Learning Variability of Image Feature Appearance Using Statistical Methods

  • Rodrigo Munguía
  • Antoni Grau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


Motivated by the problems of vision-based mobile robot map building and localization, in this work, we show that using statistical learning methods the performance of the standard descriptor based methodology for matching image features in a wide base line can be improved. First, we propose two kinds of descriptors for image features and two statistical learning methods. Later, we present a study of the performance of descriptors with and without the statistical learning methods. This work does not pretend to present an exhaustive description of the mentioned methods but to give a good idea the effectiveness of using statistical learning methods together with descriptors for matching image features in a wide base line.


Support Vector Machine Independent Component Analysis Independent Component Analysis Scale Invariant Feature Transform Kernel Principal Component Analysis 
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 2006

Authors and Affiliations

  • Rodrigo Munguía
    • 1
  • Antoni Grau
    • 1
  1. 1.Department of Automatic ControlUPCBarcelonaSpain

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