Segmentation, Reconstruction, and Visualization of Ancient Inscriptions in 2.5D

Abstract

This article presents a new algorithm for the automated reconstruction and visualization of damaged ancient inscriptions. After reviewing current methods for enhancing incisions, a hybrid approach is adopted that combines advantages of 2D and 3D analytical techniques. A photogrammetric point cloud of an inscription is projected orthographically from an ideal vantage point, generating a 2.5D raster, including channels describing depth and surface derivatives. The next consideration is the obstacle to legibility posed by breaks in the ancient surface, which motivates the development of a new segmentation algorithm based on SLIC superpixels with region-merging adapted to operate on the geometry of the inscribed surface instead of color or intensity values. The algorithm classifies surface points by their likelihood of belonging to the uninscribed original plane, deliberate strokes, or breaks. Results are visualized in a manner suited for epigraphical analysis and publication through static images. Two case studies demonstrate the power and flexibility of this method, which has indicated changes to the reading of IG XIV 1, an early Greek text that has been debated for more than 150 years.