skip to main content
10.1145/1057432.1057449acmotherconferencesArticle/Chapter ViewAbstractPublication PagessgpConference Proceedingsconference-collections
Article

Persistence barcodes for shapes

Published: 08 July 2004 Publication History

Abstract

In this paper, we initiate a study of shape description and classification via the application of persistent homology to two tangential constructions on geometric objects. Our techniques combine the differentiating power of geometry with the classifying power of topology. The homology of our first construction, the tangent complex, can distinguish between topologically identical shapes with different "sharp" features, such as corners. To capture "soft" curvature-dependent features, we define a second complex, the filtered tangent complex, obtained by parametrizing a family of increasing subcomplexes of the tangent complex. Applying persistent homology, we obtain a shape descriptor, called a barcode, that is a finite union of intervals. We define a metric over the space of such intervals, arriving at a continuous invariant that reflects the geometric properties of shapes. We illustrate the power of our methods through a number of detailed studies of parametrized families of mathematical shapes.

References

[1]
{BG92} Berger M., Gastiaux B.: G�omtri� diff�rentitelle: vari�t�s, courbes et surfaces. Math�matiques Presses Universitaires de France, Paris, France, 1992.
[2]
{Boo91} Bookstein F.: Morphometric Tools for Landmark Data. Cambridge Univ. Press, Cambridge, UK, 1991.
[3]
{CdS03} Carlsson G., De Silva V.: A geometric framework for sparse matrix problems. Advances in Applied Mathematics (2003). (To appear).
[4]
{CLRS01} Cormen T. H., Leiserson C. E., Rivest R. L., Stein C.: Introduction to Algorithms. The MIT Press, Cambridge, MA, 2001.
[5]
{CZCG04} Collins A., Zomorodian A., Carlsson G., Guibas L.: A barcode shape descriptor for curve point cloud data, 2004. To appear in Proc. Symposium on Point-Based Graphics.
[6]
{DK90} Donaldson S. K., Kronheimer P. B.: The Geometry of Four-Manifolds. The Clarendon Press, New York, NY, 1990.
[7]
{ELZ02} Edelsbrunner H., Letscher D., Zomorodian A.: Topological persistence and simplification. Discrete Comput. Geom. 28 (2002), 511--533.
[8]
{Fan90} Fan T.-J.: Describing and Recognizing 3D Objects Using Surface Properties. Springer-Verlag, New York, NY, 1990.
[9]
{Fed69} Federer H.: Geometric Measure Theory. vol. 153 of Die Grundlehren der Mathematischen Wissenschaften. Springer-Verlag, New York, NY, 1969.
[10]
{Fis89} Fisher R. B.: From Surfaces to Objects: Computer Vision and Three-Dimensional Scene Analysis. John Wiley and Sons, Inc., New York, NY, 1989.
[11]
{GH81} Greenberg M. J., Harper J. R.: Algebraic Topology: A First Course, vol. 58 of Mathematics Lecture Note Series. Benjamin/Cummings Publishing Co., Reading, MA, 1981.
[12]
{GT89} Gabow H. N., Tarjan R. E.: Faster scaling algorithm for network problems. SIAM J. Comput. 18 (1989), 1013--1036.
[13]
{Hat01} Hatcher A.: Algebraic Topology. Cambridge Univ. Press, Cambridge, UK, 2001.
[14]
{KBCL99} Kendall D., Barden D., Carne T., Le H.: Shape and Shape Theory. John Wiley and Sons, Inc., New York, NY, 1999.
[15]
{Kuh55} Kuhn H. W.: The Hungarian method for the assignment problem. Naval Research Logistics Quarterly 2 (1955), 83--97.
[16]
{LPM01} Lee A. B., Pedersen K. S., Mumford D.: The non-linear statistics of high contrast patches in natural images. Tech. rep., Brown University, 2001. Available online.
[17]
{Mil63} Milnor J.: Morse Theory, vol. 51 of Annals of Mathematical Studies. Princeton Univ. Press, Princeton, NJ, 1963.
[18]
{MS74} Milnor J., Stasheff J. D.: Characteristic Classes, vol. 76 of Annals of Mathematical Studies. Princeton Univ. Press, Princeton, NJ, 1974.
[19]
{ZC04} Zomorodian A., Carlsson G.: Computing topological persistence, 2004. To appear in Proc. 20th Ann. ACM Sympos. Comput. Geom.
[20]
{ZY96} Zhu S., Yuille A.: Forms: A flexible object recognition and modeling system. Int. J. Comp. Vision 20, 3 (1996), 187--212.

Cited By

View all
  • (2024)Mild explocivity, persistent homology and cryptocurrencies' bubbles: An empirical exerciseAIMS Mathematics10.3934/math.20240459:1(896-917)Online publication date: 2024
  • (2024)ChatGPT for computational topologyFoundations of Data Science10.3934/fods.2024009(0-0)Online publication date: 2024
  • (2024)GLiDR: Topologically Regularized Graph Generative Network for Sparse LiDAR Point Clouds2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01435(15152-15161)Online publication date: 16-Jun-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
SGP '04: Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
July 2004
259 pages
ISBN:3905673134
DOI:10.1145/1057432
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • EUROGRAPHICS: The European Association for Computer Graphics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2004

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Article

Conference

SGP04
Sponsor:
  • EUROGRAPHICS
SGP04: Symposium on Geometry Processing
July 8 - 10, 2004
Nice, France

Acceptance Rates

Overall Acceptance Rate 64 of 240 submissions, 27%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)91
  • Downloads (Last 6 weeks)11
Reflects downloads up to 19 Oct 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Mild explocivity, persistent homology and cryptocurrencies' bubbles: An empirical exerciseAIMS Mathematics10.3934/math.20240459:1(896-917)Online publication date: 2024
  • (2024)ChatGPT for computational topologyFoundations of Data Science10.3934/fods.2024009(0-0)Online publication date: 2024
  • (2024)GLiDR: Topologically Regularized Graph Generative Network for Sparse LiDAR Point Clouds2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01435(15152-15161)Online publication date: 16-Jun-2024
  • (2024)Persistent homology based Bottleneck distance in hypergraph productsApplied Network Science10.1007/s41109-024-00617-39:1Online publication date: 23-Apr-2024
  • (2024)Topological Analysis of�Seizure-Induced Changes in�Brain Hierarchy Through Effective ConnectivityTopology- and Graph-Informed Imaging Informatics10.1007/978-3-031-73967-5_13(134-145)Online publication date: 11-Oct-2024
  • (2023)Towards a persistence diagram that is robust to noise and varied densitiesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620171(41952-41972)Online publication date: 23-Jul-2023
  • (2023)Describing and Modeling Rough Composites Surfaces by Using Topological Data Analysis and Fractional Brownian MotionPolymers10.3390/polym1506144915:6(1449)Online publication date: 14-Mar-2023
  • (2023)Sobre el an�lisis de la forma de los datos: un nuevo paradigma en ciencia de datosRevista Ciencia UANL10.29105/cienciauanl22.96-422:96(54-59)Online publication date: 26-Oct-2023
  • (2023)Geometry-Aware Merge Tree Comparisons for Time-Varying Data With Interleaving DistancesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.316334929:8(3489-3506)Online publication date: 1-Aug-2023
  • (2023)Topological Simplifications of HypergraphsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.315389529:7(3209-3225)Online publication date: 1-Jul-2023
  • Show More Cited By

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media