A new edge-preserving algorithm based on the CIE- Lu'v' color space for color contrast enhancement

A new edge-preserving algorithm based on the CIE- Lu'v' color space for color contrast enhancement

Image segmentation and edge detection are the most important presteps in machine vision, and their successfulness can affect the success of the next steps. In this paper, the performance of the Trahanias edge detector in different color spaces, such as RGB, YCbCr, HSI, and CIE Lu'v', is compared in order to find the best color space for image segmentation and edge detection. We then offer an efficient edge-preserving algorithm for color contrast enhancement in the CIE Lu'v' space. The proposed algorithm can increase the color contrast, which causes a remarkable improvement in image segmentation and edge detection in the CIE Lu'v' color space. Moreover, it can efficiently reduce the number of spurious edges that may be produced during the color contrast enhancement process. The results obtained by applying the proposed algorithm, as compared with those by applying another recently introduced algorithm, demonstrate the better performance of the proposed algorithm.

___

  • [1] T.W. Ridler, S. Calvard, “Picture thresholding using an iterative selection method”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 8, pp. 630–632, 1978.
  • [2] A. Mehnert, P. Jackway, “An improved seeded region growing algorithm”, Pattern Recognition Letters, Vol. 18, pp. 1065–1071, 1997.
  • [3] Z. Wu, R. Leahy, “An optimal graph theoretic approach to data clustering: theory and its application to image segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, pp. 1101–1113, 1993.
  • [4] P.E. Trahanias, A.N. Venetsanopoulos, “Color edge detection using order statistics”, IEEE Transactions on Image Processing, Vol. 2, pp. 259–264, 1993.
  • [5] J. Schanda, Colorimetry Understanding the CIE System,New York, Wiley Interscience, 2007.
  • [6] S.J. Sangwine, R.E.N. Horne, The Color Image Processing Handbook, London, UK, Chapman and Hall, 1998.
  • [7] L. Lucchese, S.K. Mitra, “Filtering color images in the xyY color space”, Proceedings of the International Conference of Image Processing, pp. 500–503, 2000.
  • [8] K.L. Chung, Y.W. Liu, W.M. Yan, “Efficient edge preserving algorithm for color contrast enhancement with application to color image segmentation”, Journal of Visual Communication and Image Representation, Vol. 17, pp. 299–310, 2008.
  • [9] J. Scharcanski, A.N. Venetsanopoulos, “Edge detection of color images using directional operators”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 7, pp.397–401, 1997.
  • [10] C. Theoharatos, G. Economou, S. Fotopoulos, “Color edge detection using the minimal spanning tree”, Pattern Recognition, Vol. 38, pp. 603–60, 2005.
  • [11] V. Barnett, “The ordering of multivariate data”, Journal of The Royal Statistical Society, Vol. 139, pp. 318–343, 1976.
  • [12] A. Sharma, Understanding Color Management, New York, Thomson Delmar Learning, 2003.
  • [13] R. Jackson, L. MacDonald, K. Freeman, Computer Generated Colour, New York, Wiley, 1994.
  • [14] L. Lucchese, S.K. Mitra, J. Mukherjee, “A new algorithm based on saturation and desaturation in the xy chromaticity diagram for enhancement and rendition of color images”, Proceedings of the International Conference of Image Processing, Vol. 2, pp. 1077–1080, 2000.
  • [15] S.C. Pei, Y.C. Zeng, C.H. Chang, “Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis”, IEEE Transactions on Image Processing, Vol. 13, pp. 416–429, 2004.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK