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Thin section analysis for ceramic petrography using motion analysis and segmentation techniques

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Abstract

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.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, The Open University of Israel, Ra’anana, Israel

    Jenny Lerner

  2. Department of Information Systems, University of Haifa, Haifa, Israel

    Ilan Shimshoni

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  1. Jenny Lerner
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  2. Ilan Shimshoni
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Correspondence to Jenny Lerner.

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This research was partially funded by the Israeli Ministry Science and Technology (MOST) grant number 3-17513.

Appendix: Detailed results

Appendix: Detailed results

Figures 33, 34, 35, 36, 37, 38, 39, 40 and 41 summarize the result of each site individually. The final results of each sample video include:

  1. 1.

    An area histogram, which shows the distribution of the area of the segments.

  2. 2.

    A length histogram, which shows the distribution of the segment’s length.

  3. 3.

    A compactness vs. eccentricity heatmap, which shows the general segment shape distribution.

  4. 4.

    Compactness vs. eccentricity examples, which show one example segment for each bin.

  5. 5.

    The final segmented result of the entire video, which shows all the identified segments, in the video.

  6. 6.

    One example frame from the video.

  7. 7.

    A color histogram that shows the segment color distribution.

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Lerner, J., Shimshoni, I. Thin section analysis for ceramic petrography using motion analysis and segmentation techniques. Machine Vision and Applications 33, 70 (2022). https://doi.org/10.1007/s00138-022-01324-8

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