Combining Image Invariant Features and Clustering Techniques for Visual Place Classification

  • Jesús Martínez-Gómez
  • Alejandro Jiménez-Picazo
  • José A. Gámez
  • Ismael García-Varea
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6388)


This paper presents the techniques developed by the SIMD group and the results obtained for the 2010 RobotVision task in the ImageCLEF competition. The approach presented tries to solve the problem of robot localization using only visual information. The proposed system presents a classification method using training sequences acquired under different lighting conditions. Well-known SIFT and RANSAC techniques are used to extract invariant points from the images used as training information. Results obtained in the RobotVision@ImageCLEF competition proved the goodness of the proposal.


Mobile Robot Cluster Technique Training Sequence Invariant Feature Test Frame 
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 2010

Authors and Affiliations

  • Jesús Martínez-Gómez
    • 1
  • Alejandro Jiménez-Picazo
    • 1
  • José A. Gámez
    • 1
  • Ismael García-Varea
    • 1
  1. 1.Computing Systems Department, SIMD i3AUniversity of Castilla-la ManchaAlbaceteSpain

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