Hypothesis testing for fourier based edge detection methods

A. Petersen, Anne Gelb, R. Eubank

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Edge detection is an essential task in image processing. In some applications, such as Magnetic Resonance Imaging, the information about an image is available only through its frequency (Fourier) data. In this case, edge detection is particularly challenging, as it requires extracting local information from global data. The problem is exacerbated when the data are noisy. This paper proposes a new edge detection algorithm which combines the concentration edge detection method (Gelb and Tadmor in Appl. Comput. Harmon. Anal. 7:101-135, 1999) with statistical hypothesis testing. The result is a method that achieves a high probability of detection while maintaining a low probability of false detection.

Original languageEnglish (US)
Pages (from-to)608-630
Number of pages23
JournalJournal of Scientific Computing
Issue number3
StatePublished - Jun 2012


  • Edge detection
  • False discovery rate
  • Fourier data
  • Hypothesis testing
  • Parameter estimation

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Numerical Analysis
  • Engineering(all)
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics


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