Comparing and Evaluating Interest Points

Cordelia Schmid 1 Roger Mohr 1 Christian Bauckhage 1
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : Many computer vision tasks rely on feature extraction. Interest points are such features. This paper shows that interest points are geometrically stable under different transformations and have high information content (distinctiveness). These two properties make interest points very successful in the contest of image matching. To measure these two properties quantitatively, we introduce two evaluation criteria: repeatability rate and information content. The quality of the interest points depends on the detector used. In this paper several detectors are compared according to the criteria specified above. We determine which detector gives the best results and show that it satisfies the criteria well.
Type de document :
Communication dans un congrès
6th International Conference on Computer Vision (ICCV '98), Jan 1998, Bombay, India. IEEE Computer society, pp.230--235, 1998, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=710723〉. 〈10.1109/ICCV.1998.710723〉
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https://hal.inria.fr/inria-00548328
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Dernière modification le : mercredi 11 avril 2018 - 01:53:04
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Cordelia Schmid, Roger Mohr, Christian Bauckhage. Comparing and Evaluating Interest Points. 6th International Conference on Computer Vision (ICCV '98), Jan 1998, Bombay, India. IEEE Computer society, pp.230--235, 1998, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=710723〉. 〈10.1109/ICCV.1998.710723〉. 〈inria-00548328〉

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