Nonparametric tests for edge detection in noise

Alan C. Bovik, Thomas S Huang, David C. Munson

Research output: Contribution to journalArticle

Abstract

In this paper we describe three statistically motivated techniques for detecting edges in gray-level images. Two similar methods based on linear rank sums are described; specifically, the Wilcoxon and median statistics are implemented in a modified form and are found to perform effectively on both noisy and uncontaminated sample images. A novel approach based on fitting order statistics to the image data is also advanced. Numerous examples are given and computational requirements are examined.

Original languageEnglish (US)
Pages (from-to)209-219
Number of pages11
JournalPattern Recognition
Volume19
Issue number3
DOIs
StatePublished - Jan 1 1986

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Edge detection
Statistics

Keywords

  • Computer vision
  • Image processing
  • Linear rank sums
  • Nonparametrics
  • Order statistics
  • Statistical edge detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

Nonparametric tests for edge detection in noise. / Bovik, Alan C.; Huang, Thomas S; Munson, David C.

In: Pattern Recognition, Vol. 19, No. 3, 01.01.1986, p. 209-219.

Research output: Contribution to journalArticle

Bovik, Alan C. ; Huang, Thomas S ; Munson, David C. / Nonparametric tests for edge detection in noise. In: Pattern Recognition. 1986 ; Vol. 19, No. 3. pp. 209-219.
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