The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.
Scale & Affine Invariant Interest Point Detectors
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In the citing paper
“Using the determinant of the Hessian matrix rather than its trace (the Laplacian) seems advantageous, as it fires less on elongated, ill-localised structures.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [15]
In the citing paper
“By studying the existing detectors and from published comparisons [15, 8], we can conclude that (1) Hessian-based detectors are more stable and repeatable than their Harris-based counterparts.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [15]
In the citing paper
“Also, (2) approximations like the DoG can bring speed at a low cost in terms of lost accuracy.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [15]
Citations
Cites
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Self-citationAn Affine Invariant Interest Point Detector
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Self-citation
Research works printed but not linked: 5
- Förstner,W. and Gülchm, E. 1987. A fast operator for detection and precise location of distinct points, corners and centres of circular features. In Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, pp. 281–305.
- Lazebnik, S., Schmid, C., and Ponce, J. 2003. Sparse texture representation using affine-invariant neighborhoods. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, USA, pp. 319–324.
- Mikolajczyk, K. 2002. Interest point detection invariant to affine transformations. PhD thesis, Institut National Polytechnique de Grenoble.
- Pritchett, P. and Zisserman, A. 1998.Wide baseline stereo matching. In Proceedings of the 6th International Conference on Computer Vision, Bombay, India. IEEE Computer Society Press, pp. 754–760.
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Metadata
- Authors
- Mikolajczyk, K., & Schmid, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- International Journal of Computer Vision
- Published
- 2004-10
- Volume
- 60
- Issue
- 1
- Pages
- 63-86
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2172188317
- Cited by
- 3,436
- References
- 45
- Citations by year
- 2026: 17 · 2025: 35 · 2024: 47 · 2023: 64 · 2022: 66 · 2021: 94 · 2020: 93 · 2019: 124 · 2018: 103 · 2017: 160 · 2016: 186 · 2015: 277 · 2014: 251 · 2013: 279 · 2012: 270
- FWCI
- 116.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Keywords
- keypoint detection, scale invariance, affine invariance, Harris corner detector, structure tensor, multi-scale representation, image matching
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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