Abstract
In order to improve the correction accuracy of tongue colors by use of the Munsell colorchecker , this research aims to design a new colorchecker by aid of the tongue color space . Three essential issues leading to the development of this space-based colorchecker are investigated in this chapter. First, based on a large and comprehensive tongue database, the tongue color space is established by which all visible colors can be classified as tongue or non-tongue colors. Hence, colors of the designed tongue colorchecker are selected from tongue colors to achieve high correction performance. Second, the minimum sufficient number of colors involved in the colorchecker is attained by comparing the correction accuracy when a different number (range from 10 to 200) of colors are contained. Thereby, 24 colors are included because the obtained minimum number of colors is 20. Lastly, criteria for optimal color selection and their corresponding objective function are presented. Two color selection methods, i.e., greedy and clustering-based selection methods, are proposed to solve the objective function. Experimental results show that the clustering-based method outperforms its counterpart to generate the new tongue colorchecker. Compared to the Munsell colorchecker , this proposed space-based colorchecker can improve the correction accuracy by 48%. Further experimental results on more correction tasks also validate its effectiveness and superiority.
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References
Bala, R., Sharma, G., Monga, V., De Capelle, V., & Others. (2005). Two-dimensional transforms for device color correction and calibration. IEEE Transactions on Image Processing, 14(8), 1172–1186.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2), 191–203.
Cai, Y. (2002). A novel imaging system for tongue inspection (pp. 159–164): IEEE; 1999.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
Chang, Y., & Reid, J. F. (1996). RGB calibration for color image analysis in machine vision. IEEE Transactions on Image Processing, 5(10), 1414–1422.
Cheung, V., & Westland, S. (2006). Methods for optimal color selection. Journal of Imaging Science and Technology, 50(5), 481–488.
Cheung, V., Westland, S., Connah, D., & Ripamonti, C. (2004). A comparative study of the characterisation of colour cameras by means of neural networks and polynomial transforms. Coloration Technology, 120(1), 19–25.
Cormen, T. H. (2009). Introduction to algorithms. Cambridge, Massachusetts: MIT Press.
de Lasarte, M., Arjona, M., Vilaseca, M., & Pujol, J. (2010). Influence of the number of samples of the training set on accuracy of color measurement and spectral reconstruction. Journal of Imaging Science and Technology, 54(3), 30501.
Ilie, A., & Welch, G. (2005). Ensuring color consistency across multiple cameras (pp. 1268–1275). New York: IEEE.
Jiang, L., Xu, W., & Chen, J. (2008). Digital imaging system for physiological analysis by tongue colour inspection (pp. 1833–1836). New York: IEEE.
Johnson, T. (1996). Methods for characterizing colour scanners and digital cameras. Displays, 16(4), 183–191.
Kang, H. R. (1997). Color technology for electronic imaging devices. Bellingham, Washington: SPIE press.
Kosztyán, Z. T., & Schanda, J. A. N. (2009). Adaptive statistical methods for optimal color selection and spectral characterization of color scanners and cameras. Journal of Imaging Science and Technology, 53(1), 10501.
Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Luo, M. R., Hong, G., & Rhodes, P. A. (2001). A study of digital camera colorimetric characterization based on polynomial modeling. Color: Research and applications, 26(1), 76–84.
Manevitz, L. M., & Yousef, M. (2002). One-class SVMs for document classification. Journal of machine Learning research, 2, 139–154.
McCamy, C. S., Marcus, H., & Davidson, J. G. (1976). A color-rendition chart. Journal of Applied Photographic Engineering, 2(3), 95–99.
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
Sharma, G., & Bala, R. (Eds.). (2002). Digital color imaging handbook. Boca Raton, Florida: CRC Press.
Shen, L., Cai, Y., & Zhang, X. (2007). Tongue image acquisition and analysis. Beijing: Beijing University of Technology Press.
Shen, H., Zhang, H., Xin, J. H., & Shao, S. (2008). Optimal selection of representative colors for spectral reflectance reconstruction in a multispectral imaging system. Applied Optics, 47(13), 2494–2502.
Vrhel, M. J., & Trussell, H. J. (1999). Color device calibration: a mathematical formulation. IEEE Transactions on Image Processing, 8(12), 1796–1806.
Wang, X., & Zhang, D. (2010a). An optimized tongue image color correction scheme. IEEE Transactions on Information Technology in Biomedicine, 14(6), 1355–1364.
Wang, X., & Zhang, D. (2010b). A comparative study of color correction algorithms for tongue image inspection. In Medical Biometrics (pp. 392–402). New York: Springer.
Wang, X., & Zhang, D. (2011). Statistical tongue color distribution and its application. Health, 2856, 2566.
Wang, X., & Zhang, D. (2012). A New tongue colorchecker design by space representation for precise correction. IEEE Journal of Biomedical and Health Informatics, 17(2), 381–391.
X-Rite. (2010). Munsell colorchecker classic. [Online]. Available: http://xritephoto.com/ph_product_overview.aspx?id=1192&catid=28
Zhang, H. (2007). Tongue image acquisition and analyzing technology. Harbin: Harbin Institute of Technology.
Zhang, H., Wang, K., Jin, X., & Zhang, D. (2005). SVR based color calibration for tongue image (pp. 5065–5070). New York: IEEE.
Zhang, D., Wang, X., & Lu, G. (2010). An integrated portable standardized human tongue image acquisition system. Chinese Patent, ZL A, 101972138.
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Zhang, D., Zhang, H., Zhang, B. (2017). Tongue Colorchecker for Precise Correction. In: Tongue Image Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-2167-1_10
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DOI: https://doi.org/10.1007/978-981-10-2167-1_10
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