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 3,760
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 2,360
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 2,030
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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Weng, R., Lu, J., & Tan, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2515987 · Cited by 144
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
Seo, H. J., & Milanfar, P. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.153 · Cited by 144
Evaluation of 3D interest point detection techniques via human-generated ground truth
Dutağacı, H., Cheung, C. P., & Godil, A. (2012) · The Visual Computer · doi:10.1007/s00371-012-0746-4 · Cited by 142
Scene reconstruction and visualization from internet photo collections
Seitz, S. M., Szeliski, R., & Snavely, K. N. (2008) · Cited by 139
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Reconfigurable models for scene recognition
Parizi, S. N., Oberlin, J., & Felzenszwalb, P. F. (2012) · 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.2012.6248001 · Cited by 144
Recognition of Images Degraded by Gaussian Blur
Flusser, J., Farokhi, S., Höschl, C., et al. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2512108 · Cited by 144
Region-based SIFT approach to iris recognition
Belcher, C., & Du, E. Y. (2008) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2008.07.004 · Cited by 143
Delving deeper into convolutional neural networks for camera relocalization
Wu, J., Ma, L., & Hu, X. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989663 · Cited by 148
Real-time monocular object SLAM
Gálvez‐López, D., Salas, M., Tardós, J. D., & Montiel, J. M. M. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2015.08.009 · Cited by 137
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Registration of Multimodal Remote Sensing Image Based on Deep Fully Convolutional Neural Network
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Lee, K.-H., & Hwang, J. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2455418 · Cited by 139
Li, H., Huang, D., Morvan, J., et al. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-014-0785-6 · Cited by 143
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Xiao, Y., Wu, J., & Yuan, J. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2295756 · Cited by 143
Hugenholtz, C. H., Brown, O. W., Walker, J., et al. (2016) · GEOMATICA · doi:10.5623/cig2016-102 · Cited by 143
Artieda, J., Sebastian, J. M., Campoy, P., Correa, J. F., Mondragon, I. F., Martínez, C., & Olivares, M. (2009) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-008-9304-8 · Cited by 142
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Ahmad, J., Farman, H., & Jan, Z. (2018) · SpringerBriefs in computer science · doi:10.1007/978-981-13-3459-7_3 · Cited by 165
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Haghighi, F., Taher, M. R. H., Zhou, Z., Gotway, M. B., & Liang, J. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3060634 · Cited by 151
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Ali-bey, A., Chaib-draa, B., & Giguère, P. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.127 · Cited by 161
Kersten, T. P., & Lindstaedt, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34234-9_1 · Cited by 143
Real-time image-based 6-DOF localization in large-scale environments
Lim, H., Sinha, S. N., Cohen, M. F., & Uyttendaele, M. (2012) · 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.2012.6247782 · Cited by 143
Fraundorfer, F., Tanskanen, P., & Pollefeys, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_20 · Cited by 138
Soft Margin Multiple Kernel Learning
Xu, X., Tsang, I. W.-H., & Xu, D. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2237183 · Cited by 139
Shi, Y., Qi, F., Xue, Z., Chen, L., Ito, K., Matsuo, H., & Shen, D. G. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.908130 · Cited by 142
A Survey on Map-Based Localization Techniques for Autonomous Vehicles
Chalvatzaras, A., Pratikakis, I., & Amanatiadis, A. A. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3192102 · Cited by 163
3D facial expression recognition using SIFT descriptors of automatically detected keypoints
Berretti, S., Ben Amor, B., Daoudi, M., & del Bimbo, A. (2011) · The Visual Computer · doi:10.1007/s00371-011-0611-x · Cited by 138
LNIFT: Locally Normalized Image for Rotation Invariant Multimodal Feature Matching
Li, J., Xu, W., Shi, P., Zhang, Y., & Hu, Q. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3165940 · Cited by 148
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Peng, C., Gao, X., Wang, N., & Li, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2542816 · Cited by 139
Fan, J., Wu, Y., Li, M., Liang, W., & Cao, Y. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2815523 · Cited by 140
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Allan, M., Sebastien Ourselin, A., Thompson, S., Hawkes, D. J., Kelly, J. D., & Stoyanov, D. V. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2229278 · Cited by 139
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Zerr, S., Siersdorfer, S., Hare, J. S., & Demidova, E. (2012) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2348283.2348292 · Cited by 148
A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes
Creusot, C., Pears, N. E., & Austin, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0605-9 · Cited by 142
Deep roto-translation scattering for object classification
Oyallon, E., & Mallat, S. (2014) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2015.7298904 · Cited by 304
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He, Y., Xiang, S., Kang, C., Wang, J., & Pan, C. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2558463 · Cited by 137
Jigsaw puzzles with pieces of unknown orientation
Gallagher, A. (2012) · 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.2012.6247699 · Cited by 141
Unified Vision‐Based Methodology for Simultaneous Concrete Defect Detection and Geolocalization
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Meyers, E. M., & Wolf, L. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0058-8 · Cited by 143
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Shi, Q., Cheng, L., Wang, L., & Smola, A. J. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0384-0 · Cited by 137
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Do, V. T., & Chong, U. (2011) · Strojniški vestnik – Journal of Mechanical Engineering · doi:10.5545/sv-jme.2010.162 · Cited by 139
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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.
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