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Title: The Impact of Changes in Resolution on the Persistent Homology of Images
Digital 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.  more » « less
Award ID(s):
1928930
NSF-PAR ID:
10351334
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
2021 IEEE International Conference on Big Data (Big Data)
Page Range / eLocation ID:
3824 to 3834
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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