Mineral segmentation in ceramic thin sections containing different minerals, in which there are no evident and close boundaries, is a rather complex process. The results of such a process are used in archaeology for analyzing the origin and manufacturing techniques of ancient ceramics. In this paper we present a methodology for the segmentation and analysis of thin sections of material segments and reaching some conclusions in a fully automatic way. We employ machine learning and computer vision techniques to analyze a video of the thin section sample, acquired under an optical microscope. When examined under polarized light, the color of segments may vary during sample rotation. This variation is due to the optical properties of the materials and it provides valuable information about the material inclusions in the sample. Using the video as our input, we perform an entire-video segmentation. To accomplish this task, we developed a hierarchical categorical mean-shift-based algorithm. Using the entire-video segmentation we examine the detected segments and gather statistical information about their sizes, shapes and colors and present an overall report about the sample. We tested the algorithm on nine specimens of ancient ceramics, taken from three different Mediterranean sites. The results show clear differences between the sites in the amounts, sizes and shapes of the segments present in the specimens.

陶瓷薄片中的矿物分割包含不同的矿物，其中没有明显和紧密的边界，是一个相当复杂的过程。这一过程的结果被用于考古学，以分析古代陶瓷的起源和制造技术。在本文中，我们提出了一种对材料段的薄片进行分割和分析的方法，并以全自动方式得出一些结论。我们采用机器学习和计算机视觉技术来分析在光学显微镜下获得的薄片样品的视频。在偏振光下检查时，样品旋转过程中片段的颜色可能会有所不同。这种变化是由于材料的光学特性造成的，它提供了有关样品中材料夹杂物的有价值的信息。使用视频作为我们的输入，我们执行整个视频分割。为了完成这项任务，我们开发了一种基于分层分类均值偏移的算法。使用整个视频分割，我们检查检测到的片段并收集有关其大小、形状和颜色的统计信息，并提供有关样本的整体报告。我们在取自地中海三个不同地点的九个古代陶瓷标本上测试了该算法。结果表明，在样本中存在的片段的数量、大小和形状方面，各部位之间存在明显差异。形状和颜色，并提供有关样品的总体报告。我们在取自地中海三个不同地点的九个古代陶瓷标本上测试了该算法。结果表明，在样本中存在的片段的数量、大小和形状方面，各部位之间存在明显差异。形状和颜色，并提供有关样品的总体报告。我们在取自地中海三个不同地点的九个古代陶瓷标本上测试了该算法。结果表明，在样本中存在的片段的数量、大小和形状方面，各部位之间存在明显差异。