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.
This work is funded by the Austrian Joint Research Project Cognitive Vision under sub-projects S9103-N04 and S9104-N04, by a grant from Federal Ministry for Education, Science and Culture of Austria under the CONEX program and partially supported by the European Union Network of Excellence, MUSCLE under contract FP6-507752.
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Fraundorfer, F., Winter, M., Bischof, H. (2005). MSCC: Maximally Stable Corner Clusters. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds) Image Analysis. SCIA 2005. Lecture Notes in Computer Science, vol 3540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499145_6
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DOI: https://doi.org/10.1007/11499145_6
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