Automatic selection of edge detector parameters based on spatial and statistical measures

Raz Koresh, Yitzhak Yitzhaky

Research output: Contribution to journalConference articlepeer-review

Abstract

The basic and widely used operation of edge detection in an image usually requires a prior step of setting the edge detector parameters (thresholds, blurring extent etc.). In real-world images this step is usually done subjectively by human observers. Finding the best detector parameters automatically is a problematic challenge because no absolute ground truth exists when real-world images are considered. However, the advantage of automatic processing over manual operations done by humans motivates the development of automatic detector parameter selection which will produce results agreeable by human observers. In this work we propose an automatic method for detector parameter selection which considers both, statistical correspondence of detection results produced from different detector parameters, and spatial correspondence between detected edge points, represented as saliency values. The method improves a recently developed technique that employs only statistical correspondence of detection results, and depends on the initial range of possible parameters. By incorporating saliency values in the statistical analysis, the detector parameters adaptively converge to best values. Automatic edge detection results show considerable improvement of the purely statistical method when a wrong initial parameter range is selected.

Original languageEnglish
Article number120
Pages (from-to)838-846
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5558
Issue numberPART 2
DOIs
StatePublished - 1 Dec 2004
EventApplications of Digital Image Processing XXVII - Denver, CO, United States
Duration: 2 Aug 20046 Aug 2004

Keywords

  • Edge detection evaluation
  • Edge detection parameters
  • Saliency

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