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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Each is checked against this paper’s own text: does it say what the citing paper says it does?
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.
Cited by
Sorted by citations
Items 53,901–54,000 of 56,204
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Ren, Q., Zhou, B., & Guo, W. (2019) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2019.10165
Coarse Registration of Point Cloud Base on Deep Local Extremum Detection and Attentive DescriptionPreprint
Lu, H., & Nie, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4088388
Feature-matching-based specific object segmentation
Cui, X., Wang, Z., & Chen, T. (2010) · International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) · doi:10.1109/ispacs.2010.5704620
Gong, M., Wang, D., Peng, M., et al. (2025) · Sixth Symposium on Novel Optoelectronic Detection Technology and Applications · doi:10.1117/12.3055140
“Common feature point extraction algorithms include Harris [4] , SUSAN [5] , FAST [6] , SIFT [7] and SURF [8] .”
Segmentation Based Fine Registration of Very High Resolution Multi-Temporal Images
Tamboli, A. I., Yadav, P., Chauhan, D. K., et al. (2023) · doi:10.1109/icpsitiags59213.2023.10527724
RetractedNatural scene category recognition based on multiple channels of PHOW
Lu, F., Zhang, R., & Yu, S. (2011) · International Conference on Image Analysis and Signal Processing · doi:10.1109/iasp.2011.6109053
A RFM Pattern Recognition System Invariant to Rotation, Scale and Translation
Solorza-Calderón, S., & Verdugo-Olachea, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25751-8_57
PRESCAN Adaptive Vehicle Image Real-Time Stitching Algorithm Based on Improved SIFT
Li, Q., Xu, Y., & Ding, P. (2023) · International Journal of Information Technologies and Systems Approach · doi:10.4018/ijitsa.321754
Rajesh, A., & Kouser, R. R. (2025) · International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) · doi:10.1109/iccams65118.2025.11234462
“Early robotic vision systems utilized handcrafted keypoint detectors and descriptors like SIFT [3] and SURF [4].”
Algorithms for photo book authoring
Egorova, M. A., & Safonov, I. V. (2013) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661813010045
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Fang, Z., & Zhang, Z. M. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.673
Yang, Z. (2012) · Institutional Repositories DataBase (IRDB)
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Tie, Y., & Guan, L. (2021) · doi:10.32920/14669016
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Bae, B.-J., & Park, J.-S. (2010) · The KIPS Transactions PartB · doi:10.3745/kipstb.2010.17b.5.345
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朱, 苗. (2020) · Computer Science and Application · doi:10.12677/csa.2020.103059
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Egbash, E. K., & Mohammad, S. (2017) · Iraqi Journal of Science
Fisher, R. B. (2020) · Springer eBooks · doi:10.1007/978-3-030-03243-2_189-1
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Du, B., Wang, Y., & Ren, P. (2014) · doi:10.1109/mfi.2014.6997709
Mignon, A., Bronisz, A., Hy, R. L., et al. (2017) · IEEE RO-MAN · doi:10.1109/roman.2017.8172468
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Wang, R., & Ma, C. (2023) · International Conference on Signal Processing and Computer Science (SPCS) · doi:10.1117/12.3012485
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Kim, J.-W., & Shim, D.-S. (2014) · The Journal of Advanced Navigation Technology · doi:10.12673/jant.2014.18.2.134
Kumar, V., Reddy, L. Y., Vinnie, Y., et al. (2022) · Emperor Journal of Applied Scientific Research · doi:10.35338/ejasr.2022.4902
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Pavić, D., & Kobbelt, L. P. (2010) · RWTH Publications (RWTH Aachen)
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Wang, Y., & Shebzukhov, Y. (2018) · International Journal of Advanced Network Monitoring and Controls · doi:10.21307/ijanmc-2019-028
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Wang, K., Gao, F., Ma, D., et al. (2026) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2026.104567
Physics-Inspired Prior Learning and Target-Aware Multimodal Fusion for RGB-T Pedestrian Detection
Gao, H., Wang, S., Zheng, Y., Hu, Y., Tang, Z.-R., & Chen, Y. (2025) · International Conference on Computers and Artificial Intelligence Technology (CAIT) · doi:10.1109/cait68620.2025.11424672
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Kender, T. K., Corrias, M., & Franchini, C. (2025) · Advanced Intelligent Discovery · doi:10.1002/aidi.202500043 · Cited by 1
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Novin, I. A. (2014) · TSpace (University of Toronto)
Semi-supervised learning with double head approach for carotid artery detection
Li, Z., Peng, W., & Lu, C. (2022) · International Conference on Biomedical Signal and Image Processing (ICBIP) · doi:10.1145/3563737.3563740
Parachute Autonomous Visual Tracking Technology in Mars Landing Mission
Li, Y., Li, H., Zou, X., et al. (2026) · Springer proceedings in physics · doi:10.1007/978-981-95-1410-6_6
Feature Coverage Indexes for Dual Homography Estimation in Constructing Panorama Image
Jun, K., & Kim, S.-J. (2015) · Journal of Sensors · doi:10.1155/2016/1350750
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Wang, P., Chen, P., Zhang, D., et al. (2012) · Lecture notes in electrical engineering · doi:10.1007/978-1-4614-2185-6_8
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gao, J., Jiang, L., Feng, C.-L., et al. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae761b
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Chen, Y., Xu, J., Feng, W., et al. (2021) · doi:10.1145/3469213.3470679
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Trí, H. V. H., Trâm, V. H., Duy, L. B., et al. (2017) · Nghiên cứu cơ bản và ứng dụng Công nghệ thông tin (FAIR) · doi:10.15625/vap.2016.000102
Honig, N., Teubert, C., Lamparter, L., Keller, M. N., Austermann, J., Berger, P., Schmitz, Ä., Rasch, C., Nüsse, H., Klingauf, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.24.600322
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Xu, Y., Gong, M., Liu, T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07076
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Wu, J., Gu, Y., & Yang, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103736
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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
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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.
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