Parallel edge detection on a virtual hexagonal structure

Xiangjian He, Wenjing Jia, Qiang Wu, Tom Hintz

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This paper presents an edge detection method based on bilateral filtering taking into account both spatial closeness and intensity similarity of pixels in order to preserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on sampled images represented on a newly developed virtual hexagonal structure. Due to the compact and circular nature of the hexagonal lattice, a better quality edge map is obtained. We also present a parallel implementation for edge detection on the virtual hexagonal structure that significantly increases the computation speed.

Original languageEnglish
Title of host publicationAdvances in Grid and Pervasive Computing - Second International Conference, GPC 2007, Proceedings
PublisherSpringer Verlag
Pages751-756
Number of pages6
ISBN (Print)9783540723592
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2nd International Conference on Grid and Pervasive Computing, GPC 2007 - Paris, France
Duration: 2 May 20074 May 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4459 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Grid and Pervasive Computing, GPC 2007
Country/TerritoryFrance
CityParis
Period2/05/074/05/07

Keywords

  • Edge detection
  • Gaussian filtering
  • Hexagonal image structure
  • Image analysis
  • Parallel processing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Parallel edge detection on a virtual hexagonal structure'. Together they form a unique fingerprint.

Cite this