IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v129y2014icp171-185.html
   My bibliography  Save this article

On the estimation of the medial axis and inner parallel body

Author

Listed:
  • Cuevas, Antonio
  • Llop, Pamela
  • Pateiro-López, Beatriz

Abstract

The medial axis and the inner parallel body of a set C are different formal translations for the notions of the “central core” and the “bulk”, respectively, of C. On the basis of their applications in image analysis, both notions (and especially the first one) have been extensively studied in the literature, from different points of view. A modified version of the medial axis, called λ-medial axis, has been recently proposed in order to get better stability properties. The estimation of these relevant subsets from a random sample of points is a partially open problem which has been considered only very recently. Our aim is to show that standard, relatively simple, techniques of set estimation can provide natural, consistent, easy-to-implement estimators for both the λ-medial axis Mλ(C) and the inner parallel body Iλ(C) of C. The consistency of these estimators follows from two results of stability (i.e. continuity in the Hausdorff metric) of Mλ(C) and Iλ(C) obtained under some, not too restrictive, regularity assumptions on C. As a consequence, natural algorithms for the approximation of the λ-medial axis and the λ-inner parallel body can be derived. The whole approach could be useful for some practical problems in image analysis where estimation techniques are needed.

Suggested Citation

  • Cuevas, Antonio & Llop, Pamela & Pateiro-López, Beatriz, 2014. "On the estimation of the medial axis and inner parallel body," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 171-185.
  • Handle: RePEc:eee:jmvana:v:129:y:2014:i:c:p:171-185
    DOI: 10.1016/j.jmva.2014.04.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X14000943
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jmva.2014.04.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Christopher R. Genovese & Marco Perone-Pacifico & Isabella Verdinelli & Larry Wasserman, 2012. "The Geometry of Nonparametric Filament Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 788-799, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paula Saavedra-Nieves & Rosa M. Crujeiras, 2022. "Nonparametric estimation of directional highest density regions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 761-796, September.
    2. Kunhui Zhang & Yen-Chi Chen, 2021. "Refined Mode-Clustering via the Gradient of Slope," Stats, MDPI, vol. 4(2), pages 1-23, June.
    3. Pulkkinen, Seppo, 2015. "Ridge-based method for finding curvilinear structures from noisy data," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 89-109.
    4. Alberto Rodríguez-Casal & Paula Saavedra-Nieves, 2022. "Spatial distribution of invasive species: an extent of occurrence approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 416-441, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jmvana:v:129:y:2014:i:c:p:171-185. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.