Colour Object Classification Using the Fusion of Visible and Near-Infrared Spectra

  • Heesang Shin
  • Napoleon H. Reyes
  • Andre L. Barczak
  • Chee Seng Chan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6230)


Under extreme light conditions, a conventional colour CCD camera would fail to render the colours of an object properly as the visible spectrum is either faintly observable in the scene or the presence of glare corrupts the colours sensed. On the other hand, for darkly-illuminated areas, a near-infrared (NIR) camera would sense stronger more discriminable signals, but could only render the scene monochromatically. The underlying challenge in this research is how to adaptively integrate a monochromatic NIR image with a faintly rendered colour image of the same darkly or very brightly lit scene to give rise to improved colour classification results that discriminate colours more effectively. This research proposes a Fuzzy-Genetic colour processing algorithm that adaptively marries together the visible and near-infrared spectra signals for the purpose of colour object recognition. The experiments were done on a scene with spatially varying illumination intensities, using Fujifilm’s UV/IR Super CCD camera with a sensitivity range between 380nm to 1000nm in conjunction with NIR filters. Results prove that the proposed multi-spectrum technique yields better colour classification results than utilizing the pure visible spectrum alone.


Target Colour Fusion Operation Colour Constancy Infrared Signal 200ms Exposure Time 


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  1. 1.
    Kong, S., Heo, J., Abidi, B., Paik, J., Abidi, M.: Recent advances in visual and infrared face recognition - a review. The Journal of Computer Vision and Image Understanding 97(1), 103–135 (2005)CrossRefGoogle Scholar
  2. 2.
    Ebner, M.: Color Constancy. Wiley, Chichester (2007)Google Scholar
  3. 3.
    Pan, Z., Healey, G., Prasad, M., Tromberg, B.: Face recognition in hyperspectral images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1552–1560 (2003)CrossRefGoogle Scholar
  4. 4.
    Rauss, P.J., Daida, J.M., Chaudhary, S.: Classification of spectral imagery using genetic programming. In: Proc. GECCO, pp. 726–733 (2000)Google Scholar
  5. 5.
    Montoliu, R., Pla, F., Klaren, A.C.: Illumination intensity, object geometry and highlights invariance in multispectral imaging (2005)Google Scholar
  6. 6.
    Ghosh, P., Jayas, D.: Use of spectroscopic data for automation in food processing industry. Sensing and Instrumentation for Food Quality and Safety 3(1), 3–11 (2009)CrossRefGoogle Scholar
  7. 7.
    Chao, K., Park, B., Chen, Y., Hruschka, W., Wheaton, F.: Design of a dual-camera system for poultry carcasses inspection. Appl. Eng. Agric. 16(5), 581–587 (2000)Google Scholar
  8. 8.
    Chen, Y.R., Chao, K., Kim, M.S.: Machine vision technology for agricultural applications. Computers and Electronics in Agriculture 36(2-3), 173–191 (2002)CrossRefGoogle Scholar
  9. 9.
    Kleynen, O., Leemans, V., Destain, M.F.: Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering 69(1), 41–49 (2005)CrossRefGoogle Scholar
  10. 10.
    ElMasry, G., Wang, N., Vigneault, C., Qiao, J., ElSayed, A.: Early detection of apple bruises on different background colors using hyperspectral imaging. LWT - Food Science and Technology 41(2), 337–345 (2008)CrossRefGoogle Scholar
  11. 11.
    Kobayashi, H., Ogawa, M., Kosaka, N., Choyke, P., Urano, Y.: Multicolor imaging of lymphatic function with two nanomaterials: quantum dot-labeled cancer cells and dendrimer-based optical agents. Nanomedicine 4, 411–419 (2009)CrossRefGoogle Scholar
  12. 12.
    Kosaka, N., Ogawa, M., Longmire, M.R., Choyke, P.L., Kobayashi, H.: Multi-targeted multi-color in vivo optical imaging in a model of disseminated peritoneal ovarian cancer. Journal of Biomedical Optics 14 (2009)Google Scholar
  13. 13.
    Vilaseca, M., Pujol, J., Arjona, M., Martnez-Verd, F.M.: Color visualization system for near-infrared multispectral images. Journal of Imaging Science and Technology 49(3), 246–255 (2005)Google Scholar
  14. 14.
    Menesatti, P., Antonucci, F., Pallottino, F., Roccuzzo, G., Allegra, M., Stagno, F., Intrigliolo, F.: Estimation of plant nutritional status by vis-nir spectrophotometric analysis on orange leaves (citrus sinensis (l) osbeck cv tarocco). Biosystems Engineering 105(4), 448–454 (2010)CrossRefGoogle Scholar
  15. 15.
    Mertens, K., Vaesen, I., Loffel, J., Kemps, B., Kamers, B., Perianu, C., Zoons, J., Darius, P., Decuypere, E., De Baerdemaeker, J., De Ketelaere, B.: The transmission color value: A novel egg quality measure for recording shell color used for monitoring the stress and health status of a brown layer flock. Poult. Sci. 89(3), 609–617 (2010)CrossRefGoogle Scholar
  16. 16.
    Pap, K., Žiljak, I., Žiljak Vujić, J.: Image reproduction for near infrared spectrum and the infraredesign theory. Journal of Imaging Science and Technology 54(1), 010502 (2010)CrossRefGoogle Scholar
  17. 17.
    Shin, H., Reyes, N.H.: Variable colour depth look-up table based on fuzzy colour processing. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5506, pp. 1071–1078. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Shin, H.: Finding near optimum colour classifiers: Genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth. Master’s thesis, Massey University (2009)Google Scholar
  19. 19.
    Reyes, N.H., Dadios, P.E.: Dynamic color object recognition using fuzzy logic. Journal of Advanced Computational Intelligence and Intelligent Informatics 8, 29–38 (2004)Google Scholar
  20. 20.
    Thomas, P., Stonier, R., Wolfs, P.: Robustness of color detection for robot soccer. In: Proceedings of the Seventh International Conference on Control, Automation, Robotics and Vision, pp. 1245–1249 (2002)Google Scholar
  21. 21.
    Shin, H., Reyes, N.: Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth. Memetic Computing Journal, 1–18 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Heesang Shin
    • 1
  • Napoleon H. Reyes
    • 1
  • Andre L. Barczak
    • 1
  • Chee Seng Chan
    • 2
  1. 1.Institute of Information and Mathematical SciencesMassey UniversityAucklandNew Zealand
  2. 2.Mimos BerhadKuala LumpurMalaysia

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