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首页> 外文会议>Conference on Metrology for Archaeology and Cultural Heritage >Automatic Mosaic Digitalization: a Deep Learning approach to tessera segmentation
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Automatic Mosaic Digitalization: a Deep Learning approach to tessera segmentation

机译：自动马赛克数字化：令人胸衣细分的深入学习方法

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摘要

A mosaic, made of small coloured tiles, called tesserae or tessellas, is a form of decorative art to create images and patterns on a surface. One of the first steps to obtain digital mosaics is the segmentation process to separate (identify) tesserae. Then, various tools are necessary to catalog the digitalized scenes, to extract the figures, such as animal or human beings, from the puzzle of small pieces, to geolocalize the segmented objects and to assign a semantic meaning to them. While some mosaic segmentation approaches have already been reported in the literature, currently, mosaic segmentation is still done by human operators, resulting time consuming and error prone. We propose in this paper an automatic approach, based on Deep Learning, to segment the mosaic floor tesserae of the church of S. Stephen in Umm ar Rasas. Our approach allows to obtain automatically and with good reliability the description of the main elements of a mosaic (the tesserae) that are not homogeneous. The experiments performed on the collected tesserae dataset yield high accuracies and demonstrate the effectiveness and suitability of our approach.
机译：由小型瓷砖制成的马赛克，称为Tosserae或甜叶，是一种装饰艺术的形式，可以在表面上创造图像和图案。获得数字马赛克的第一步之一是分割（识别）Tesserae的分割过程。然后，各种工具是对数字化场景目录，以从小块的难题中提取诸如动物或人类的数字，以使分段对象的拼凑和分配对它们的语义意义。虽然已经在文献中报告了一些马赛克分割方法，但目前，马赛克分割仍然由人类运营商完成，导致耗时和容易出错。我们提出了一种基于深度学习的自动方法，分段为umm ar rasas的斯蒂芬教堂的马赛克地板。我们的方法允许自动获得，并且具有良好的可靠性，对不是均匀的马赛克（Tosserae）的主要元素的描述。对收集的胸部数据集进行的实验产生了高精度，并证明了我们方法的有效性和适用性。

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