[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

RGB to \(L^{ * } a^{ * } b^{ * }\) Color Prediction Model Based on Color Cards

  • Conference paper
  • First Online:
Cognitive Systems and Information Processing (ICCSIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1919))

Included in the following conference series:

  • 321 Accesses

Abstract

Color is a critical characteristic indicating the traits of an object and is widely used in computer vision technology. In order to ensure uniformity and device independence of color in image processing, standardized and normative color management are needed. \(L^{*} a^{*} b^{*}\) color space is a device-independent expression of color values because it is uniform and independent of hardware devices. Therefore, in this paper, we compare four prediction models from RGB to \(L^{*} a^{*} b^{*}\). The experimental platform was built to acquire digital images with Pantone color cards as sample data and extract RGB value. The real \(L^{*} a^{*} b^{*}\) value is measured by colorimeter. And preprocess the original RGB value and \(L^{*} a^{*} b^{*}\) value data, and establish four prediction models: linear, quadratic polynomial, neural network and extreme learning machine, by comparing preprocessed data with real data. Using four evaluation metrics (MAE, MSE, RMSE and R2), and scatter fit plots were used to compare results. The results show that the model based on extreme learning machine has the highest prediction accuracy, with MAE reaching 0.021, 0.017 and 0.010, MSE 0.0006, 0.0005 and 0.0001, RMSE 0.026, 0.021 and 0.031, R2 0.96, 0.97 and 0.96, respectively. Indicates that the extreme learning machine has good potential for application.

This work is support by Aeronautical Science Foundation of China (Grant No. 20200051042003).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

References

  1. Mahanta, D.K., Laskar, S.: Electrical insulating liquid: a review. J. Adv. Dielectr. 7(04), 1730001 (2017)

    Article  Google Scholar 

  2. Lukinac, J., Mastanjević, K., Mastanjević, K., et al.: Computer vision method in beer quality evaluation—a review. Beverages 5(2), 38 (2019)

    Article  Google Scholar 

  3. Wu, D., Sun, D.W.: Colour measurements by computer vision for food quality control–a review. Trends Food Sci. Technol. 29(1), 5–20 (2013)

    Article  Google Scholar 

  4. Samiullah, S., Roberts, J.R., Chousalkar, K.: Eggshell color in brown-egg laying hens—a review. Poult. Sci. 94(10), 2566–2575 (2015)

    Article  Google Scholar 

  5. Costa, J.J.F., Giasson, E., da Silva, E.B., et al.: Use of color parameters in the grouping of soil samples produces more accurate predictions of soil texture and soil organic carbon. Comput. Electron. Agric. 177, 105710 (2020)

    Article  Google Scholar 

  6. Chu, S.J., Trushkowsky, R.D., Paravina, R.D.: Dental color matching instruments and systems. Review of clinical and research aspects. J. Dentist. 38, e2–e16 (2010)

    Google Scholar 

  7. Hu, C., Mei, H., Guo, H., et al.: Color analysis of textile fibers by microspectrophotometry. Forensic Chem. 18, 100221 (2020)

    Article  Google Scholar 

  8. Leong, Y.S., Ker, P.J., Jamaludin, M.Z., et al.: UV-Vis spectroscopy: a new approach for assessing the color index of transformer insulating oil. Sensors 18(7), 2175 (2018)

    Article  Google Scholar 

  9. Ma, J., Ruan, S., Hu, J., et al.: The intrinsic relationship between color variation and performances of the deteriorated aviation lubrication oil. J. Ind. Eng. Chem. 92, 88–95 (2020)

    Article  Google Scholar 

  10. Duarte, D.P., Nogueira, R.N., Bilro, L.: Low-cost color assessment of turbid liquids using supervised learning data analysis–proof of concept. Sens. Actuators, A 305, 111936 (2020)

    Article  Google Scholar 

  11. Zobkov, M.B., Zobkova, M.V.: New spectroscopic method for accurate color determination in natural water with the high agreement with visual methods. Water Res. 177, 115773 (2020)

    Article  Google Scholar 

  12. N’cho, J.S., Fofana, I., Hadjadj, Y., et al.: Review of physicochemical-based diagnostic techniques for assessing insulation condition in aged transformers. Energies 9(5), 367 (2016)

    Article  Google Scholar 

  13. Schelkopf, C.S., Rice, E.A., Swenson, J.K., et al.: Nix Pro Color Sensor provides comparable color measurements to the HunterLab colorimeter for fresh beef. J. Food Sci. Technol. 58, 3661–3665 (2021)

    Google Scholar 

  14. Dussán-Sarria, S., Garzón-García, A.M., Melo-Sevilla, R.E.: Desarrollo y evaluación de un prototipo de medición de color en vegetales frescos. Información tecnológica 31(1), 253–260 (2020)

    Article  Google Scholar 

  15. Zhang, S., Guo, Y.: Measurement of gem colour using a computer vision system: a case study with Jadeite-Jade. Minerals 11(8), 791 (2021)

    Article  Google Scholar 

  16. Nguyen, C.N., Vo, V.T., Ha, N.C.: Developing a computer vision system for real-time color measurement–a case study with color characterization of roasted rice. J. Food Eng. 316, 110821 (2022)

    Article  Google Scholar 

  17. Heng, C., Shen, H., Wang, F., et al.: Calibrated color measurement of cashmere using a novel computer vision system. Measurement 185, 109991 (2021)

    Article  Google Scholar 

  18. Su, Z., Yang, J., Li, P., et al.: Colour space conversion model from CMYK to CIELab based on CS-WNN. Color. Technol. 137(3), 272–279 (2021)

    Article  Google Scholar 

  19. Su, Z., Yang, J., Li, P., Jing, J., Zhang, H.: A precise method of color space conversion in the digital printing process based on PSO-DBN. Text. Res. J. 92(9–10), 1673–1681 (2022). https://doi.org/10.1177/00405175211067287

    Article  Google Scholar 

  20. Heng, C., Shen, H., Wang, F.: A novel digital imaging method for measuring cashmere color and its application. Text. Res. J. 91(21–22), 2528–2539 (2021)

    Article  Google Scholar 

  21. Zhang, J., Liu, Y., Zhang, X., et al.: Evaluation and consistency calibration of hyperspectral imaging system based on liquid crystal tunable filter for fabric color measurement. Color. Res. Appl. 47(2), 401–415 (2022)

    Article  Google Scholar 

  22. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), vol. 2, pp. 985–990. IEEE (2004). Author, F.: Article title. Journal 2(5), 99–110 (2016)

    Google Scholar 

  23. Author, F.: Article title. Journal 2(5), 99–110 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, China

    Yong Zhang, Chao Ma, Yunpeng Gu & Jianwei Ma

  2. Science and Technology on Electro-Optic Control Laboratory AVIC Optronics, Luoyang, 471000, China

    Jie Zou

Authors
  1. Yong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunpeng Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianwei Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianwei Ma .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Southern University of Science and Technology, Shenzhen, China

    Qinghu Meng

  3. Henan University of Science and Technology, Luoyang, China

    Zhumu Fu

  4. Tsinghua University, Beijing, China

    Bin Fang

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Y., Zou, J., Ma, C., Gu, Y., Ma, J. (2024). RGB to \(L^{ * } a^{ * } b^{ * }\) Color Prediction Model Based on Color Cards. In: Sun, F., Meng, Q., Fu, Z., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2023. Communications in Computer and Information Science, vol 1919. Springer, Singapore. https://doi.org/10.1007/978-981-99-8021-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8021-5_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8020-8

  • Online ISBN: 978-981-99-8021-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics