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  • Research Article
  • Open Access

Evaluating Edge Detection through Boundary Detection

EURASIP Journal on Advances in Signal Processing20062006:076278

  • Received: 27 February 2005
  • Accepted: 30 June 2005
  • Published:


Edge detection has been widely used in computer vision and image processing. However, the performance evaluation of the edge-detection results is still a challenging problem. A major dilemma in edge-detection evaluation is the difficulty to balance the objectivity and generality: a general-purpose edge-detection evaluation independent of specific applications is usually not well defined, while an evaluation on a specific application has weak generality. Aiming at addressing this dilemma, this paper presents new evaluation methodology and a framework in which edge detection is evaluated through boundary detection, that is, the likelihood of retrieving the full object boundaries from this edge-detection output. Such a likelihood, we believe, reflects the performance of edge detection in many applications since boundary detection is the direct and natural goal of edge detection. In this framework, we use the newly developed ratio-contour algorithm to group the detected edges into closed boundaries. We also collect a large data set ( ) of real images with unambiguous ground-truth boundaries for evaluation. Five edge detectors (Sobel, LoG, Canny, Rothwell, and Edison) are evaluated in this paper and we find that the current edge-detection performance still has scope for improvement by choosing appropriate detectors and detector parameters.


  • Computer Vision
  • Large Data
  • Quantum Information
  • Edge Detection
  • Challenging Problem

Authors’ Affiliations

Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA


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