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About quantifying small color differences in digital images
I.G. Palchikova 1, E.S. Smirnov 1, O.A. Barinova 2, I.V. Latyshov 3, V.A. Vasiliev 2, A.V. Kondakov 2

Technological Design Institute of Scientific Instrument Engineering SB RAS,
41, Russkaya str., Novosibirsk, 630058, Russia,
The Volgograd Academy of the Russian Internal Affairs Ministry,
130,Istoricheskaya str., Volgograd, 400075, Russia,
The Saint Petersburg University of the Russian Internal Affairs Ministry,
1, Pilot Pilyutov str., Saint Petersburg, 198206, Russia

 PDF, 1233 kB

DOI: 10.18287/2412-6179-CO-631

Pages: 606-617.

Full text of article: Russian language.

We discuss aspects of the use and possibilities provided by three-color colorimeters or digital cameras in problems of detecting small color differences by computer vision methods. The spectral dependence of the total color differences between pairs of visually indiscernible monochromatic stimuli is experimentally revealed. An experimental setup based on the UM-2 monochromator is created for producing a digital atlas of monochromatic stimuli at 1-nm increments. The atlas serves to test the color gamut and color differentiation of cameras. It is experimentally shown that in the visible spectral range a color difference of 3 units is detected by pairs of stimuli that are unevenly distributed across the spectrum and differ in wavelengths from 1 to 6 nm. The capabilities of computer vision are tested on the examples of identifying additional texts during a technical and forensic examination of documents.
     A new algorithm is developed for finding and quantitatively characterizing color difference of inserts based on a digital image of the inscription. In the algorithm, the objective analysis of the image is divided into a block of color segmentation and that of color tone and color difference assessment. With such an approach, the color segmentation block performs preprocessing functions, making a border map for the classes with different colors for the subsequent calculations. The Otsu method of optimal global threshold transformation is for the first time applied to a problem of image segmentation by color saturation. The trial of the algorithm confirms its efficiency in the solution of expert tasks.

digital camera color gamut, RGB sensor, color, monochromatic stimuli, dominant wavelength, saturation, digital image processing, Otsu algorithm, segmentation, color difference.

Palchikova IG, Smirnov ES, Barinova OA, Latyshov IV, Vasiliev VA, Kondakov AV. About quantifying small color differences in digital images. Computer Optics 2020; 44(4): 606-617. DOI: 10.18287/2412-6179-CO-631.


  1. Nikolayev PP, Karpenko SM, Nikolaev DP. Spectral models of color constancy: selection rules [In Russian]. Proc ISA RAS 2008; 38: 332-335.
  2. Zeichner A, Levin N, Klein A, Novoselsky Y. Transmission and reflectance microspectrophotometry of inks. J Forensic Sci 1988; 33(5); 1171-1184.
  3. GOST 13088-67. Colorimetry. Terms, alphabetical symbols [In Russian]. – Moscow: "Izdateljstvo standartov" Publisher; 1990.
  4. Tominaga S. Spectral imaging by a multichannel camera. Proc SPIE 1998; 3648. DOI: 10.1117/12.334596.
  5. Domasev MV, Gnatyuk SP. Color, management of color, color calculations and measurements [In Russian]. Saint-Petersburg: "Piter" Publisher; 2009.
  6. Khorunzhy MD. Method of scoring color difference in digital image sensing [In Russian]. Vestnik NSU Series: Information Technologies 2008; 6(1): 80-88.
  7. Megahed A, Fadl SM, Han Q. Handwriting forgery detection based on ink colour features. ICSESS 2017. Source: <https://researchgate.net/publication/324728079>. DOI: 10.1109/ICSESS.2017.8342883.
  8. Barinova OA, Palchikova IG. Possibility of color analy-sis of dyes in the production of forensic examination of documents [In Russian]. Forensic Examination 2017; 52(4): 75-82.
  9. Russian federal center for forensic expertise under the Ministry of Justice of the Russian Federation: Technical examination of documents [In Russian]. Source: <http://sudexpert.ru/possib/techn.php>.
  10. Selivanov NA. Forensic color guide [In Russian]. Moscow: 1977.
  11. Palchikova IG, Smirnov ES, Palchikov EI. Quantization noise as a determinant for color thresholds in machine vision. Journal of the Optical Society of America A 2018; 35(4): B214-B222. DOI: 10.1364/JOSAA.35.00B214.
  12. Khan Z, Shafait F, Mian A. Automatic ink mismatch detection for forensic document analysis. Pattern Recognit 2015, 48: 3615-3626.
  13. Khairkar SR, Gaikawad SV, Kokare RN, Daundkar BB. Forensic discrimination potential of video spectral comparator and micro spectrophotometer in analyzing question document and fraud cases in India. J Forensic Res 2016; 7(3). Source: áhttps://pdfs.semanticscholar.org/5fe3/4d896a20057408d4779793662c98c4074092.pdfñ. DOI: 10.4172/2157-7145.1000329.
  14. Potaturkin OI, Borzov SM, Potaturkin AO, Uzilov SB. Methods and technologies of processing multi- and hyperspectral data for the high resolution remote sensing [In Russian]. Computational technologies 2013; 18: 60-67.
  15. Palchikova IG, Aleynikov AF, Chugui YuV, Vorobyov VV, Yarushin TV, Sarktakkov VYu, Makashov YuD, Smirnov ES, Shvydkov AN. Videoanalyzer of quantitative color characteristics of samples [In Russian]. Instruments 2014; 12: 38-44.
  16. Palchikova IG, Latyshov IV, Vasiliev VA, Kondakov AV, Smirnov ES. Color analysis of digital images in ex-pert judgement of shot’s trace [In Russian]. Proceedings of the Russian Higher School Academy of Sciences 2015; 27(2): 88-101.
  17. Luizov AV. Color and light [In Russian]. Leningrad: “Energoatomizdat” Publisher; 1989.
  18. Palchikova IG, Smirnov ES. Interval estimation of color parameters of the digital images [In Russian]. Computer optics 2017; 41(1): 95-102. DOI: 10.18287/2412-6179-2017-41-1-95-102.
  19. Vavilov SI. The Human eye and the Sun. “Hot” and “cold” light. Oxford: Pergamon Press Ltd; 1965. ISBN: 978-0-08-010381-5.
  20. GOST R 52490-2005 (ISO 7724-3:1984). Paints and varnishes – Colorimetry – Part 3: Calculation of colour differences (MOD). Moscow: "Standartinform" Publisher; 2007.
  21. CIE Publication No. 142-2001. Improvement to industrial colour-difference evaluation. Vienna: Central Bureau of the CIE; 2001. ISBN: 978-3-901906-08-4.
  22. Sharma G, Wu W, Dalal EN. The CIE ΔE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res Appl 2005; 30(1): 21-30.
  23. LuxaLight. CIE convertor. Source: <https://ledtuning.nl/en/cie-convertor>.
  24. Gonzales RC, Woods RE, Eddins SL. Digital image processing using MATLAB. Upper Saddle River, NJ: Pearson/Prentice Hall; 2004. ISBN: 978-81-7758-898-9.
  25. Chochia PA. Image segmentation based on the analysis of distances in an attribute space. Optoelectronics, Instrumentation and Data Processing 2014; 50(6): 613-624. DOI: 10.3103/S8756699014060107.
  26. Karamshuk EV. Development of photobox design for the criminalistic research of the shot tracks [In Russian]. Interexpo GEO-Siberia "SibOptica-2019" 2019; 8: 286-291. DOI: 2618-981Х-2019-8-286-291.
  27. Mc Camy CS, Marcus H, Davidson JG. A color rendition chart. J Appl Photogr Eng 1976; 11: 95-99.

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