Improving Local Features by Dithering-Based Image Sampling

  • Christos VarytimidisEmail author
  • Konstantinos Rapantzikos
  • Yannis Avrithis
  • Stefanos Kollias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)


The recent trend of structure-guided feature detectors, as opposed to blob and corner detectors, has led to a family of methods that exploit image edges to accurately capture local shape. Among them, the W\(\alpha \)SH detector combines binary edge sampling with gradient strength and computational geometry representations towards distinctive and repeatable local features. In this work, we provide alternative, variable-density sampling schemes on smooth functions of image intensity based on dithering. These methods are parameter-free and more invariant to geometric transformations than uniform sampling. The resulting detectors compare well to the state-of-the-art, while achieving higher performance in a series of matching and retrieval experiments.


Image Coverage Gradient Strength Sift Descriptor Retrieval Experiment Binary Edge 
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.



This work is supported by the National GSRT-funded project 09SYN-72-922 “IS-HELLEANA : Intelligent System for HELLEnic Audiovisual National Aggregator”,, 2011-2014.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christos Varytimidis
    • 1
    Email author
  • Konstantinos Rapantzikos
    • 1
  • Yannis Avrithis
    • 1
  • Stefanos Kollias
    • 1
  1. 1.National Technical University of AthensAthensGreece

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