Image Descriptors Based on Curvature Histograms

  • Philipp FischerEmail author
  • Thomas Brox
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)


Descriptors based on orientation histograms are widely used in computer vision. The spatial pooling involved in these representations provides important invariance properties, yet it is also responsible for the loss of important details. In this paper, we suggest a way to preserve the details described by the local curvature. We propose a descriptor that comprises the direction and magnitude of curvature and naturally expands classical orientation histograms like SIFT and HOG. We demonstrate the general benefit of the expansion exemplarily for image classification, object detection, and descriptor matching.


Object Detection Image Classification Vector Curvature Sift Descriptor Discrete Image 
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.



The work was partially funded by the ERC Starting Grant VideoLearn.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburg im BreisgauGermany

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