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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Kawahara, I., Hosoda, Y., & Lee, J.-H. (2026) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur69537.2026.11626773
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Luchka, V.-T., Vdovychenko, V., & Furgala, Y. (2024) · Electronics and Information Technologies · doi:10.30970/eli.25.7
Maı̂tre, H. (2015) · doi:10.1002/9781119238447.biblio
Fourt, O., Rouveure, R., & Faure, P. (2014) · International Journal of Signal Processing Image Processing and Pattern Recognition · doi:10.14257/ijsip.2014.7.2.04
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Gherabi, N., & Bahaj, M. (2019) · arXiv (Cornell University)
Giammarino, L. D., Sun, B., Grisetti, G., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73016-0_12
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Chen, X., Jian-wen, C., & Wang, Y. (2020) · International Conference on Multimedia and Image Processing (ICMIP) · doi:10.1145/3381271.3381298
Yanhan, 张. Z., Yinxin, 张. Z., Zhanhua, 黄. H., et al. (2024) · Laser & Optoelectronics Progress · doi:10.3788/lop230657
Hijab, M. H. F., Shamhan, A. N. M., Houki, Z. E., Qaraqe, M., & Al‐Thani, D. (2026) · Autism Research · doi:10.1002/aur.70249
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Web Image Gathering with a Part-Based Object Recognition Method
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Ivanova, E., Ramp, P., Zimmer, N., et al. (2026) · Metabolic Engineering · doi:10.1016/j.ymben.2026.102523
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Li, W., Ma, X., Wang, L., et al. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727400
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Murabayashi, N., Kurahashi, S., & Yoshida, K. (2009) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.24.203
Deebika, R., Sowmitha, V., Sivasundaram, M., et al. (2026) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss69635.2026.11645928
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Liu, H.-Q., Li, Y., & Li, T. (2015) · Communications in computer and information science · doi:10.1007/978-3-662-47791-5_12
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Chen, Q., Miao, R., & Lee, F. (2025) · International Conference on Information and Network Technologies (ICINT) · doi:10.1109/icint65528.2025.11030879
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Picard, D., & Gosselin, P.-H. (2015) · HAL
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Dehghani, A., & Sutherland, A. (2013) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/iranianmvip.2013.6779959
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Siddique, A., Browne, W. N., & Grimshaw, G. M. (2026) · IEEE Access · doi:10.1109/access.2026.3660716 · Cited by 2
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Miao, X., Zhao, W., Lu, T., Xu, L., Yu, M., Long, Y., Pang, J., & Dong, J. (2026) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3799902.3811184
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Yan, C., Wang, K., Wang, Y., et al. (2026) · Information Sciences · doi:10.1016/j.ins.2026.124083
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Li, Y., Zhang, Y., Liu, C., et al. (2017) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2017.8015973
Improved SIFT algorithm with camera orientation
Zhang, S., Qin, Z., & Fang, D. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.899864
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Li, H., & Jia, Y. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-6496-8_19
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FudaSys Video Retrieval in TRECVID 2012.
Huang, J., Ouyang, S., Xu, Q. Q., et al. (2012)
GeoSAM: A Sampling and Attention Mechanism for Cross-View Geo-Localisation
Wu, F., & Zhou, H. (2024) · doi:10.1109/icftic64248.2024.10912902
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Liang, J., Shulakova, V., Kempton, R. H., Piane, C. D., Perera, S., Pervukhina, M., Clennell, M. B., Hancock, L., Brooks, D., & Wawryk, M. (2026) · SPE Journal · doi:10.2118/232786-pa
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Hu, Z. Y. (2013) · Advanced materials research · doi:10.4028/www.scientific.net/amr.846-847.770
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Kyutoku, H., Deguchi, D., Takahashi, T., et al. (2014) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.134.702
Getahun, M. N., Anikin, D., Shmatok, A., et al. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133156
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Rezaei, A., Aldea, E., Dondi, P., et al. (2023) · Journal on Computing and Cultural Heritage · doi:10.1145/3575866
Laga, H., Guo, Y., Tabia, H., Fisher, R. B., & Bennamoun, M. (2018) · Wiley eBooks · doi:10.1002/9781119405207.refs
Liu, F., Li, Y., & Wang, M. (2026) · Applied Sciences · doi:10.3390/app16084029
MORRIS, H. C., Eyadat, M. S., & Muhi, I. (2010) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-61520-903-3.ch012
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Horii, K., Maeda, K., Ogawa, T., et al. (2017) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce.2017.8229261
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Banno, A., & Ikeuchi, K. (2008) · Springer eBooks · doi:10.1007/978-0-387-75807_2
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Li, H., Zhao, L., Zheng, J., & Zhang, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4867953
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Eigenmaps, H., Si, S., Tao, D., et al. (2010)
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Junior, E. A. D. V., Cord, M., & Philipp‐Foliguet, S. (2007) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2007.4377001
Komorowski, J., & Trzcinski, T. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.11062
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Halawani, A., Réhman, S. U., & Li, H. (2015)
Object Description Techniques in Real-Life Environments: a Review for the Underwater Scenario
Salvo, R. D., & Pino, C. (2013) · International Conference on Computer Science and Electronics Engineering (ICCSEE) · doi:10.2991/iccsee.2013.412
Shanmugapriyan, T., & Gurusamy, M. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.112247
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Cleveston, I. (2021) · doi:10.47749/t/unicamp.2021.1165396
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Sharma, B., & Rakshit, S. (2008) · doi:10.1109/icvgip.2008.20
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Xu, M. G., Zhang, F., Nie, Y., et al. (2025) · doi:10.1109/icaisisas64483.2025.11052202
CS-BoW: a scalable parallel image recognition method
Liu, Y. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2268411
Ikeuchi, K., Matsushita, Y., Sagawa, R., et al. (2020) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-56577-0_2
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Chennamma, H. R., Rangarajan, L., & Rao, M. S. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-515-5.ch018
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Yan, W., Wang, S., Yue, G., et al. (2018) · EasyChair preprint · doi:10.29007/ctsv
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Progressive correspondence learning by effective multi-channel aggregation
Liu, X., Chen, S., Xiao, G., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127368
Robot-Based Creation of Complete 3D Workpiece Models
Singer, D., Rohner, D., & Henrich, D. (2022) · Springer eBooks · doi:10.1007/978-3-030-74032-0_24
Yu, S.-I., Yang, Y., Xu, Z., Xu, S., Meng, D., Mao, Z., Ma, Z., Lin, M., Li, X., Li, H., et al. (2016)
Hybrid SURFGolay Marker Detection Method for Augmented Reality Applications.
Procházka, D., Popelka, O., Koubek, T., et al. (2012)
Visual Concept Detection and Annotation via Multiple Kernel Learning of Multiple Models
Zhang, Y., Bres, S., & Chen, L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41184-7_59
Détection de fausses informations dans les réseaux sociaux
Maigrot, C. (2019) · theses.fr (ABES) · doi:10.70675/69d409bcze00az41f5z98e4zea9ad5cf9157 · Cited by 1
Mery, D., & Pieringer, C. (2020) · Springer eBooks · doi:10.1007/978-3-030-56769-9_1
Overlapping local image descriptor
Chen, M., Men, A., Fan, P., et al. (2009) · doi:10.1109/peits.2009.5406753
XRefine: Attention-Guided Keypoint Match Refinement
Schmid, J. F., & Hagemann, A. (2026) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv69130.2026.00023
“Thus, our model only needs to be trained once and generalizes across a wide range of classical (e.g., SIFT [20]) and learned (e.g., SuperPoint [5], ALIKED [34]) detectors without requiring per-detector adaptation.”
InfluentialUnordered Image Stitching Based on the Registration Index Table
Si, Z. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2303/1/012076
Yu, L., Zhao, H., & Meng, J. (2026) · doi:10.1145/3821966.3822058
“To solve these problems, this project adopts the SIFT-ORB dual-branch feature fusion strategy (Lowe, 2004; Rublee et al., 2011) [5] [9], a scene-adaptive Gaussian 𝜎 optimization system, and a joint Poisson modeling pipeline for image editing and surface reconstruction (Pérez et al., 2003; Kazhdan…”
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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