Work

Comparing and evaluating interest points

Year: 2002

Type: conference-paper

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.

Source: IEEE/CVF International Conference on Computer Vision (ICCV)

DOI: 10.1109/iccv.1998.710723

Authors: C. Schmid, R. Mohr, C. Bauckhage

Institutions: Institut national de recherche en sciences et technologies du numérique, Centre Inria de l'Université Grenoble Alpes

Language: English

Topic: Advanced Image and Video Retrieval Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W1773272891

FWCI: 19.12

Cites: 11

Cited by: 268

Related to: 9