A Robust Footprint Detection Using Color Images and Neural Networks

  • Marco Mora
  • Daniel Sbarbaro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


The automatic detection of different foot’s diseases requires the analysis of a footprint, obtained from a digital image of the sole. This paper shows that optical monochromatic images are not suitable for footprint segmentation purposes, while color images provide enough information for carrying out an efficient segmentation. It is shown that a multiplayer perceptron trained with bayesian regularization backpropagation allows to adequately classify the pixels on the color image of the footprint and in this way, to segment the footprint without fingers. The footprint is improved by using a classical smoothing filter, and segmented by performing erosion and dilation operations. This result is very important for the development of a low cost system designed to diagnose pathologies related to the footprint form.


Hide Layer Color Image Gray Scale Image Sole Image Neural Network Toolbox 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Marco Mora
    • 1
  • Daniel Sbarbaro
    • 2
  1. 1.Department of Computer ScienceCatholic University of MauleTalcaChile
  2. 2.Department of Electrical EngineeringUniversity of ConcepcionConcepcionChile

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