CEDContours: A high speed contour detector for color images

Akinlar C.

IMAGE AND VISION COMPUTING, vol.54, pp.60-70, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 54
  • Publication Date: 2016
  • Doi Number: 10.1016/j.imavis.2016.08.010
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.60-70
  • Keywords: Edge detection, Contour detection, Image segmentation, Color Edge Drawing, BOUNDARY DETECTION, EDGE, MODEL
  • Anadolu University Affiliated: Yes


We propose a high-speed contour detector for color images that produces its contours as set of edge segments, each a chain of pixels. The proposed algorithm performs a multi-scale analysis of the input image by combining the edge segments produced by Color Edge Drawing (CED) at different scales; thus, the name CEDContours. We evaluate the performance of CEDContours both qualitatively by presenting visual experimental results, and quantitatively within the precision-recall framework of the Berkeley Segmentation Dataset and Benchmark (BSDS300 and BSDS500). Experimental results show that CEDContours with the DiZenzo gradient operator, named CEDContours-DiZenzo, surpasses many of the prominent contour detectors found in the literature (0.70 and 0.71 F-score for BSDS300 and BSDS500 respectively), and gives comparable results to the leading contour detectors, i.e., the global Probability of boundary ultrametric contour maps (gPb-ucm: 0.71 and 0.73), and the sparse code gradients (scg: 0.72 and 0.74), but runs up to 100 times faster than these contour detectors (700ms for 481x321 images as opposed to 40s for gPb-ucm and 70s for scg), making it suitable for high-speed image processing and computer vision applications. (C) 2016 Published by Elsevier B.V.