Abstract This paper presents the application of two key stages in Cultural Heritage (CH) analysis: cross-time registration and change detection, aimed at automatically identifying subtle geometric variations in CH objects. The proposed method addresses the challenge of manually aligning and detecting differences among large collections of 3D-digitized objects, which is both time-consuming and error-prone. The method combines CrossTimeReg deep learning technique for automatic registration and the Change-Based-Segmentation method for identifying differences and segmented changes. We applied this method to two ceramic sculptures, titled Zephyr and Flora, from the Museum of King Jan III’s Palace at Wilanów, Poland, and validated the results with the CH scientific experts of the museum. The results demonstrated that our method effectively detected geometric variations resulting from potential alterations or restorations, which are crucial for verifying the authenticity of artifacts. Additionally, the method facilitated comparative analysis, enabling researchers to examine similar objects and establish connections, origins, and historical significance. The technique also proved useful in analyzing changes in the geometry of the same object over time due to destructive factors.

Image processing workflows typically consist of a series of different functions, each working with “image” inputs and outputs in an abstract sense. However, the specific in-memory representation of images differs between and sometimes within libraries. Conversion is therefore necessary when integrating functions from several sources into a single program. The conversion process forces users to consider low-level implementation details, including data types, color channels, channel order, minibatch layout, memory locations, and pixel intensity ranges. Specifically in the case of visual programming languages (VPLs), this distracts from high- level operations. We introduce im2im, a Python library that automates the conversion of in-memory image representations. The central concept of this library is a knowledge graph that describes image representations and how to convert between them. The system queries this knowledge graph to generate the conversion code and execute it, converting an image to the desired representation. The effectiveness of the approach is evaluated through two case studies in VPLs. In each case, we compared a workflow created in a basic blockbased VPL with the same workflow enhanced using im2im. These evaluations show that im2im automates type conversions and eliminates the need for manual intervention. Additionally, we compared the overhead of using explicit intermediate representations versus im2im, both of which avoid manual type conversions in VPLs. The results indicate that im2im generates only the necessary conversions, avoiding the runtime overhead associated with converting to and from intermediate formats. A performance comparison between the step-by-step approach used by im2im and a single-function approach demonstrates that the overhead introduced by im2im does not impact practical usability. While focused on block-based VPLs, im2im can be generalized to other VPLs and textual programming environments. Its principles are also applicable to domains other than images. The source code and analyses are available via GitHub.