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Descriptor Learning for Omnidirectional Image Matching

  • Jonathan Masci
  • Davide Migliore
  • Michael M. Bronstein
  • Jürgen Schmidhuber
Part of the Studies in Computational Intelligence book series (SCI, volume 532)

Abstract

Feature matching in omnidirectional vision systems is a challenging problem, mainly because complicated optical systems make the theoretical modelling of invariance and construction of invariant feature descriptors hard or even impossible. In this paper, we propose learning invariant descriptors using a training set of similar and dissimilar descriptor pairs.We use the similarity-preserving hashing framework, in which we are trying to map the descriptor data to the Hamming space preserving the descriptor similarity on the training set. A neural network is used to solve the underlying optimization problem. Our approach outperforms not only straightforward descriptor matching, but also state-of-the-art similarity-preserving hashing methods.

Keywords

Shape Retrieval Parabolic Mirror Invariant Descriptor Omnidirectional Image Omnidirectional Camera 
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 2014

Authors and Affiliations

  • Jonathan Masci
    • 1
  • Davide Migliore
    • 2
  • Michael M. Bronstein
    • 3
  • Jürgen Schmidhuber
    • 1
  1. 1.IDSIA, USI and SUPSIManno-LuganoSwitzerland
  2. 2.Evidence S.r.l.MilanItaly
  3. 3.Dept. of InformaticsUniversità della Svizzera ItalianaLuganoSwitzerland

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