N-point hough transform derived by geometric duality

Yoshihiko Mochizuki, Akihiko Torii, Atsushi Imiya

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

Abstract

We propose an extension of the three-point Randomized Hough transform. Our new Hough transform, which permits a continuous voting space without any cell-tessellation, uses both one-to-one mapping from an image plane to the parameter space and from the parameter space to the image plane. These transforms define a parameter from samples and a line from a parameter, respectively. Furthermore, we describe the classical Hough transform, the randomized Hough transform, the three-point randomized Hough transform and our new Hough transform in a generalized framework using geometric duality.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages148-157
Number of pages10
Volume4319 LNCS
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006 - Hsinchu
Duration: 2006 Dec 102006 Dec 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4319 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006
CityHsinchu
Period06/12/1006/12/13

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Mochizuki, Y., Torii, A., & Imiya, A. (2006). N-point hough transform derived by geometric duality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4319 LNCS, pp. 148-157). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4319 LNCS). https://doi.org/10.1007/11949534-15