The DOI matches its Crossref record; 44 of the 50 references it cites were found in a registry or an index.
Distinctive Image Features from Scale-Invariant Keypoints
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In the citing paper
“Moreover, the efficiency of HF-net2 was tested and compared with several wellknown handcrafted algorithms for keypoint detection and description including Harris [4], FAST [6], SIFT [9] and ORB [8].”
From Keypoint Detection and Description through Deep Learning in Unstructured Environments (2023) · cites this paper as [9]
In the citing paper
“By including a background removal step sim-330 ilar to the WGE in [9], which makes use of a Prewitt operator in combination with a gradient filter, the authors could also demonstrate the robustness of their IP with respect to the Earth in the background.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Feature fusion was then used to synthesise the detected points into polylines which resemble parts of the spacecraft body.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Alternatively, Capuano et al. [32] introduced a new IP scheme in which three different parallel processing streams, which use the Shi-Tommasi (ST) corners detector, the HT, and the Line Segment Detector (LSD), are exploited in order to filter three sets of points and improve the robustness of the feature detection.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Furthermore, the scenarios with the Earth horizon were tackled by tuning the threshold of gradient filter to a more selective value.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Robustness proven”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Although pyramid decomposition has been widely used in image compression [8], computer vision [9], etc., its use in image hashing, as proposed in this paper, has proved to be every effective.”
From A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids (2019) · cites this paper as [9]
In the citing paper
“The latter, introduced by Lowe [2], have been shown to outperform the others [7].”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“This can be explained by the fact that they capture a substantial amount of information about the spatial intensity patterns, while at the same time being robust to small deformations or localisation errors.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under partial occlusion and view point changes.”
From 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch (2016) · cites this paper as Lowe 2004
In the citing paper
“Traditional methods may require complex feature matching algorithms (e.g., SIFT, SURF) or additional classifiers (e.g., SVM), whereas the MobileNetV2 network model directly utilizes features extracted by the deep learning model to perform comparison, which simplifies the process [27,28].”
From Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology (2025) · cites this paper as [27]
Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Self-citationA Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Generalizing the Hough transform to detect arbitrary shapes
Ballard, D. H. (1981) · Pattern Recognition · doi:10.1016/0031-3203(81)90009-1 · Cited by 4,434
Robust wide-baseline stereo from maximally stable extremal regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,764
An Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802
Koenderink, J. J. (1984) · Biological Cybernetics · doi:10.1007/bf00336961 · Cited by 2,452
Witkin, A. (1987) · Elsevier eBooks · doi:10.1016/b978-0-08-051581-6.50036-2 · Cited by 2,375
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1998) · Journal of the ACM · doi:10.1145/293347.293348 · Cited by 2,290
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211479 · Cited by 2,046
Research works printed but not linked: 3
- Harris, C. and Stephens, M. 1988. Acombined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.
- Mikolajczyk, K. 2002. Detection of local features invariant to affine transformations, Ph.D. thesis, Institut National Polytechnique de Grenoble, France.
- Torr, P. 1995. Motion segmentation and outlier detection, Ph.D. Thesis, Dept. of Engineering Science, University of Oxford, UK.
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Kontogianni, G., Stathopoulou, E. K., Georgopoulos, A., et al. (2015) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-5-w4-325-2015 · Cited by 19
Scale-aware Conditional Generative Adversarial Network for Image Dehazing
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Liu, X., Wang, Y., Du, J., et al. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6361-2 · Cited by 19
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Gauthier, S., Therien, B., Alsène-Racicot, L., et al. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00566 · Cited by 19
Bubble space and place representation in topological maps
Erkent, Ö., & Bozma, H. I. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913481393 · Cited by 19
Detecting Text Areas and Decorative Elements in Ancient Manuscripts
Garz, A., Diem, M., & Sablatnig, R. (2010) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2010.35 · Cited by 19
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Diaz, R., Hallman, S., & Fowlkes, C. C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.41 · Cited by 18
Paper2GIS: improving accessibility without limiting analytical potential in Participatory Mapping
Denwood, T., Huck, J., & Lindley, S. J. (2022) · Journal of Geographical Systems · doi:10.1007/s10109-022-00386-6 · Cited by 19
Zhao, G., Chen, L., Chen, G., & Yuan, J. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874180 · Cited by 19
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Dong, L., Yu, X., Li, L., et al. (2010) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2010.5707916 · Cited by 19
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Zhang, C., Bastian, J., Shen, C., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738262 · Cited by 19
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Yamamoto, K., Yendo, T., Fujii, T., et al. (2007) · Journal of information processing · doi:10.11185/imt.2.713 · Cited by 19
Vivekraj, V. K., Sen, D., & Raman, B. (2019) · ACM Computing Surveys · doi:10.1145/3347712 · Cited by 41
Target-referenced Reactive Grasping for Dynamic Objects
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Dozza, M., & González, N. P. (2012) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2012.06.1029 · Cited by 19
Yun, H. S., Wei, D., Yang, S., et al. (2025) · Advanced Materials · doi:10.1002/adma.202504288 · Cited by 19
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Sarlin, P.-E., Debraine, F., Dymczyk, M., Siegwart, R., & Cadena, C. (2018) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000318818 · Cited by 91
Kim, J., & Kim, T. (2016) · Sensors · doi:10.3390/s16030412 · Cited by 19
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Rao, M. B., Rao, B. P., & Govardhan, A. (2011) · Cited by 19
FASSD: A Feature Fusion and Spatial Attention-Based Single Shot Detector for Small Object Detection
Deng, J., Sun, B., Su, S., et al. (2020) · Electronics · doi:10.3390/electronics9091536 · Cited by 19
RGB and Optimal Waveband Image Fusion for Real-Time Underwater Clear Image Acquisition
Sun, X., Zhu, Y., & Fu, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3290366 · Cited by 19
Features Combined Binary Descriptor Based on Voted Ring-Sampling Pattern
Liu, H., Zhang, Q., Fan, B., Wang, Z., & Han, J. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2943595 · Cited by 19
An effective local feature descriptor for object detection in real scenes
Nigam, S., Khare, M., Srivastava, R. K., & Khare, A. (2013) · IEEE Conference on Information and Communication Technologies (ICT) · doi:10.1109/cict.2013.6558098 · Cited by 17
Multi-modal and inertial sensor solutions for navigation-type factor graphs
Fourie, D. (2017) · Open Access Server of the Woods Hole Scientific Community (Woods Hole Scientific Community) · doi:10.1575/1912/9305 · Cited by 17
Compass radius estimation for improved image classification using Edge-SIFT
Fidalgo, E., Alegre, E., González‐Castro, V., et al. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.02.045 · Cited by 19
Rodrigues, M. T. A. N., Padua, F. L. C., Gomes, R. M., & Soares, G. E. (2010) · International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) · doi:10.1109/bicta.2010.5645273 · Cited by 19
Perceptual Video Processing: Seeing the Future [Point of View]
Bovik, A. C. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2068371 · Cited by 19
Coalesced global and local feature discrimination for content-based image retrieval
Kumar, V. D. A. (2017) · International Journal of Information Technology · doi:10.1007/s41870-017-0042-7 · Cited by 19
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Wiering, M., & van Otterlo, M. (2012) · Adaptation, learning, and optimization · doi:10.1007/978-3-642-27645-3_19 · Cited by 19
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Matías, J. D., Blasco, J. J. D. S., Nicolás, G. L., et al. (2009) · Remote Sensing · doi:10.3390/rs1040829 · Cited by 19
Geolocation of Multiple Targets from Airborne Video Without Terrain Data
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Cai, J., Liu, Q., Chen, F., et al. (2014) · doi:10.1145/2578726.2578780 · Cited by 19
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Hassaballah, M., Alshazly, H. A., & Ali, A. A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-030-03000-1_5 · Cited by 19
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Taira, H., Inoue, Y., Torii, A., & Okutomi, M. (2015) · IPSJ Transactions on Computer Vision and Applications · doi:10.2197/ipsjtcva.7.84 · Cited by 19
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“SIFT [75] is first used to find correspondences between frame pairs, followed by the RANSAC algorithm to remove mismatches.”
Nonparametric Label-to-Region by search
Liu, X., Yan, S., Luo, J., et al. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540033 · Cited by 19
Local Descriptor by Zernike Moments for Real-Time Keypoint Matching
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Fang, X., Luo, B., Zhao, H., et al. (2010) · IET Computer Vision · doi:10.1049/iet-cvi.2009.0025 · Cited by 19
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Zheng, X., Liu, X., Chen, J., & Zhu, E. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22655 · Cited by 20
SLAM in O(log n) with the Combined Kalman - Information filter
Cadena, C., & Neira, J. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354521 · Cited by 20
Human motion segmentation and recognition using machine vision for mechanical assembly operation
Jiang, Q., Liu, M., Wang, X., et al. (2016) · SpringerPlus · doi:10.1186/s40064-016-3279-x · Cited by 19
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Zhou, X., Wang, K., & Fu, J. Y. (2016) · International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) · doi:10.1109/iciicii.2016.0029 · Cited by 19
Metadata
- Authors
- Lowe, D. G.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Lowe, David G.
- ORCID
- Lowe, David G.
- Venue
- International Journal of Computer Vision
- Published
- 2004-11
- Volume
- 60
- Issue
- 2
- Pages
- 91-110
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2151103935
- Semantic Scholar
- 8c04f169203f9e55056a6f7f956695babe622a38
- Cited by
- 55,589
- References
- 50
- Citations by year
- 2026: 1480 · 2025: 2400 · 2024: 2519 · 2023: 2604 · 2022: 2705 · 2021: 3066 · 2020: 2933 · 2019: 3240 · 2018: 3232 · 2017: 3363 · 2016: 3431 · 2015: 3747 · 2014: 3569 · 2013: 3333 · 2012: 3003
- FWCI
- 699.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- 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 › Robotics and Sensor-Based Localization
- Keywords
- feature matching, affine invariance, rotation invariance, object recognition, nearest neighbor matching, Hough transform, pose estimation, occlusion
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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