Computer Vision Algorithms Versus Traditional Methods in Food Technology: The Desired Correlation

  • Andrés Caro Lindo
  • Pablo García Rodríguez
  • María Mar Ávila
  • Teresa Antequera
  • R. Palacios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

Active Contours represent a common Pattern Recognition technique. Classical active contours are based on different methodologies (variational calculus, dynamic programming and greedy algorithm). This paper reviews the most frequently used active contours in a practical application, comparing weights, manually obtained by food technology experts, to volumes, automatically achieved by computer vision results. An experiment has been designed to recognize muscles from Magnetic Resonance (MR) images of Iberian ham at different maturation stages in order to calculate their volume change, using different active contour approaches. The sets of results are compared with the physical data. The main conclusions of the paper are the excellent correlation established between the data obtained with these three non-destructive techniques and the results achieved using the traditional destructive methodologies, as well as the real viability of the active contours to recognize muscles in MR images.

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References

  1. 1.
    Amini, A.A., Weymouth, T.E., Jain, R.: Using Dynamic Programming for Solving Variational Problems in Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 855–867 (1990)CrossRefGoogle Scholar
  2. 2.
    Antequera, T., López-Bote, C.J., Córdoba, J.J., García, C., Asensio, M.A., Ventanas, J., Díaz, Y.: Lipid oxidative changes in the processing of Iberian pig hams. Food Chem. 54, 105 (1992)CrossRefGoogle Scholar
  3. 3.
    Berry, D.A., Lindgren, B.W.: Statistics. Theory and method, Duxbury (1996)Google Scholar
  4. 4.
    Blake, A., Isard, M.: Active Contours. Springer, London (1998)Google Scholar
  5. 5.
    Caro, A., Rodríguez, P.G., Cernadas, E., Durán, M.L., Antequera, T.: Potencial Fields,as an External Force and Algorithmic Improvements in Deformable Models. Electronic Letters on Computer Vision and Image Analisys 2(1), 23–34 (2003)Google Scholar
  6. 6.
    Caro, A., Rodríguez, P.G., Ávila, M., Rodríguez, F., Rodríguez, F.J.: Active Contours Using Watershed Segmentation. In: IEEE 9th Int. Workshop on Systems, Signal and Image Processing, Manchester - UK, pp. 340–345 (2002)Google Scholar
  7. 7.
    Cava, R., Ventanas, J.: Dinámica y control del proceso de secado del jamón ibérico en condic. naturales y cámaras climatizadas, T. jamón ibérico, Mundi Prensa, 260–274 (2001)Google Scholar
  8. 8.
    Cohen, L.D.: On Active Contour Models and Balloons, Computer Vision. Graphics and Image Processing: Image Understanding 53(2), 211–218 (1991)MATHGoogle Scholar
  9. 9.
    Colton, T.: Statistical in Medicine. Little Brown and Co., Boston (1974)Google Scholar
  10. 10.
    Courant, R., Hilbert, D.: Methods of Mathematical Physics, New York, vol. 1. Interscience, Hoboken (1953)Google Scholar
  11. 11.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour models. In: Proceedings of First International Conference on Computer Vision, London, pp. 259–269 (1987)Google Scholar
  12. 12.
    Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., Yezzi, A.: Gradient Flows and Geometric Active Contour Models. In: Int. Conf. on Computer Vision, pp. 810–815 (1995)Google Scholar
  13. 13.
    Larsen, O.V., Radeva, P., Martí, E.: Guidelines for Choosing Optimal Parameters of Elasticity for Snakes. In: Int. Conf. Computer Analysis and Image Processing, pp. 106–113 (1995)Google Scholar
  14. 14.
    Ranganath, S.: Analysis of the effects of Snake Parameters on Contour Extraction. In: Proc. Int. Conference on Automation, Robotics, and Computer Vision, pp. 451–455 (1992)Google Scholar
  15. 15.
    Williams, D.J., Shah, M.: A Fast Algorithm for Active Contours and Curvature Estimation. In: C. Vision, Graphics and Im. Proc.: Im. Understanding, vol. 55, pp. 14–26 (1992)Google Scholar
  16. 16.
    Xu, C., Prince, J.L.: Gradient Vector Flow: A New External Force for Snakes. In: IEEE Proc. on Computer Vision and Pattern Recognition, pp. 66–71 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Andrés Caro Lindo
    • 1
  • Pablo García Rodríguez
    • 1
  • María Mar Ávila
    • 1
  • Teresa Antequera
    • 2
  • R. Palacios
    • 3
  1. 1.Computer Science Dept., Escuela PolitécnicaUniversity of ExtremaduraCáceresSpain
  2. 2.Food Technology, Facultad VeterinariaUniversity of ExtremaduraCáceresSpain
  3. 3.Radiology Service“Infanta Cristina” University HospitalBadajozSpain

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