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D. R. Martin, C. C. Fowlkesand and J. Malik, "Learning To Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 5, 2004, pp. 530-549. doi: 10.1109/TPAMI.2004.127391822.

has been cited by the following article:

• TITLE: Restricted Hysteresis Reduce Redundancy in Edge Detection

  AUTHORS: Bo Li, Ulrik Söderström, Shafiq Ur Réhman, Haibo Li

  KEYWORDS: Edge Detection; Hysteresis; Non-Maximum Suppression; Redundancy

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.4 No.3B, October 17, 2013

  ABSTRACT: In edge detection algorithms, there is a common redundancy problem, especially when the gradient direction is close to -135°, -45°, 45°, and 135°. Double edge effect appears on the edges around these directions. This is caused by the discrete calculation of non-maximum suppression. Many algorithms use edge points as feature for further task such as line extraction, curve detection, matching and recognition. Redundancy is a very important factor of algorithm speed and accuracy. We find that most edge detection algorithms have redundancy of 50% in the worst case and 0% in the best case depending on the edge direction distribution. The common redundancy rate on natural images is approximately between 15% and 20%. Based on Canny’s framework, we propose a restriction in the hysteresis step. Our experiment shows that proposed restricted hysteresis reduce the redundancy successfully.