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

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.