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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Haselhoff, A., Schauland, S., & Kummert, A. (2008) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2008.4621252 · Cited by 21
A relational vector space model using an advanced weighting scheme for image retrieval
Martinet, J., Chiaramella, Y., & Mulhem, P. (2010) · Information Processing & Management · doi:10.1016/j.ipm.2010.10.003 · Cited by 18
Suriyamongkol, T., & Mali, I. (2018) · Copeia · doi:10.1643/ch-18-101 · Cited by 18
k -SLAM: A fast RGB-D SLAM approach for large indoor environments
Güçlü, O., & Can, A. B. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.04.005 · Cited by 18
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Tan, X., Mulay, O., Xie, J., MacDonald, S., Kim, T., Zhou, C., Xiong, Z., Tan, S. X., Ye, N., Reed, A. E. M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68487-0 · Cited by 18
Object Tracking Method Based on SURF
Shuo, H., Na, W., & Huajun, S. (2012) · AASRI Procedia · doi:10.1016/j.aasri.2012.11.055 · Cited by 18
Overlapping node discovery for improving classification of lung nodules
Zhang, F., Cai, W., Song, Y., et al. (2013) · doi:10.1109/embc.2013.6610785 · Cited by 18
Recent Advances in 3D Object Detection for Self-Driving Vehicles: A Survey
Fawole, O. A., & Rawat, D. B. (2024) · AI · doi:10.3390/ai5030061 · Cited by 25
“…relied on geometric modeling and manual feature extraction techniques like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) to data-driven approaches has greatly improved the capability to directly interpret complex spatial data from the environment [66,67].”
MethodSwarm Reconnaissance Drone System for Real-Time Object Detection Over a Large Area
Moon, S., Jeon, J., Kim, D., & Kim, Y. (2023) · IEEE Access · doi:10.1109/access.2022.3233841 · Cited by 20
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Ao, S., Guo, Y., Tian, J., et al. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107186 · Cited by 18
Meng, F., Qin, Y., Wu, Y., Shao, C., & Jia, L. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102393 · Cited by 18
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Charmette, B., Royer, E., & Chausse, F. (2016) · Machine Vision and Applications · doi:10.1007/s00138-016-0759-5 · Cited by 18
.K., M. M., & Mohan Shajee, B. S. (2016) · doi:10.1109/infosci.2016.7845313 · Cited by 18
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Mai, X., Li, W., Huang, Y., et al. (2018) · doi:10.1109/irce.2018.8492946 · Cited by 18
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Fan, B., Wu, F., & Hu, Z. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.07.029 · Cited by 18
Gao, F., Zielke, O., Han, Z., et al. (2021) · Tectonophysics · doi:10.1016/j.tecto.2021.229195 · Cited by 18
Tao, Y., Douté, S., Müller, J., et al. (2021) · Remote Sensing · doi:10.3390/rs13112185 · Cited by 18
Chen, M., Yan, S., Qin, R., et al. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.09.002 · Cited by 18
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Long, X., Hu, S., Hu, Y., Gu, Q., & Ishii, I. (2019) · Sensors · doi:10.3390/s19173707 · Cited by 18
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Moulon, P., & Bezzi, A. (2011) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 18
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Fernández, J., Cañas, J. M., Fernández, V., & Paniego, S. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4632353 · Cited by 26
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Jiang, D., & Yi, J. (2012) · International Conference on Computer Science and Service System · doi:10.1109/csss.2012.572 · Cited by 19
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Vácha, P., Haindl, M., & Suk, T. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.01.002 · Cited by 18
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Shaw, D., & Barnes, N. M. (2006) · Cited by 18
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Zhelezniakov, A., Eerola, T., Koivuniemi, M., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27863-6_21 · Cited by 18
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Zhang, H., Sun, Z., Tan, T., & Wang, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25449-9_11 · Cited by 18
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HEASK: Robust homography estimation based on appearance similarity and keypoint correspondences
Yan, Q. Y., Xu, Y., Yang, X., & Nguyen, T. Q. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.05.007 · Cited by 16
Choi, S.-K., Ramirez, R., & Kwon, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20225 · Cited by 18
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Cai, Y., Huang, K., & Tan, T. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761704 · Cited by 18
Botha, T. R., & Schalk, P. (2015) · Journal of Terramechanics · doi:10.1016/j.jterra.2015.05.003 · Cited by 18
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Li, B., Yang, J., Zeng, X.-Y., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2925087 · Cited by 18
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Ehlers, S. F. G., Stuede, M., Nuelle, K., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196997 · Cited by 18
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Song, D., Liu, W., Zhou, T., et al. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2438553 · Cited by 18
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Lipski, C., Linz, C., Neumann, T., et al. (2010) · doi:10.1109/cvmp.2010.12 · Cited by 18
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Kim, D., Choi, S., & Sohn, K. (2011) · Journal of Electronic Imaging · doi:10.1117/1.3624499 · Cited by 18
Bilal, M. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0403 · Cited by 18
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Yang, Y.-B., Li, Y., Gao, Y., et al. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065714500245 · Cited by 18
Tang, C.-P., Chen, K., & Lin, T. (2021) · Sensors · doi:10.3390/s21165315 · Cited by 18
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Campoy, P., Correa, J. F., Mondragon, I. F., et al. (2008) · Springer eBooks · doi:10.1007/978-1-4020-9137-7_8 · Cited by 18
Zelnik‐Manor, L., & Perona, P. (2007) · International Symposium on Non-Photorealistic Animation and Rendering (NPAR) · doi:10.1145/1274871.1274890 · Cited by 18
Mei, Z., Xu, H., Yan, L., & Wang, K. (2025) · Measurement · doi:10.1016/j.measurement.2025.117701 · Cited by 21
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Ommer, B., & Buhmann, J. M. (2005) · Lecture notes in computer science · doi:10.1007/11585978_16 · Cited by 18
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Pele, O., & Taskar, B. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40020-9_43 · Cited by 18
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Amato, G. M., Falchi, F., & Gennaro, C. (2011) · doi:10.1145/1995412.1995428 · Cited by 18
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Ulukaya, S., & Erdem, Ç. E. (2014) · Digital Signal Processing · doi:10.1016/j.dsp.2014.05.013 · Cited by 18
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Korytkowski, M. (2016) · Integrated Computer-Aided Engineering · doi:10.3233/ica-160534 · Cited by 18
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Huang, J., Tsai, C.-H., & Huang, S. T. (2012) · Communications of the ACM · doi:10.1145/2093548.2093570 · Cited by 18
Tahir, M. A., Yan, F., Koniusz, P., et al. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2264927 · Cited by 18
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Zhong, J., Qiao, H., Chen, L., Shang, M., & Liu, Q. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533647 · Cited by 20
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Ye, C., Hong, S., & Qian, X. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974058 · Cited by 18
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Liu, R., Sun, Z., Wang, A., et al. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01536-7 · Cited by 18
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Gil, A., Reinoso, Ó., Vicente, M. A., et al. (2005) · Lecture notes in computer science · doi:10.1007/11492429_75 · Cited by 18
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Yang, H., Mou, W., Zhang, Y., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.03148 · Cited by 18
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Raj, R., & Joseph, N. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.223 · Cited by 18
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Zhao, C., Sain, A., Qu, Y., et al. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2854273 · Cited by 18
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Li, X. S., & Guo, Y. (2014) · Cited by 16
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Ward, I. R., Laga, H., & Bennamoun, M. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-28603-3_8 · Cited by 21
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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