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Thresholding in edge detection: a statistical approach.

Rishi R Rakesh1, Probal Chaudhuri, C A Murthy

  • 1American Express, Gurgaon, India. rishi_r_r@hotmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 15, 2005
PubMed
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This study introduces a novel edge detection method using statistical principles for thresholding. The proposed local thresholding approach offers stable, statistically interpretable results comparable to existing edge detectors.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Traditional edge detection methods often rely on user-defined parameters chosen arbitrarily.
  • This subjectivity can lead to inconsistent and suboptimal edge detection results.
  • A need exists for more robust and statistically grounded edge detection techniques.

Purpose of the Study:

  • To propose a new edge detection algorithm that utilizes statistical principles for thresholding.
  • To introduce a local thresholding method based on the statistical variability of gradient vectors.
  • To evaluate the performance and stability of the proposed edge detector against established methods.

Main Methods:

  • A novel edge detection approach is presented, employing statistical principles for threshold determination.

Related Experiment Videos

  • Local standardization of thresholds is performed for each pixel, considering the gradient vector's statistical variability.
  • A standardized statistic derived from the gradient vector at each pixel determines its edge pixel eligibility.

    • Main Results:

    • The proposed edge detector yields results comparable to well-known existing edge detection algorithms.
    • Input parameter values for the proposed method demonstrate greater stability compared to other detectors.
    • The parameter values in the proposed detector possess a clear statistical interpretation.

    Conclusions:

    • The developed edge detection method offers a statistically robust and stable alternative to conventional techniques.
    • Local thresholding based on gradient vector statistics provides reliable edge identification.
    • The method's stability and interpretability make it a valuable contribution to image processing.