CSDD features: Center-surround distribution distance for feature extraction and matching

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


We present an interest region operator and feature descriptor called Center-Surround Distribution Distance (CSDD) that is based on comparing feature distributions between a central foreground region and a surrounding ring of background pixels. In addition to finding the usual light(dark) blobs surrounded by a dark(light) background, CSDD also detects blobs with arbitrary color distribution that "stand out" perceptually because they look different from the background. A proof-of-concept implementation using an isotropic scale-space extracts feature descriptors that are invariant to image rotation and covariant with change of scale. Detection repeatability is evaluated and compared with other state-of-the-art approaches using a standard dataset, while use of CSDD features for image registration is demonstrated within a RANSAC procedure for affine image matching.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Number of pages14
EditionPART 3
ISBN (Print)3540886893, 9783540886891
StatePublished - 2008
Event10th European Conference on Computer Vision, ECCV 2008 - Marseille, France
Duration: Oct 12 2008Oct 18 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume5304 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th European Conference on Computer Vision, ECCV 2008

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'CSDD features: Center-surround distribution distance for feature extraction and matching'. Together they form a unique fingerprint.

Cite this