MSCC: Maximally Stable Corner Clusters View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2005

AUTHORS

Friedrich Fraundorfer , Martin Winter , Horst Bischof

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. More... »

PAGES

45-54

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2004-08. Matching Widely Separated Views Based on Affine Invariant Regions in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2004. An Affine Invariant Salient Region Detector in COMPUTER VISION - ECCV 2004
  • 1998-11. Feature Detection with Automatic Scale Selection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2002. An Affine Invariant Interest Point Detector in COMPUTER VISION — ECCV 2002
  • 2000-06. Evaluation of Interest Point Detectors in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Image Analysis

    ISBN

    978-3-540-26320-3
    978-3-540-31566-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/11499145_6

    DOI

    http://dx.doi.org/10.1007/11499145_6

    DIMENSIONS

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