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MSCC: Maximally Stable Corner Clusters

  • Friedrich Fraundorfer
  • Martin Winter
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

A novel distinguished region detector, complementary to existing approaches like Harris-corner detectors, Difference of Gaussian detectors (DoG) or Maximally Stable Extremal Regions (MSER) is proposed. The basic idea is to find distinguished regions by clusters of interest points. In order to determine the number of clusters we use the concept of maximal stableness across scale. Therefore, the detected regions are called: Maximally Stable Corner Clusters (MSCC). In addition to the detector, we propose a novel joint orientation histogram (JOH) descriptor ideally suited for regions detected by the MSCC detector. The descriptor is based on the 2D joint occurrence histograms of orientations. We perform a comparative detector and descriptor analysis based on the recently proposed framework of Mikolajczyk and Schmid, we present evaluation results on additional non-planar scenes and we evaluate the benefits of combining different detectors.

Keywords

Minimal Span Tree Interest Point Viewpoint Change Interest Point Detector Harris Corner 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Friedrich Fraundorfer
    • 1
  • Martin Winter
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria

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