Some heuristics for the homological simplification problem

Erin W. Chambers; Tao Ju; David Letscher; Mao Li; Christopher Topp; Yajie Yan

Some heuristics for the homological simplification problem

Erin W. Chambers
, Tao Ju
, David Letscher
, Mao Li
, Christopher Topp
, Yajie Yan

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, we consider heuristic approaches for solving the homological simplification problem. While NP-Hard in general, we propose an algorithm that in practice significantly reduces topological noise from large datasets, such as those from medical or biological imaging.

Original language	English
Pages	353-359
Number of pages	7
State	Published - 2018
Event	30th Canadian Conference on Computational Geometry, CCCG 2018 - Winnipeg, Canada Duration: Aug 8 2018 → Aug 10 2018

Conference

Conference	30th Canadian Conference on Computational Geometry, CCCG 2018
Country/Territory	Canada
City	Winnipeg
Period	08/8/18 → 08/10/18

Cite this

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title = "Some heuristics for the homological simplification problem",

abstract = "In this paper, we consider heuristic approaches for solving the homological simplification problem. While NP-Hard in general, we propose an algorithm that in practice significantly reduces topological noise from large datasets, such as those from medical or biological imaging.",

author = "Chambers, \{Erin W.\} and Tao Ju and David Letscher and Mao Li and Christopher Topp and Yajie Yan",

note = "Publisher Copyright: {\textcopyright} 2018 Canadian Conference on Computational Geometry. All rights reserved.; 30th Canadian Conference on Computational Geometry, CCCG 2018 ; Conference date: 08-08-2018 Through 10-08-2018",

year = "2018",

language = "English",

pages = "353--359",

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TY - CONF

T1 - Some heuristics for the homological simplification problem

AU - Chambers, Erin W.

AU - Ju, Tao

AU - Letscher, David

AU - Li, Mao

AU - Topp, Christopher

AU - Yan, Yajie

PY - 2018

Y1 - 2018

N2 - In this paper, we consider heuristic approaches for solving the homological simplification problem. While NP-Hard in general, we propose an algorithm that in practice significantly reduces topological noise from large datasets, such as those from medical or biological imaging.

AB - In this paper, we consider heuristic approaches for solving the homological simplification problem. While NP-Hard in general, we propose an algorithm that in practice significantly reduces topological noise from large datasets, such as those from medical or biological imaging.

UR - https://www.scopus.com/pages/publications/85072890349

M3 - Paper

AN - SCOPUS:85072890349

SP - 353

EP - 359

T2 - 30th Canadian Conference on Computational Geometry, CCCG 2018

Y2 - 8 August 2018 through 10 August 2018

ER -

Some heuristics for the homological simplification problem

Abstract

Conference

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