Advertisement

Using Small Checkerboards as Size Reference: A Model-Based Approach

  • Hamid HassannejadEmail author
  • Guido Matrella
  • Monica Mordonini
  • Stefano Cagnoni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

Monitoring diet is crucial for preventing or dealing with many chronic diseases. Therefore, plenty of different methods have been developed to serve this purpose. Among these, automatic diet monitoring based on mobile devices are of particular interest. An automatic system is supposed to be able to detect type and amount of food intake. This work suggests using a small checkerboard in food images as size reference as an aid for estimating food amount. Although checkerboard is a simple pattern, most of the off-the-shelf algorithms do not perform well in detecting small checkerboards. This paper extends a previous work presenting a new stochastic model-based algorithm to detect small checkerboards. The algorithm first locates the checkerboard in the food image and then applies a customized corner detection algorithm to the located region. Experimental results show notably better performance in comparison to basic methods and to the previous version of the method.

Keywords

Corner detection Small checkerboard Model-based method 

References

  1. 1.
  2. 2.
    Almaghrabi, R., Villalobos, G., Pouladzadeh, P., Shirmohammadi, S.: A novel method for measuring nutrition intake based on food image. In: 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 366–370, May 2012Google Scholar
  3. 3.
    Hassannejad, H., Matrella, G., Mordonini, M., Cagnoni, S.: A stochastic approach to detect small checkerboards . accepted in AI*IA 2015 (2015)Google Scholar
  4. 4.
    Martin, C.K., Kaya, S., Gunturk, B.K.: Quantification of food intake using food image analysis. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 6869–6872. IEEE (2009)Google Scholar
  5. 5.
    Mussi, L., Cagnoni, S., Daolio, F.: Gpu-based road sign detection using particle swarm optimization. In: Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, pp. 152–157. IEEE (2009)Google Scholar
  6. 6.
    Rahman, M.H., Li, Q., Pickering, M., Frater, M., Kerr, D., Bouchey, C., Delp, E.: Food volume estimation in a mobile phone based dietary assessment system. In: 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS), pp. 988–995. IEEE (2012)Google Scholar
  7. 7.
    Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol. 3. ICSI Berkeley (1995)Google Scholar
  8. 8.
    Ugolotti, R., Nashed, Y.S.G., Cagnoni, S.: Real-time GPU based road sign detection and classification. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 153–162. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  9. 9.
    Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C.J., Ebert, D.S., Delp, E.J.: 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

  • Hamid Hassannejad
    • 1
    Email author
  • Guido Matrella
    • 1
  • Monica Mordonini
    • 1
  • Stefano Cagnoni
    • 1
  1. 1.Dipartimento di Ingegneria Dell’InformazioneUniversità degli Studi di ParmaParmaItaly

Personalised recommendations