Heiss, TeresaTymochko, SarahStory, BrittanyGarin, AdelieBui, HoaBleile, BeaRobins, Vanessa2022-07-042022-07-042022-07-042021-01-0110.1109/BigData52589.2021.9671483https://infoscience.epfl.ch/handle/20.500.14299/189016WOS:000800559503126Digital images enable quantitative analysis of material properties at micro and macro length scales, but choosing an appropriate resolution when acquiring the image is challenging. A high resolution means longer image acquisition and larger data requirements for a given sample, but if the resolution is too low, significant information may be lost. This paper studies the impact of changes in resolution on persistent homology, a tool from topological data analysis that provides a signature of structure in an image across all length scales. Given prior information about a function, the geometry of an object, or its density distribution at a given resolution, we provide methods to select the coarsest resolution yielding results within an acceptable tolerance. We present numerical case studies for an illustrative synthetic example and samples from porous materials where the theoretical bounds are unknown.Computer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Science, Theory & MethodsComputer Scienceimage processingimage resolutionpersistent homologymicro-ct imagestopological persistencetext::conference output::conference proceedings::conference paper