An Efficient Color Edge Detection Using the Mahalanobis Distance

  • Khongkraphan, Kittiya (Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus)
  • Received : 2013.09.16
  • Accepted : 2014.02.05
  • Published : 2014.12.31


The performance of edge detection often relies on its ability to correctly determine the dissimilarities of connected pixels. For grayscale images, the dissimilarity of two pixels is estimated by a scalar difference of their intensities and for color images, this is done by using the vector difference (color distance) of the three-color components. The Euclidean distance in the RGB color space typically measures a color distance. However, the RGB space is not suitable for edge detection since its color components do not coincide with the information human perception uses to separate objects from backgrounds. In this paper, we propose a novel method for color edge detection by taking advantage of the HSV color space and the Mahalanobis distance. The HSV space models colors in a manner similar to human perception. The Mahalanobis distance independently considers the hue, saturation, and lightness and gives them different degrees of contribution for the measurement of color distances. Therefore, our method is robust against the change of lightness as compared to previous approaches. Furthermore, we will introduce a noise-resistant technique for determining image gradients. Various experiments on simulated and real-world images show that our approach outperforms several existing methods, especially when the images vary in lightness or are corrupted by noise.



Supported by : Songkla University


  1. C.L. Novak and S.A. Shafer, "Color edge detection," in Proceedings of DARPA Image Understanding Workshop, Los Angeles, CA, 1987, pp.35-37.
  2. A. Koschan and M. Abidi, "Detection and classification of edges in color images," IEEE Signal Processing Magazine, vol. 22, no. 1, pp. 64-73, 2005.
  3. M. Hedley and H. Yan, "Segmentation of color images using spatial and color space information," Journal of Electronic Imaging, vol. 1, no. 4, pp. 374-380, 1992.
  4. S. Di Zenzo, "A note on the gradient of a multi-image," Computer Vision, Graphics, and Image Processing, vol. 33, no. 1, pp. 116-125, 1986.
  5. J. Fan, D. K. Yau, A. K. Elmagarmid, and W. G. Aref, "Automatic image segmentation by integrating color-edge extraction and seeded region growing," IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1454-1466, 2001.
  6. E. Nezhadarya and R. K. Ward, "A new scheme for robust gradient vector estimation in color images," IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2211-2220, 2011.
  7. P. E. Trahanias and A. N. Venetsanopoulos, "Color edge detection using vector order statistics," IEEE Transactions on Image Processing, vol. 2, no. 2, pp. 259-264, 1993.
  8. J. Lee, R. M. Haralick, And L. G. Shapiro, "Morphologic edge detection," IEEE Journal of Robotics and Automation, vol. 3, no. 2, pp. 142-156, 1987.
  9. S. Wesolkowski and E. Jernigan, "Color edge detection in RGB using jointly Euclidean distance and vector angle," in Proceedings of the IAPR Vision Interface Conference, Trois-Rivieres, Canada, 1999, pp. 9-16.
  10. L. Shafarenko, M. Petrou, and J. Kittler, "Automatic watershed segmentation of randomly textured color images," IEEE Transactions on Image Processing, vol. 6, no. 11, pp. 1530-1544, 1997.
  11. A. N. Evans and X. U. Liu, "A morphological gradient approach to color edge detection," IEEE Transactions on Image Processing, vol. 15, no. 6, pp. 1454-1463, 2006.
  12. M. A. Ruzon and C. Tomasi, "Color edge detection with the compass operator," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Ft. Collins, CO, 1999, pp. 160-166.
  13. P. C. Mahalanobis, "On the generalized distance in statistics," Proceedings of the National Institute of Sciences (Calcutta), vol. II, no. 1, pp. 49-55, 1936.
  14. P. Chauhan and R. V. Shahabade, "Edge detection comparison on various color spaces using histogram equalization," International Journal of Advance Computational Engineering and Networking, vol. 1, no. 4, pp. 7-10, 2013.
  15. J. Canny, "A computational approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, 1986.
  16. I. E. Abdou and W. Pratt, "Quantitative design and evaluation of enhancement/thresholding edge detectors," Proceedings of the IEEE, vol. 67, no. 5, pp. 753-763, 1979.

Cited by

  1. A new depth image quality metric using a pair of color and depth images vol.76, pp.9, 2017,
  2. Near-reversible efficient image resizing for devices supporting different spatial resolutions vol.73, pp.7, 2017,
  3. A bimodal empty space skipping of ray casting for terrain data vol.72, pp.7, 2016,
  4. Geo-registration of wide-baseline panoramic image sequences using a digital map reference vol.76, pp.9, 2017,
  5. Segmentation optimization simulation of water remote congestion image of the ship vol.76, pp.19, 2017,
  6. Compass radius estimation for improved image classification using Edge-SIFT vol.197, 2016,
  7. Perception Oriented Haze Image Definition Restoration by Basing on Physical Optics Model vol.10, pp.3, 2018,