Modified Hough transform for images containing many textured regions

Yun Seok Lee, Seung H. Yoo, Chang-Sung Jeong

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

Abstract

Images which have a lot of textured regions make the result of Hough transform (HT) very poor. This paper presents an improved HT that deals with such a textured image by diminishing the effect of noise edges and using weighted voting score. The method first eliminates the noise edges resulted from textured regions; then, the method casts votes for edges upon the accumulator array with weight score in accordance with the number of sequential votes. Our modified HT is efficient in that it produces important lines first such as verge of building, avoiding improper lines taken from the noise edges.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages824-833
Number of pages10
Volume4259 LNAI
Publication statusPublished - 2006 Dec 11
Event5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006 - Kobe, Japan
Duration: 2006 Nov 62006 Nov 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4259 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006
CountryJapan
CityKobe
Period06/11/606/11/8

Fingerprint

Hough Transform
Hough transforms
Noise
Vote
Line
Diminishing
Politics
Voting
Eliminate
Weights and Measures

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Lee, Y. S., Yoo, S. H., & Jeong, C-S. (2006). Modified Hough transform for images containing many textured regions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4259 LNAI, pp. 824-833). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4259 LNAI).

Modified Hough transform for images containing many textured regions. / Lee, Yun Seok; Yoo, Seung H.; Jeong, Chang-Sung.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4259 LNAI 2006. p. 824-833 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4259 LNAI).

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

Lee, YS, Yoo, SH & Jeong, C-S 2006, Modified Hough transform for images containing many textured regions. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4259 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4259 LNAI, pp. 824-833, 5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006, Kobe, Japan, 06/11/6.
Lee YS, Yoo SH, Jeong C-S. Modified Hough transform for images containing many textured regions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4259 LNAI. 2006. p. 824-833. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Lee, Yun Seok ; Yoo, Seung H. ; Jeong, Chang-Sung. / Modified Hough transform for images containing many textured regions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4259 LNAI 2006. pp. 824-833 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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