Data-inspired advances in geometric measure theory: generalized surface and shape metrics
Sharif Ibrahim

TL;DR
This paper advances computational geometric measure theory by developing polynomial-time algorithms for flat norm distances, proving integrality properties, and applying shape signatures for shape reconstruction from data.
Contribution
It introduces a discretized flat norm approach with guaranteed integrality and applies nonasymptotic densities for shape analysis, bridging theory and practical data analysis.
Findings
Flat norm can be computed via linear programming in polynomial time.
Discretized flat norm solutions are integral under certain topological conditions.
Single-radius shape signatures can reconstruct polygons and smooth curves.
Abstract
Modern geometric measure theory, developed largely to solve the Plateau problem, has generated a great deal of technical machinery which is unfortunately regarded as inaccessible by outsiders. Some of its tools (e.g., flat norm distance and decomposition in generalized surface space) hold interest from a theoretical perspective but computational infeasibility prevented practical use. Others, like nonasymptotic densities as shape signatures, have been developed independently for data analysis (e.g., the integral area invariant). The flat norm measures distance between currents (generalized surfaces) by decomposing them in a way that is robust to noise. The simplicial deformation theorem shows currents can be approximated on a simplicial complex, generalizing the classical cubical deformation theorem and proving sharper bounds than Sullivan's convex cellular deformation theorem.…
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Taxonomy
TopicsTopological and Geometric Data Analysis · Digital Image Processing Techniques · Advanced Numerical Analysis Techniques
