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Improved colour-to-grey method using image segmentation and colour difference model for colour vision deficiency

Improved colour-to-grey method using image segmentation and colour difference model for colour vision deficiency

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Colour vision deficiency (CVD) is a genetic condition that has troubled people for a long time. This study proposes an improved colour-to-grey method for CVD using image segmentation and a colour difference model. In this method, the colour image is first segmented using a region growing method so that each region corresponds to one colour. Next, the colour difference is computed between arbitrary segmented region pairs. Finally, the greyscale image is obtained by minimising a target function. Experimental results show that compared with state-of-the-art colour-to-grey methods, the proposed algorithm can improve the E-score by about 10.99%.

References

    1. 1)
      • 1. Fairchild, M.D.: ‘Color appearance models’ (Addison-Wesley, 2005, 2nd edn.).
    2. 2)
      • 2. Viénot, F., Brettel, H., Ott, L., et al: ‘What do colour-blind people see?’, Nature, 1995, 376, pp. 127128.
    3. 3)
      • 3. Brettel, H., Viénot, F., Mollon, J.: ‘Computerized simulation of color appearance for dichromats’, J. Opt. Soc. Am., 1997, 14, (10), pp. 26472655.
    4. 4)
      • 4. Kim, Y.K., Kim, K.W., Yang, X.: ‘Real time traffic light recognition system for color vision deficiencies’. IEEE Int. Conf. on Mechatronics and Automation, 2007, ICMA 2007, 2007, pp. 7681.
    5. 5)
      • 5. Katsuhiro, N., Manami, T., Hiroshi, S., et al: ‘A way of color image processing for the colorblinds’ (Bulletin of Hiroshima Mercantile Marine College, 2016), vol. 38.
    6. 6)
      • 6. Rasche, K., Geist, R., Westall, J.: ‘Detail preserving reproduction of color images for monochromats and dichromats’, IEEE Comput. Graph., 2005, 25, (3), pp. 2230.
    7. 7)
      • 7. Navada, B.R., Santhosh, K.V.: ‘An image processing technique for color blind people to classify color and edges’. Int. Conf. on Knowledge Collaboration in Engineering, Coimbatore, India, March 2015.
    8. 8)
      • 8. Martin, C.E., Keller, J.O., Rogers, S.K., et al: ‘Color blindness and a color human visual system model’, IEEE Trans. Syst. Man Cybern. A, Syst. Hum., 2000, 30, (4), pp. 494500.
    9. 9)
      • 9. Gooch, A.A., Olsen, S.C., Tumblin, J., et al: ‘Color2gray: salience-preserving color removal’, ACM Trans. Graph., 2005, 24, (3), pp. 634639.
    10. 10)
      • 10. Neumann, L., Čadík, M., Nemcsics, A., et al: ‘An efficient perception-based adaptive color to gray transformation’. Eurographics Conf. on Computational Aesthetics in Graphics, Visualization and Imaging, Banff, Alberta, Canada, June 2007, pp. 7380.
    11. 11)
      • 11. Liu, Q., Liu, P.X., Xie, W., et al: ‘Gcsdecolor: gradient correlation similarity for, efficient contrast preserving decolorization’, IEEE Trans. Image Process., 2015, 24, (9), pp. 28892904.
    12. 12)
      • 12. Rasche, K.: ‘Re-coloring images for gamuts of lower dimension’, Comput. Graph. Forum, 2005, 24, (3), pp. 423432.
    13. 13)
      • 13. Lu, C., Xu, L., Jia, J.: ‘Contrast preserving decolorization with perception-based quality metrics’, Int. J. Comput. Vis., 2014, 110, (2), pp. 222239.
    14. 14)
      • 14. Lu, C., Xu, L., Jia, J.: ‘Real-time contrast preserving decolorization’. SIGGRAPH Asia 2012 Technical Briefs, 2012, pp. 17.
    15. 15)
      • 15. Du, H., He, S., Sheng, B., et al: ‘Saliency-guided color-to-gray conversion using region-based optimization’, IEEE Trans. Image Process., 2014, 24, (1), pp. 434443.
    16. 16)
      • 16. Grundland, M., Dodgson, N.A.: ‘Decolorize: fast, contrast enhancing, color to grayscale conversion’, Pattern Recogn., 2007, 40, (11), pp. 28912896.
    17. 17)
      • 17. Kim, Y., Jang, C., Demouth, J., et al: ‘Robust color-to-gray via nonlinear global mapping’, ACM Trans. Graph., 2009, 28, (5), pp. 8997.
    18. 18)
      • 18. Ancuti, C.O., Ancuti, C., Bekaert, P.: ‘Enhancing by saliency-guided decolorization’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, June 2011, pp. 257264.
    19. 19)
      • 19. Chen, D.S., Song, F.F., Zhang, Q.: ‘An adaptive global mapping approach for color to grayscale image conversion’, Comput. Syst. Appl., 2013, 22, pp. 164167.
    20. 20)
      • 21. Nazareth, J.L.: ‘Conjugate-gradient methods’ (Springer, 2001).
    21. 21)
      • 22. Jain, R., Kasturi, R., Schunck, B.G.: ‘Machine vision’ (McGraw-Hill, New York, 1995).
    22. 22)
      • 23. ‘Most of the Ishihara-colour-test-plates’. Available at http://www.colour-blindness.com/colour-blindness-tests/ishihara-colour-test-plates/, accessed 25 September 2016.
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