Using camera of a cell phone as a spectrophotometer in the outdoor usage

Document Type : Original Article

Authors

1 Department of Textile Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran

2 Department of Chemistry Engineering, University of Tehran, Tehran 141556619, Iran

3 Jonbesh Hamgam Tolid and Peiman Pars Company, MSTP, Sari 4816845155, Iran

Abstract

To use a mobile phone camera as a spectrophotometer, two steps are usually required. In the first step, the output RGB values of a camera are converted to device-independent color values. Afterward, spectral reflection values are reproduced from those device-independent values. In this paper, the matrix method was used to convert two color spaces to each other in the first step. In the next step, spectral reflection values were reproduced using the principal component analysis (PCA) method. It was shown that the best result is obtained using light source D50/ standard observer in 1964 and light source A/ standard observer in 1931 in a way that the color of the second light source and observer are obtained through a conversion matrix from the color values of the first light source and observer.

Keywords

References

  1. Gorji, K. Ansari, F. Ameri, S. Moradian. A concise review on color match prediction models. J. Color Sci. Tech. 8(2014), 249-260.
  2. Fraser, C.Murphy, F.Bunting.Real World Color Management. peachpit press, 2005, 79-95.
  3. م. گرجی بندپی، ارزیابی مدل­های مرسوم برای تبدیل اختلاط افزایشی به کاهشی رنگ در چاپگرهای دیجیتال. پایان نامه کارشناسی ارشد، ایران، 1390
  4. Poynton, A Technical Introduction to Digital Video. John Wiley & Sons., NewYork. 1996, 170-210.
  5. Barnard, B. Funt, Camera characterization for color research. Color. Res. Appl. 27 (2002), 152-163.
  6. R. Kang, Color scanner calibration. JIST. 36(1992), 162–170.
  7. R. Kang, H. R, Color Technology for Electronic Imaging Devices. SPIE, 23 (1997), 23-28.
  8. Y. Hardeberg, Transformations and Colour Consistency for the Colour Facsimile, MS thesis, Norway, 1995.
  9. Y. Hardeberg, F. Schmitt, Colorimetric characterization of a printer for the color facsimile. Technical report, ENST / SEPT, (1996).
  10. Schmitt, H. Maˆıtre, Y. Wu, First progress report: tasks 2.4 (Development / procurement of basic software routines) and 3.3 (Spectrophotometric characterization of paintings)—Vasari project. Technical Report 2649 (1990), CEE ESPRIT II.
  11. Schmitt, Y. Wu, J. P. Crettez, G. Boulay, Color calibration for color facsimile. In SID International Symposium, Orlando, (1995).
  12. Schmitt, J. P. Crettez, H. Brettel, J. Y. Hardeberg, I. Tastl, Input and output device characterization in the field of color facsimile. CSIT, 10 (1996), 141–143.
  13. C. Hung, Colorimetric calibration for scanners and media. CISS, 1448(1991), 164–174.
  14. A. Wandell, J. E. Farrell, Water into wine: Converting scanner RGB into tristimulus XYZ, SPIE proceedings,1909 (1996), 92–101.
  15. Haneishi, T. Hirao, A. Shimazu, Y. Miyake, Colorimetric precision in scanner calibration using matrices. Color Imaging Conference, (1995), 106–108.
  16. Hong, M. R. Luo, P. A. Rhodes, A study of digital camera colorimetric characterization based on polynomial modeling. Color. Res. Appl. 26(2001), 76-84.
  17. Liang, C. W. Chow, Y. Liu, RGB visible light communication using mobile-phone camera and multi-input multi-output. Optics Express. 24 (2016), 83-93.
  18. Y. Hardeberg, Acquisition and reproduction of colour images: colorimetric and multispectral approaches, PhD thesis, France, 1999.
  19. S. Berns, R. J. Motta, M. E. Gorzynski, (1993). CRT colorimetry. part I: Theory and practice. Color. Res. Appl. 18 (1993), 299–314.
  20. Schmitz, M. Ye, R. Shapiro, R. Yang, B. Noehren, Accuracy and repeatability of joint angles measured using a single camera markerless motion capture system. J. Biomech. 47(2014), 587-591.
  21. Bullas, S. Choppin, B. Heller, J. Wheat, Validity and repeatability of a depth camera-based surface imaging system for thigh volume measurement, J. Sport. Sci. 34(2016), 1998-2004.
  22. Du-Yong, J. P. Allebach, A subspace matching color filter design methodology for a multispectral imaging system. IEEE Trans. Image Process. 15(2006), 2631-2643.
  23. Zheng , S. Lin, Ch. Kambhamettu, J. Yu, Sing Bing Kang Single-Image Vignetting Correction. IEEE Trans. Pattern Anal. Mach. Intelligence, 31(2009), 14.
  24. Mahmoudi Nahavandi, M. Saf. Investigating and proving of multiplicative lighting correction in color characterization of digital camera. J. Color Sci. Tech. 8(2014), 109-115.
  25. Gutirerrez, A. Wendel, J. Underwood, Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation, Comput. Electron. Agric. 164 (2019), 105-112.
  26. J. Hawkyard, Synthetic reflectance curves by subtractive colour mixing, J. Soc. Dyers Colour. 109 (1993), 246-251.
  27. J. Hawkyard, Synthetic reflectance curves by additive mixing. J. Soc. Dyers Colour. 109 (1993), 323-329.
  28. S. Berns,Synthetic reflectance curve. J. Soc. Dyers Colour, 110 (1994), 386-389.
  29. T. Jolliffe, Principal component analysis, second edition, Springer series in statistic, New youk, 2002.
  30. س. ح. امیرشاهی، ف. آگهیان، فیزیک رنگ محاسباتی، انتشارات ارکان دانش، چاپ اول، اصفهان، 1386.
  31. B. Feallock, J. F. Southard, M. Kobayashi, W. C. Howell, absolute judgment of colors in the Federal Standards System.  Appl. 1966 (50), 266–272.
  32. ع. محمودی، توسعه یک مدل ریاضی برای یافتن حداقل تعداد فیلتر با منحنی عبوری بهینه به منظور دستیابی به بازسازی طیفی مطلوب. پایان نامه دکتری، ایران، 1395.

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