A Sheaf and Topology Approach to Generating Local Branch Numbers in Digital Images
This work addresses the problem of capturing local consistency and merging relations in persistent homology for researchers and practitioners using TDA, offering an incremental theoretical advancement.
This paper introduces a theoretical framework combining topological data analysis (TDA) and sheaf theory to extract finer local consistency information from persistent homology (PH) that is not captured by persistence diagrams (PDs). The proposed theory is demonstrated to identify branch numbers of local objects in digital images.
This paper concerns a theoretical approach that combines topological data analysis (TDA) and sheaf theory. Topological data analysis, a rising field in mathematics and computer science, concerns the shape of the data and has been proven effective in many scientific disciplines. Sheaf theory, a mathematics subject in algebraic geometry, provides a framework for describing the local consistency in geometric objects. Persistent homology (PH) is one of the main driving forces in TDA, and the idea is to track changes of geometric objects at different scales. The persistence diagram (PD) summarizes the information of PH in the form of a multi-set. While PD provides useful information about the underlying objects, it lacks fine relations about the local consistency of specific pairs of generators in PD, such as the merging relation between two connected components in the PH. The sheaf structure provides a novel point of view for describing the merging relation of local objects in PH. It is the goal of this paper to establish a theoretic framework that utilizes the sheaf theory to uncover finer information from the PH. We also show that the proposed theory can be applied to identify the branch numbers of local objects in digital images.