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Maximally Stable Colour Regions for Recognition and Matching

Per-Erik Forssén
CVPR07, Minneapolis, USA
IEEE Conference on Computer Vision and Pattern Recognition
June 2007


This paper introduces a novel colour-based affine covariant region detector. Our algorithm is an extension of the maximally stable extremal region (MSER) to colour. The extension to colour is done by looking at successive time-steps of an agglomerative clustering of image pixels. The selection of time-steps is stabilised against intensity scalings and image blur by modelling the distribution of edge magnitudes. The algorithm contains a novel edge significance measure based on a Poisson image noise model, which we show performs better than the commonly used Euclidean distance. We compare our algorithm to the original MSER detector and a competing colour-based blob feature detector, and show through a repeatability test that our detector performs better. We also extend the state of the art in feature repeatability tests, by using scenes consisting of two planes where one is piecewise transparent. This new test is able to evaluate how stable a feature is against changing backgrounds.

Full Paper

Portable document format file PDF ()
On-line proceedings available from IEEE Xplore.
Video clips and source code can be found here.

Bibtex entry

  author = 	 {Per-Erik Forss\'en},
  title = 	 {Maximally Stable Colour Regions for Recognition and Matching},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition},
  year = 	 {2007},
  address = 	 {Minneapolis, USA},
  month = 	 {June},
  organization = {{IEEE} Computer Society},
  isbn =         {1-4244-1180-7},
  publisher = {{IEEE}}

Per-Erik Forssén

Per-Erik Forssén


Computer Vision Laboratory
Department of Electrical Engineering
Building B
Room 2D:521
SE-581 83 Linköping, Sweden
+46(0)13 285654

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