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A Printer Indexing System for Color Calibration with Applications in Dietary Assessment

  • Shaobo Fang
  • Chang Liu
  • Fengqing Zhu
  • Carol Boushey
  • Edward Delp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

In image based dietary assessment, color is a very important feature in food identification. One issue with using color in image analysis in the calibration of the color imaging capture system. In this paper we propose an indexing system for color camera calibration using printed color checkerboards also known as fiducial markers (FMs). To use the FM for color calibration one must know which printer was used to print the FM so that the correct color calibration matrix can be used for calibration. We have designed a printer indexing scheme that allows one to determine which printer was used to print the FM based on a unique arrangement of color squares and binarized marks (used for error control) printed on the FM. Using normalized cross correlation and pattern detection, the index corresponding to the printer for a particular FM can be determined. Our experimental results show this scheme is robust against most types of lighting conditions.

Keywords

Test Image Dietary Assessment Mobile Telephone Color Correction Normalize Cross Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    The TADA project. http://tadaproject.org
  2. 2.
    Briechle, K., Hanebeck, U.: Template matching using fast normalized cross correlation. In: Proceedings of the SPIE Optical Pattern Recognition XII, Orlando, FL, vol. 4387, pp. 95–102, April 2001Google Scholar
  3. 3.
    Daugherty, B., Schap, T., Ettienne-Gittens, R., Zhu, F., Bosch, M., Delp, E., Ebert, D., Kerr, D., Boushey, C.: Novel technologies for assessing dietary intake: Evaluating the usability of a mobile telephone food record among adults and adolescents. Journal of Medical Internet Research 14(2), e58 (2012)CrossRefGoogle Scholar
  4. 4.
    von Kries, J.: Chromatic adaptation. Festschrift der Albrecht-Ludwigs-Universit, pp. 145–158 (1902)Google Scholar
  5. 5.
    Mikkilineni, A., Chiang, P., Ali, G., Chiu, G., Allebach, J., Delp, E.: Printer identification based on graylevel co-occurrence features for security and forensic applications. In: Proceedings of the SPIE Security, Steganography, and Watermarking of Multimedia Contents VII, San Jose, CA, vol. 5681, pp. 430–440, January 2005Google Scholar
  6. 6.
    Mindru, F., Tuytelaars, T., Van Gool, L., Moons, T.: Moment invariants for recognition under changing viewpoint and illumination. Computer Vision and Image Understanding 94(1–3), 3–27 (2004)CrossRefGoogle Scholar
  7. 7.
    Sharma, G.: Digital Color Imaging Handbook. CRC Press, Boca Raton (2002)CrossRefGoogle Scholar
  8. 8.
    Stokes, M., Anderson, M., Chandrasekar, S., Motta, R.: A standard default color space for the internet-srgb. Microsoft and Hewlett-Packard Joint Report (1996)Google Scholar
  9. 9.
    van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)CrossRefGoogle Scholar
  10. 10.
    Wandell, B.: Foundations of Vision. Sinauer Associates Inc., Sunderland (1995)Google Scholar
  11. 11.
    Xu, C., Khanna, N., Boushey, C.J., Delp, E.J.: Low complexity image quality measures for dietary assessment using mobile devices. In: Proceedings of the IEEE International Symposium on Multimedia, Dana Point, CA, pp. 351–356, December 2011Google Scholar
  12. 12.
    Xu, C., Zhu, F., Khanna, N., Boushey, C., Delp, E.: Image enhancement and quality measures for dietary assessment using mobile devices. In: Proceedings of the IS&T/SPIE Conference on Computational Imaging X, San Francisco, CA, vol. 8296, pp. 82960Q–82960Q-10, January 2012Google Scholar
  13. 13.
    Zhao, F., Huang, Q., Gao, W.: Image matching by normalized cross-correlation. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Toulouse, France, vol. 2, pp. 729–732, May 2006Google Scholar
  14. 14.
    Zhu, F., Bosch, M., Khanna, N., Boushey, C., Delp, E.: Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE Journal of Biomedical and Health Informatics 19(1), 377–388 (2015)CrossRefGoogle Scholar
  15. 15.
    Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C., Ebert, D., Delp, E.: The use of mobile devices in aiding dietary assessment and evaluation. IEEE Journal of Selected Topics in Signal Processing 4(4), 756–766 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shaobo Fang
    • 1
  • Chang Liu
    • 1
  • Fengqing Zhu
    • 1
  • Carol Boushey
    • 2
  • Edward Delp
    • 1
  1. 1.School of Electrical and Computer EngineeringPurdue UniversityWest LafayetteUSA
  2. 2.Cancer Epidemiology ProgramUniversity of Hawaii Cancer CenterHonoluluUSA

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