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.
Cited by
Sorted by citations
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Bergen, T., & Wittenberg, T. C. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2384134 · Cited by 104
Image Based Localization in Indoor Environments
Liang, J., Corso, N., Turner, E., et al. (2013) · doi:10.1109/comgeo.2013.11 · Cited by 106
Self Tuning Texture Optimization
Kaspar, A., Neubert, B., Lischinski, D., et al. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12565 · Cited by 106
Learning-Based Symmetry Detection in Natural Images
Tsogkas, S., & Kokkinos, I. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33786-4_4 · Cited by 97
The One-Shot similarity kernel
Wolf, L., Hassner, T., & Taigman, Y. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459323 · Cited by 109
Scaramuzza, D. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6 · Cited by 109
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
Wang, Q., Xie, J., Zuo, W., Zhang, L., & Li, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2974833 · Cited by 109
Chaiyasarn, K., Buatik, A., Mohamad, H., Zhou, M., Kongsilp, S., & Poovarodom, N. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104388 · Cited by 109
Binder, M. D., Hirokawa, N., & Windhorst, U. (2008) · Springer eBooks · doi:10.1007/978-3-540-29678-2_1162 · Cited by 116
Action Recognition with Exemplar Based 2.5D Graph Matching
Yao, B., & Fei-Fei, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_13 · Cited by 101
Towards high-resolution large-scale multi-view stereo
Hiep, V. H., Keriven, R., Labatut, P., & Pons, J.-P. (2009) · 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.2009.5206617 · Cited by 106
Learning to Distribute Vocabulary Indexing for Scalable Visual Search
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Object recognition as ranking holistic figure-ground hypotheses
Li, F., Carreira, J., & Sminchisescu, C. (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.2010.5539839 · Cited by 105
Learning to Hallucinate Face Images via Component Generation and Enhancement
Song, Y., Zhang, J., He, S., Bao, L., & Yang, Q. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/633 · Cited by 111
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
Chen, Y., & Chen, C.-S. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.926152 · Cited by 107
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
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Stochastic Multiview Hashing for Large-Scale Near-Duplicate Video Retrieval
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Key target and defect detection of high-voltage power transmission lines with deep learning
Liu, Z., Wu, G., He, W., Fan, F., & Ye, X. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108277 · Cited by 114
Deep Learning for Free-Hand Sketch: A Survey
Xu, P., Hospedales, T. M., Yin, Q., Song, Y.-Z., Xiang, T., & Wang, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148853 · Cited by 192
Reconfigurable AUV for intervention missions: a case study on underwater object recovery
Prats, M., Ribas, D., Palomeras, N., García, J. C., Nannen, V., Wirth, S., Fernandez, J. J., Beltrán, J. P., Campos, R., Ridao, P., et al. (2011) · Intelligent Service Robotics · doi:10.1007/s11370-011-0101-z · Cited by 103
Luan, H., Qi, F., Xue, Z., Chen, L., & Shen, D. G. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.04.002 · Cited by 105
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Fritz, G., Seifert, C., & Paletta, L. (2006) · International Conference on Computer Vision Systems · doi:10.1109/icvs.2006.5 · Cited by 105
Real-time human detection using contour cues
Wu, J., Geyer, C., & Rehg, J. M. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980437 · Cited by 105
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Ferreira, B., Rodrigues, J., Leitao, J. C. A., & Domingos, H. (2017) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2017.2669999 · Cited by 106
Feurer, D., & Vinatier, F. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.10.016 · Cited by 105
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Ji, X., Vedaldi, A., & Henriques, J. F. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00996 · Cited by 223
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Meng, X., Yang, G., Yin, Y., & Xiao, R. (2012) · Sensors · doi:10.3390/s121114937 · Cited by 105
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Ghassabi, Z., Shanbehzadeh, J., Sedaghat, A., & Fatemizadeh, E. (2013) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2013-25 · Cited by 106
InfAR dataset: Infrared action recognition at different times
Gao, C., Du, Y., Liu, J., Lv, J. G., Yang, L., Meng, D., & Hauptmann, A. G. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.05.094 · Cited by 101
Region duplication detection based on Harris corner points and step sector statistics
Chen, L., Lu, W., Ni, J., Sun, W., & Huang, J. (2013) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2013.01.008 · Cited by 105
DeepSFM: Structure from Motion via Deep Bundle Adjustment
Wei, X., Zhang, Y., Li, Z., Fu, Y., & Xue, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_14 · Cited by 112
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Roy, S., & Sun, Q. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379535 · Cited by 107
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Asano, Y. M., Rupprecht, C., & Vedaldi, A. (2019) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1904.13132 · Cited by 129
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Kaur, P., Harnal, S., Gautam, V., et al. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105210 · Cited by 104
Lejsek, H., Ásmundsson, F. H., Jónsson, B. Þ., & Amsaleg, L. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.130 · Cited by 101
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Zhang, T., Jia, W., He, X., & Yang, J. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2589858 · Cited by 107
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Zhang, X., Leng, C., Hong, Y., Pei, Z., Cheng, I., & Basu, A. (2021) · Remote Sensing · doi:10.3390/rs13245128 · Cited by 115
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Wang, C.-C., & Wang, K.-C. (2007) · Springer eBooks · doi:10.1007/978-3-540-76729-9_25 · Cited by 105
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Symmetric architecture modeling with a single image
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Liu, X., Miao, X., Jiang, H., & Chen, J. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.09.002 · Cited by 127
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Recent advances in 3D SEM surface reconstruction
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Ono, D., Mukai, Y., Hung, C. J., Chowdhury, S., Sugiyama, T., & Yamanaka, A. (2020) · Science Advances · doi:10.1126/sciadv.abd0384 · Cited by 104
Visual event recognition in videos by learning from web data
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Vieira, T. F., Bottino, A., Laurentini, A., & Simone, M. D. (2013) · The Visual Computer · doi:10.1007/s00371-013-0884-3 · Cited by 102
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Yanulevskaya, V., Uijlings, J., Bruni, E., Sartori, A., Zamboni, E., Bacci, F., Melcher, D., & Sebe, N. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2393399 · Cited by 103
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Oyallon, E., & Rabin, J. (2015) · Image Processing On Line · doi:10.5201/ipol.2015.69 · Cited by 107
Seeland, M., Rzanny, M., Alaqraa, N., Wäldchen, J., & Mäder, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170629 · Cited by 106
Senst, T., Eiselein, V., Kühn, A., & Sikora, T. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2725820 · Cited by 103
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Crespo, J., Castillo, J. C., Mozos, Ó. M., & Barber, R. (2020) · Applied Sciences · doi:10.3390/app10020497 · Cited by 105
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Zhang, C., Yu, Z., Hu, Q., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11722 · Cited by 103
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Yuan, J., Yu, S., & Guo, L. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218593 · Cited by 103
Argyris, Y. A., Muqaddam, A., & Miller, S. R. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102347 · Cited by 124
Chen, C., Wang, B., Lu, C. X., Trigoni, N., & Markham, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12567 · Cited by 145
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Perronnin, F., Akata, Z., Harchaoui, Z., & Schmid, C. (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.2012.6248090 · Cited by 114
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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