Local persistent homology of metric-measure spaces in image analysisMS16

Imaging data are frequently represented as point clouds or probability measures on surfaces, volumes or other metric spaces. We discuss how local persistent homology may be used to quantify the shape of such data in a robust, stable and often interpretable manner. We illustrate the method with an application to analysis of quantitative trait loci for plant morphology.

This presentation is part of Minisymposium “MS16 - Topological Image Analysis: Methods, Algorithms, Applications (3 parts)
organized by: Patrizio Frosini (University of Bologna) , Massimo Ferri (University of Bologna) , Claudia Landi (University of Modena and Reggio Emilia) .

Washington Mio (Florida State University)
image representation, topological data analysis