Accelerating integral histograms using an adaptive approach

Thomas Müller, Claus Lenz, Simon Barner, Alois Knoll

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


Many approaches in computer vision require multiple retrievals of histograms for rectangular patches of an input image. In 2005 an algorithm to accelerate these retrievals was presented. The data structure utilized is called Integral Histogram, which was based on the well known Integral Image. In this paper we propose a novel approximating method to obtain these integral histograms that outperforms the original algorithm and reduces computational cost to more than a tenth. Alongside we will show that our adaptive approach still provides reasonable accuracy - which allows dramatic performance improvements for real-time applications while still being well suited for numerous computer vision tasks.

Original languageEnglish
Pages (from-to)209-217
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5099 LNCS
StatePublished - 2008
Event3rd International Conference on Image and Signal Processing, ICISP 2008 - Cherbourg-Octeville, France
Duration: 1 Jul 20083 Jul 2008


  • Adaptive Approximation
  • Computer Vision
  • Early Processing
  • Integral Histogram
  • Object Recognition
  • Tracking


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