Descriptor learning for omnidirectional image matching

Jonathan Masci, Davide Migliore, Michael M. Bronstein, Jürgen Schmidhuber

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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. © 2014 Springer-Verlag Berlin Heidelberg.
Original languageEnglish (US)
Pages (from-to)49-62
Number of pages14
JournalStudies in Computational Intelligence
Volume532
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence

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