A NEW APPROXIMATION ALGORITHM FOR THE MATCHING DISTANCE IN MULTIDIMENSIONAL PERSISTENCE  

作　　者：Andrea Cerri Patrizio Frosini 

机构地区：[1]FST-Fom Software Technology,Italy [2]Dipartimento di Matematica,Universita di Bologna,Italy

出　　处：《Journal of Computational Mathematics》2020年第2期291-309,共19页计算数学（英文）

基　　金：the Austrian Science Fund(FWF)grant no.P20134-N13;the CNR research activity ICT.PIO.009 and the EU project IQmulus(EU FP7-ICT-2011-318787).

摘　　要：Topological Persistence has proven to be a promising framework for dealing with problems concerning shape analysis and comparison.In this contexts,it was originally introduced by taking into account 1-dimensional properties of shapes,modeled by real-valued functions.More recently,Topological Persistence has been generalized to consider multidimensional proper ties of shapes,coded by vect or-valued functions.This extension has led to int roduce suitable shape descrip tors,named the multidimensional persis tence Betti numbers functions,and a distance to compare them,the so-called multidimensional matching distance.In this paper we propose a new computational framework to deal with the multidimensional matching distance.We start by proving some new theoretical results,and then we use them to formulate an algorithm for computing such a distance up to an arbitrary threshold error.

关 键 词：Multidimensional persistent topology Matching distance Shape comparison. 

分 类 号：O242.2[理学—计算数学]