The automatic method is extremely time-saving compared to manual stamping. (C) 2017 SPIE and IS&T
机译:在法国的萨兰(Saran)提取了一大批可追溯至中世纪的陶器陶瓷料。琴棚上刻有由窑匠雕刻的fr带,上面刻有木轮。这些浮雕图案可以用来定日期,以研究陶瓷产品的扩散。 ARCADIA项目的目的是对这种考古遗产进行自动分类。使用三维(3-D)激光扫描仪扫描了种子。将3-D点云投影到深度图上后,局部方差突出显示了浅浮雕图案。通过基于密度的FAST点空间聚类来提取关注于主题的显着区域。然后将自适应阈值应用于深度,以获得接近手动采样的二进制模式。通过用视觉单词描述符的金字塔直方图训练SVM模型对五种最具代表性的主题类型进行了分类。与其他最先进的方法相比,该方法成功地对377个扫描的片的数据集上的84%的二进制模式进行了分类。与手动冲压相比,自动方法非常省时。 (C)2017 SPIE和IS&T

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