Back to articles
Article
Volume: 34 | Article ID: 3DIA-213
Image
Segmentation in application to deformation analysis of cultural heritage surfaces
  DOI :  10.2352/EI.2022.34.17.3DIA-213  Published OnlineJanuary 2022
Abstract
Abstract

The geometry comparison is promising for the deformation assessment of cultural heritage (CH) surfaces over the decade. In this work, the potential reliability of the developed changed-based segmentation method was explored in quantifying the deformation on an object. The proposed method was tested using two parts of a single model, considering one ideal in shape and the other part is deformed over time. This study explains how deformation using changed-based segmentation can be identified successfully with millimeter level accuracy in the measurement and inform future conservation treatment. The method is insensitive to the noise of surfaces and the cross-time alignment of two models with a known reference of no change. The technique allows identifying the deterioration based on deformation detection with clear colormap visualization and its significance as a preventive measure without using a physical marker as a reference. The threshold values were fed to the method based on the known respect of no change and quantified as minor and major based on the object's size. The presented result in this paper suggests that the method can be effectively used to enhance 3D documentation of monitoring both indoor and outdoor environments and perform preventive interventions irrespective of the object's size.

Subject Areas :
Views 264
Downloads 52
 articleview.views 264
 articleview.downloads 52
  Cite this article 

Sunita Saha, Robert Sitnik, "Segmentation in application to deformation analysis of cultural heritage surfacesin Proc. IS&T Int’l. Symp. on Electronic Imaging: 3D Imaging and Applications,  2022,  pp 213-1 - 213-5,  https://doi.org/10.2352/EI.2022.34.17.3DIA-213

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2022
ei
Electronic Imaging
2470-1173
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane, Springfield, VA 22151 USA