Scale-space clustering on the sphere

Yoshihiko Mochizuki, Atsushi Imiya, Kazuhiko Kawamoto, Tomoya Sakai, Akihiko Torii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present an algorithm for scale-space clustering of point cloud on the sphere using the methodology for the estimation of the density distribution of the points in the linear scale space. Our algorithm regards the union of observed point sets as an image defined by the delta functions located at the positions of the points on the sphere. A blurred version of this image has a deterministic structure which qualitatively represents the density distribution of the points in a point cloud on a manifold.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages417-424
Number of pages8
Volume8047 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2013
Event15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013 - York
Duration: 2013 Aug 272013 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8047 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013
CityYork
Period13/8/2713/8/29

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Mochizuki, Y., Imiya, A., Kawamoto, K., Sakai, T., & Torii, A. (2013). Scale-space clustering on the sphere. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 8047 LNCS, pp. 417-424). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8047 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-40261-6_50