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Comparison of edge detection algorithms using a structure from motion task.

M C Shin1, D B Goldgof, K W Bowyer

  • 1Dept. of Comput. Sci. & Eng., South Florida Univ., Tampa, FL.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 5, 2008
PubMed
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This study evaluated edge detection algorithms using structure from motion (SFM). The Canny and Heitger detectors performed best, showing accurate structure and motion recovery in image sequences.

Area of Science:

  • Computer Vision
  • Image Processing
  • Robotics

Background:

  • Edge detection is crucial for computer vision tasks.
  • Evaluating edge detector performance is challenging.
  • Structure from Motion (SFM) offers a robust evaluation framework.

Purpose of the Study:

  • To evaluate the performance of eight different edge detection algorithms.
  • To assess edge detector efficacy using the Structure from Motion (SFM) task.
  • To correlate pixel-level metrics with SFM-based performance evaluations.

Main Methods:

  • Utilized a Structure from Motion (SFM) 'black box' approach.
  • Employed real-world image sequences with ground truth data.
  • Evaluated eight distinct edge detection algorithms from existing literature.

Related Experiment Videos

Main Results:

  • SFM-based performance ratings correlated well with pixel-level metrics.
  • The Canny edge detector demonstrated high performance.
  • The Heitger edge detector also exhibited superior performance.

Conclusions:

  • Edge detector performance can be reliably assessed via SFM.
  • Canny and Heitger detectors are recommended for SFM applications.
  • Pixel-level and SFM-based evaluations provide consistent insights into edge detector quality.