Detection of general edges and keypoints

L. Rosenthaler, F. Heitger, O. Kübler, R. von der Heydt

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

Abstract

A computational framework for extracting (1) edges with an arbitrary profile function and (2) keypoints such as corners, vertices and terminations is presented. Using oriented filters with even and odd symmetry we combine their convolution outputs to oriented energy resulting in a unified representation of edges, lines and combinations thereof. We derive an “edge quality” measure which allows to test the validity of a general edge model. A detection scheme for keypoints is proposed based on an analysis of oriented energy channels using differential geometry.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 1992 - 2nd European Conference on Computer Vision, Proceedings
EditorsGiulio Sandini
PublisherSpringer Verlag
Pages78-86
Number of pages9
ISBN (Print)9783540554264
DOIs
StatePublished - Jan 1 1992
Externally publishedYes
Event2nd European Conference on Computer Vision, ECCV 1992 - Santa Margherita Ligure, Italy
Duration: May 19 1992May 22 1992

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume588 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd European Conference on Computer Vision, ECCV 1992
CountryItaly
CitySanta Margherita Ligure
Period5/19/925/22/92

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Detection of general edges and keypoints'. Together they form a unique fingerprint.

  • Cite this

    Rosenthaler, L., Heitger, F., Kübler, O., & von der Heydt, R. (1992). Detection of general edges and keypoints. In G. Sandini (Ed.), Computer Vision - ECCV 1992 - 2nd European Conference on Computer Vision, Proceedings (pp. 78-86). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 588 LNCS). Springer Verlag. https://doi.org/10.1007/3-540-55426-2_10