Abstract
Ancient stelae are considered important historical sources. However, it is a challenge to recognize the inscriptions carved on stelae that have rough surfaces due to prolonged weathering. In this paper, we propose a deep learning-based method to extract engraved regions from the 3D scanned mesh of a stela. First, the uneven distribution of vertices in the mesh is transformed using a mesh subdivision method such that the vertices in the mesh are uniformly distributed. Then, surface features (depth, concave features, and local surface features) are extracted from the subdivided mesh. The depth represents the basic shape of the mesh and is obtained from the aligned mesh. The concave features effectively represent concave regions by using a Frangi filter, and the local surface features have the spin image technique applied to describe the fine shapes of neighboring vertices relative to a vertex. The mesh and the surface features are rasterized into feature images, and engraved regions are segmented from the feature images by using a FC-DenseNet. Our experiments confirm that the proposed method effectively extracts engraved regions of the inscriptions from the rough surface of a stela and it shows robustness to noisy and extremely abraded characters. The proposed method outperformed the second-best method, obtaining an F1 score, IoU, and SIRI of approximately 2.95%, 3.65%, and 7.53%, respectively.
Highlights
Archeological stelae recording events of the past have important value when it comes to studying political and cultural history
We propose a method to segment the engraved regions of 3D inscriptions using deep learning
The modified curvature-based relief extraction (MCRE) method applies Gaussian smoothing to reduce the noise of the principal curvature and increase the performance of the Frangi filter [21]
Summary
Archeological stelae recording events of the past have important value when it comes to studying political and cultural history. It is necessary to separate external factors such as colors and light from the surface and to effectively remove noise so as to extract only the engraved regions. Since rough surfaces do not follow a Gaussian distribution, and since these methods lack the ability to distinguish between engraved regions and noise, a lot of noise is extracted in the results. The 3D texture-based method [25] extracts texture features on local 3D surfaces, and classifies engraved regions using a support vector machine (SVM) [26]. The machine learning-based methods showed higher performance, compared to rule-based methods, but have limitations in that only local surface information is mainly used In other words, they cannot utilize a global context, mainly using the local context for classification of engraved regions and noise.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
