This work proposes a change-based segmentation method for applications to cultural heritage (CH) imaging to perform monitoring and assess changes at each surface point. It can be used as a support or component of the 3D sensors to analyze surface geometry changes. In this research, we proposed a new method to identify surface changes employing segmentation based on 3D geometrical data acquired at different time intervals. The geometrical comparison was performed by calculating point-to-point Euclidean distances for each pair of surface points between the target and source geometry models. Four other methods for local distance measurement were proposed and tested. In the segmentation method, we analyze the local histograms of the distances between the measuring points of the source and target models. Then the parameters of these histograms are determined, and predefined classes are assigned to target surface points. The proposed methodology was evaluated by considering two different case studies of restoration issues on CH surfaces and monitoring them over time. The results were presented with a colormap visualization for each category of the detected change in the analysis. The proposed segmentation method will help in the field of conservation and restoration for the documentation and quantification of geometrical surface change information. This analysis can help in decision-making for the assessment of damage and potential prevention of further damage, and the interpretation of measurement results.
The three-dimensional digitization of the cultural heritage objects during different stages of the conservation process is an important tool for objective documentation. Further data analysis is also important to monitor, estimate and understand any possible change as accurately as possible. In this work, the cultural heritage (CH) objects were selected for 3D scanning, analysis and visualisation of the change or degradation on their surface over time. The main goal of this work is to develop analysis, and visualization methods for CH object to assess local change in their surface geometry to support conservation processes documentation. The analysis was based on geometrical analysis of change in global distance between before and after chemical cleaning for a chosen object. The new local neighborhood distance histogram has been proposed as a local measure of surface change based on optimized k-neighborhood search algorithm to assess the local geometry change of a focus point.
Fast track article for IS&T International Symposium on Electronic Imaging 2021: 3D Imaging and Applications proceedings.
Fast track article for IS&T International Symposium on Electronic Imaging 2021: 3D Imaging and Applications 2021 proceedings.
Efficient monitoring of large-scale cultural heritage monuments is of great interest in understanding alteration mechanisms that lead to substantial decision making for safeguarding them. This work presents a methodological approach for monitoring areas of the wall of King Jan III' palace Wilanów (Poland), where due to weathering the documentation of the surface changes became indispensable. Data from 3D scanning and registered data, representing different time intervals, were analysed to determine surface geometry changes. The goal was to develop a methodology which could detect each surface point based on grouping it with similar behaviour of local geometry changes. Further analyses, to determine the direction of change and the local geometry, were performed with the goal to extract information on the behaviour of changes and quantify them. By assigning a gradual scale, for the calculated displacement information regarding alterations can be visualized and measured. The methodology was based on calculating initially the general direction of change and then analysing the local geometry changes based on considering neighbouring points obtained from the spherical search kernel of each surface point. The neighbouring points were then compared to weathered dataset point cloud, by calculating the Euclidean distance and data segmentation, based on histograms of local distance analysis, was produced. Each histogram was fitted to the kernel distribution curve and bandwidth parameters, with similar points, were identified and segmented for changes corresponding to different time intervals. Initial evaluation on the case study shows the ability of the proposed methodology to detect even minor surface displacements.
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