Locating topological structures in digital images via local homology

TL;DR

This paper introduces an efficient local homology method to identify topological structures in digital images, enhancing the detection of local holes and features using topological data analysis techniques.

Contribution

It proposes a novel approach combining space division and local homology to locate topological features in digital images more effectively.

Findings

Successfully applied local homology to digital images

Improved detection of local holes in topological structures

Demonstrated computational efficiency in topological analysis

Abstract

Topological data analysis (TDA) is a rising branch in modern applied mathematics. It extracts topological structures as features of a given space and uses these features to analyze digital data. Persistent homology, one of the central tools in TDA, defines persistence barcodes to measure the changes in local topologies among deformations of topological spaces. Although local spatial changes characterize barcodes, it is hard to detect the locations of corresponding structures of barcodes due to computational limitations. The paper provides an efficient and concise way to divide the underlying space and applies the local homology of the divided system to approximate the locations of local holes in the based space. We also demonstrate this local homology framework on digital images.