Restricted Hysteresis Reduce Redundancy in Edge Detection

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DOI: 10.4236/jsip.2013.43B028    3,813 Downloads   4,912 Views   Citations


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.

Cite this paper

B. Li, U. Söderström, S. Réhman and H. Li, "Restricted Hysteresis Reduce Redundancy in Edge Detection," Journal of Signal and Information Processing, Vol. 4 No. 3B, 2013, pp. 158-163. doi: 10.4236/jsip.2013.43B028.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, 1986, pp. 679-698.doi:10.1109/TPAMI.1986.4767851
[2] V. Torre and T. A. Poggio, “On Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 2, 1986, p.147. doi:10.1109/TPAMI.1986.4767769
[3] D. Ziou and S. Tabbone, “Edge Detection Techniques: An Overview,” Pattern Recognition & Image Analysis, Vol. 8, No. 4, 1998, pp. 537-559.
[4] A. Jevtic and B. Li. “Ant Algorithms for Adaptive Edge Detection”, In: T. Abrão, Ed., Search Algorithms for Engineering Optimization, ISBN: 978-953-51-0983-9, InTech. doi:10.5772/52792
[5] P. Bao, D. Zhang and W. Xiaolin, "Canny Edge Detection Enhancement by Scale Multiplication," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.9, 2005, pp.1485-1490. doi: 10.1109/TPAMI.2005.17321
[6] 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.
[7] F. Devernay, “A Non-Maxima Suppression Method for Edge Detection with Sub-Pixel Accuracy,” INRIA Research Report 11/1995; 2724.
[8] G. Bradski, The Open CV Library,,accessed 2013-04-25.
[9] R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” 3rd Edition, Addison-Wesley, Reading, MA, 2008.
[10] D. A. Forsyth and J. Ponce, “Computer Vision: A Modern Approach,”1st Edition, Prentice Hall, August 2002.
[11] J. R. Parker, “Algorithms for Image Processing and Computer Vision,” 2nd Edition, John Wiley & Sons, Nov 29, 2010.
[12] D. H. Ballard, "Generalizing the Hough Transform to Detect Arbitrary Shapes," Pattern Recognition, Vol. 13, No. 2, 1981, pp. 111-122. doi:10.1016/0031-3203(81)90009-1
[13] C. F. Olson and D. P. Huttenlocher, "Automatic Target Recognition by Matching Oriented Edge Pixels," IEEE Transactions on Image Processing, Vol. 6, No.1, Jan 1997, pp. 103-113. doi:10.1109/83.552100
[14] G. Borgefors, "Hierarchical Chamfer Matching: AParametric Edge Matching Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 6, 1988, pp. 849-865. doi:10.1109/34.9107
[15] Y. Gao and M. K. H. Leung, "Face Recognition Using Line Edge Map," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 6, 2002, pp. 764-779, doi:10.1109/TPAMI.2002.1008383
[16] M. Zhu and A. M. Martinez, "Subclass Discriminant Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 8, 2006, pp. 1274-1286. doi:org/10.1109/TPAMI.2006.172
[17] G. Papari and N. Petkov, “Edge and Line Oriented Contour Detection: State of the art,” Image and Vision Computing, Vol. 29, No. 2-3, 2011, pp. 79-103.

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