BEGL: boundary enhancement with Gaussian Loss for rock-art image segmentation

Heritage Science

Research

Yangyang Liu¹  Xiaofeng Wang¹  Mingquan Zhou²  Chuanping Bai³  Pengbo Zhou⁴ 

[1] School of Information Science and Technology, Northwest University, 1 Xuefu Avenue, Guodu Education Technology Industrial Park, Chang’an District, 710127, Xi’an, China;National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Northwest University, Xi’an, China;School of Information Science and Technology, Northwest University, 1 Xuefu Avenue, Guodu Education Technology Industrial Park, Chang’an District, 710127, Xi’an, China;National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Northwest University, Xi’an, China;Virtual Reality Research Center of Ministry of Education, Beijing Normal University, Beijing, China;School of Information Science and Technology, Northwest University, 1 Xuefu Avenue, Guodu Education Technology Industrial Park, Chang’an District, 710127, Xi’an, China;School of Mathematics and Computer science, Ningxia Normal University, Guyuan, China;Virtual Reality Research Center of Ministry of Education, Beijing Normal University, Beijing, China;

关键词: Petroglyph segmentation;    Boundary enhancement;    Cultural heritage;    Rock-art;   

DOI  :  10.1186/s40494-022-00857-5

 received in 2022-09-29, accepted in 2022-12-31,  发布年份 2022

来源: Springer

【 摘 要 】

Rock-art has been scratched, carved, and pecked into rock panels all over the world resulting in a huge number of engraved figures on natural rock surfaces that record ancient human life and culture. To preserve and recognize these valuable artifacts of human history, 2D digitization of rock surfaces has become a suitable approach due to the development of powerful 2D image processing techniques in recent years. In this article, we present a novel systematical framework for the segmentation of different petroglyph figures from 2D high-resolution images. The novel boundary enhancement with Gaussian loss (BEGL) function is proposed aiming at refining and smoothing the rock-arts boundaries in the basic UNet architecture. Several experiments on the 3D-pitoti dataset demonstrate that our proposed approach can achieve more accurate boundaries and superior results compared with other loss functions. The comprehensive framework of petroglyph segmentation from 2D high-resolution images provides the foundation for recognizing multiple petroglyph marks. The framework can then be extended to other cultural heritage digital protection domain easily.

【 授权许可】

CC BY   
© The Author(s) 2023

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