Fuzzy Hough transform

Joon H. Han, L. Kóczy, Timothy Poston

Research output: Contribution to journalArticle

87 Citations (Scopus)

Abstract

To detect shapes in noisy data, the fuzzy Hough transform is introduced. This technique finds shapes by approximately fitting the data points, which avoids the spurious shapes detected when using the conventional Hough transform. An efficient implementation of this method is found in detecting lines and circles.

Original languageEnglish
Pages (from-to)649-658
Number of pages10
JournalPattern Recognition Letters
Volume15
Issue number7
DOIs
Publication statusPublished - 1994

Fingerprint

Hough transforms

Keywords

  • Distributed voting
  • Fuzzy geometry
  • Fuzzy Hough transform
  • Hough transform
  • Shape detection

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Fuzzy Hough transform. / Han, Joon H.; Kóczy, L.; Poston, Timothy.

In: Pattern Recognition Letters, Vol. 15, No. 7, 1994, p. 649-658.

Research output: Contribution to journalArticle

Han, Joon H. ; Kóczy, L. ; Poston, Timothy. / Fuzzy Hough transform. In: Pattern Recognition Letters. 1994 ; Vol. 15, No. 7. pp. 649-658.
@article{c370d30c1844424b80d25b484e487010,
title = "Fuzzy Hough transform",
abstract = "To detect shapes in noisy data, the fuzzy Hough transform is introduced. This technique finds shapes by approximately fitting the data points, which avoids the spurious shapes detected when using the conventional Hough transform. An efficient implementation of this method is found in detecting lines and circles.",
keywords = "Distributed voting, Fuzzy geometry, Fuzzy Hough transform, Hough transform, Shape detection",
author = "Han, {Joon H.} and L. K{\'o}czy and Timothy Poston",
year = "1994",
doi = "10.1016/0167-8655(94)90068-X",
language = "English",
volume = "15",
pages = "649--658",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
publisher = "Elsevier",
number = "7",

}

TY - JOUR

T1 - Fuzzy Hough transform

AU - Han, Joon H.

AU - Kóczy, L.

AU - Poston, Timothy

PY - 1994

Y1 - 1994

N2 - To detect shapes in noisy data, the fuzzy Hough transform is introduced. This technique finds shapes by approximately fitting the data points, which avoids the spurious shapes detected when using the conventional Hough transform. An efficient implementation of this method is found in detecting lines and circles.

AB - To detect shapes in noisy data, the fuzzy Hough transform is introduced. This technique finds shapes by approximately fitting the data points, which avoids the spurious shapes detected when using the conventional Hough transform. An efficient implementation of this method is found in detecting lines and circles.

KW - Distributed voting

KW - Fuzzy geometry

KW - Fuzzy Hough transform

KW - Hough transform

KW - Shape detection

UR - http://www.scopus.com/inward/record.url?scp=0028464052&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028464052&partnerID=8YFLogxK

U2 - 10.1016/0167-8655(94)90068-X

DO - 10.1016/0167-8655(94)90068-X

M3 - Article

VL - 15

SP - 649

EP - 658

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

IS - 7

ER -