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A Hierarchic Method for Footprint Segmentation Based on SOM

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Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5163))

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Abstract

In this study we propose a new approach for solving the problem of segmenting the footprint in color images. Previous studies have presented direct and supervised methods for segmenting the footprint pattern. This new approach proposes, in comparison to the previous methods, a hierarchic segmentation method, the use of different color models to represent the image pixels, and the non-supervised classification based on SOM. The characteristics of the method allow a robust footprint segmentation with a high level of autonomy.

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References

  1. Nakajima, K., Mizukami, Y., Tanaka, K., Tamura, T.: Footprint-based personal recognition. IEEE Transactions on Biomedical Engineering 47(11), 1534–1537 (2000)

    Article  Google Scholar 

  2. Morsy, A., Hosny, A.: A new system for the assessment of diabetic foot planter pressure. In: Proceedings of 26th Annual International Conference of the IEEE EMBS, pp. 1376–1379 (2004)

    Google Scholar 

  3. Mora, M., Jarur, M.C., Sbarbaro, D.: Automatic diagnosis of the footprint pathologies based on neural networks. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 107–114. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  4. Valenti, V.: Orthotic Treatment of Walk Alterations (in spanish), 1st edn. Panamerican Medicine (1979)

    Google Scholar 

  5. Patil, K., Bhat, V., Bhatia, M., Narayanamurthy, V., Parivalan, R.: New online methods for analysis of foot preesures in diabetic neuropathy. Frontiers Me. Biol. Engg. 9, 49–62 (1999)

    Google Scholar 

  6. Chu, W., Lee, S., Chu, W., Wang, T., Lee, M.: The use of arch index to characterized arch height: a digital image processing approach. IEEE Transaction on Biomedical Engineering 42(11), 1088–1093 (1995)

    Article  Google Scholar 

  7. Mora, M., Sbarbaro, D.: A robust footprint detection using color images and neural networks. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 311–318. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Mora, M., Jarur, M., Pavesi, L., Achu, E., Drut, H.: A pattern recognition approach to diagnose foot plant pathologies: From segmentation to classification. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 378–387. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  9. Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  10. Cheng, H., Jiang, X., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34(9), 2259–2281 (2001)

    Article  MATH  Google Scholar 

  11. Gonzalez, R., Woods, R.: Digital image processing, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)

    Google Scholar 

  12. Gevers, T., Groen, F.: Segmentation of color images. In: Proceedings of 7th Scandinavian Conference on Image Analysis 1991 (SCI 1991) (1991)

    Google Scholar 

  13. Atsalakis, A., Paparmakos, N.: Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas. Engineering Applications of Artificial Intelligence 19(7), 769–786 (2006)

    Article  Google Scholar 

  14. Papamarkos, N., Atsalakis, A., Strouthopoulos, C.: Adaptive color reduction. IEEE Transaction on System, Man and Cybernetics, Part B 32(1), 44–56 (2002)

    Article  Google Scholar 

  15. Mardia, K., Kent, J., Bibby, J.: Multivariate Analysis, 1st edn. Academic Press, London (1979)

    MATH  Google Scholar 

  16. Pratt, W.: Digital image processing, 2nd edn. John Wiley and Sons, Chichester (1991)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Les Fous du Pixel Image Processing Research Group Department of Computer Science, Catholic University of Maule, Casilla 617, Talca, Chile

    Marco Mora Cofre & Ruben Valenzuela

  2. Department of Computer Science, University of Luxembourg, Campus Kirchberg 6, rue Richard Coudenhove-Kalergi, L-1359, Luxembourg

    Girma Berhe

Authors
  1. Marco Mora Cofre
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  2. Ruben Valenzuela
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  3. Girma Berhe
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Editor information

Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Mora Cofre, M., Valenzuela, R., Berhe, G. (2008). A Hierarchic Method for Footprint Segmentation Based on SOM. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_91

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  • DOI: https://doi.org/10.1007/978-3-540-87536-9_91

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87535-2

  • Online ISBN: 978-3-540-87536-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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