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 2,030
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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Tscharf, A., Rumpler, M., Fraundorfer, F., Mayer, G., & Bischof, H. (2015) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-1-w1-15-2015 · Cited by 41
Moghimi, A., Çelik, T., Mohammadzadeh, A., & Kusetoğulları, H. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3069919 · Cited by 41
Recognizing Activities with Multiple Cues
Biswas, R., Thrun, S., & Fujimura, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-75703-0_18 · Cited by 40
Unsupervised Footwear Impression Analysis and Retrieval from Crime Scene Data
Kortylewski, A., Albrecht, T., & Vetter, T. (2014) · ACCV Workshops · doi:10.1007/978-3-319-16628-5_46 · Cited by 49
Incremental Learning With Open-Set Recognition for Remote Sensing Image Scene Classification
Liu, W., Nie, X., Zhang, B., & Sun, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3173995 · Cited by 40
Plant Identification by Image Processing of Leaf Veins
Selda, J. D. S., Ellera, R. M. R., Cajayon, L. C., & Linsangan, N. B. (2017) · International Conference on Imaging, Signal Processing and Communications (ICISPC) · doi:10.1145/3132300.3132315 · Cited by 42
PMatch: Paired Masked Image Modeling for Dense Geometric Matching
Zhu, S., & Liu, X. (2023) · 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/cvpr52729.2023.02098 · Cited by 45
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Mohamadian, Z., & Pouyan, A. A. (2013) · UKSim International Conference on Computer Modelling and Simulation (UKSim) · doi:10.1109/uksim.2013.94 · Cited by 40
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Peng, Y., Lin, X., Ma, N., Du, J., Liu, C.-W., Liu, C., & Chen, Q. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113176 · Cited by 52
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Pirker, K., Rüther, M., & Bischof, H. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094588 · Cited by 49
Meilland, M., Comport, A., & Rives, P. (2015) · J. Field Robotics · doi:10.1002/rob.21531 · Cited by 45
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Mery, D. (2014) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2014.2311032 · Cited by 40
Data Association Using Visual Object Recognition for EKF-SLAM in Home Environment
Ahn, S., Choi, M., Choi, J., & Chung, W. K. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.281936 · Cited by 40
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Hejazifar, H., & Khotanlou, H. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-017-1231-3 · Cited by 40
Amirgaliyev, B., Mussabek, M., Rakhimzhanova, T., & Zhumadillayeva, А. (2025) · Sensors · doi:10.3390/s25051410 · Cited by 41
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Toews, M., & Arbel, T. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.892510 · Cited by 41
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Elibol, A., Gracias, N., & García, R. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.10.010 · Cited by 40
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Kong, S., Shen, X., Lin, Z., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01621 · Cited by 40
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Moradi, M., Abolmaesoumi, P., & Mousavi, P. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.652132 · Cited by 40
Mahami, A., Rahmoune, C., Bettahar, T., et al. (2021) · Advances in Mechanical Engineering · doi:10.1177/16878140211060956 · Cited by 40
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Lin, J., Duan, L., Huang, Y., Luo, S., Huang, T., & Gao, W. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2296532 · Cited by 40
Toward Dynamic Image Mosaic Generation With Robustness to Parallax
Qi, Z., & Cooperstock, J. R. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2162743 · Cited by 40
DIARC: a testbed for natural human-robot interaction
Schermerhorn, P., Kramer, J., Middendorff, C., et al. (2006) · Cited by 40
A recent survey on the applications of genetic programming in image processing
Khan, A., Qureshi, A. S., Wahab, N., Hussain, M., & Hamza, M. Y. (2021) · Computational Intelligence · doi:10.1111/coin.12459 · Cited by 51
Toward automated model building from video in computer-assisted diagnoses in colonoscopy
Koppel, D., Chen, C.-I., Wang, Y.-F., Lee, H., Gu, J., Poirson, A. B., & Wolters, R. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.709595 · Cited by 40
Classification and recognition scheme for vegetable pests based on the BOF-SVM model
Xiao, D., Feng, J., Lin, T., et al. (2018) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20181103.3477 · Cited by 40
Perspective-aware warping for seamless stereoscopic image cloning
Luo, S., Shen, I., Chen, B., et al. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366201 · Cited by 40
Robust multi-view feature matching from multiple unordered views
Yao, J., & Cham, W.-K. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.02.011 · Cited by 39
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Li, M., Lining, S., Huang, Q., Zesu, C., & Piao, S. (2010) · Information Technology Journal · doi:10.3923/itj.2011.29.39 · Cited by 40
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Qu, Y., Lin, L., Shen, F., Lu, C., Wu, Y., Xie, Y., & Tao, D. (2017) · UTS ePRESS (University of Technology Sydney) · Cited by 39
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Yu, Y., Cao, Z., Liu, Z., Geng, W., Yu, J., & Zhang, W. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3018757 · Cited by 42
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Zhuang, L., Zhong, X., Xu, L., Tian, C., & Yu, W.-S. (2024) · Italian National Conference on Sensors · doi:10.3390/s24102980 · Cited by 46
“A Scale-Invariant Feature Transform (SIFT) algorithm was proposed by [44] to extract robust features to scale, including illumination, view changes, image rotation, and noise.”
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Yuan, K., Tang, D., Liao, X., Wang, X., Feng, X., Chen, Y., Sun, M., Lu, H., & Zhang, K. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00032 · Cited by 42
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Kümmerle, R., Grisetti, G., & Burgard, W. (2012) · Advanced Robotics · doi:10.1080/01691864.2012.728694 · Cited by 41
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Hurtós, N., Nagappa, S., Cufí, X., et al. (2013) · doi:10.1109/oceans-bergen.2013.6608124 · Cited by 40
Wang, Q., Wang, J., Hu, S., Zou, Q., & Ren, K. (2016) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/2897845.2897861 · Cited by 42
Thermal 3D mapping for object detection in dynamic scenes
Weinmann, M., Leitloff, J., Hoegner, L., et al. (2014) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-1-53-2014 · Cited by 40
Direction matters: Depth estimation with a surface normal classifier
Häne, C., Ladický, Ľ., & Pollefeys, M. (2015) · 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.2015.7298635 · Cited by 39
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Li, Y., Zeng, J., & Shan, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3011063 · Cited by 40
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Verykokou, S., & Ioannidis, C. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1444294 · Cited by 44
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Rochefort, G., Champagnat, F., Besnerais, G. L., et al. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881996 · Cited by 40
Feng, S., Gao, M., Jin, X., Zhao, T., & Yang, F. (2023) · Measurement · doi:10.1016/j.measurement.2023.113844 · Cited by 41
Efficient 2D-3D Matching for Multi-Camera Visual Localization
Geppert, M., Liu, P., Cui, Z., Pollefeys, M., & Sattler, T. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794280 · Cited by 37
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Cheng, Y., Zhao, X., Huang, K., & Tan, T. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.412 · Cited by 41
Sun, S., Liu, Y., & Mao, L. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.10.004 · Cited by 38
Skurikhin, A. N., Garrity, S. R., McDowell, N. G., & Cai, D. M. (2012) · Remote Sensing Letters · doi:10.1080/2150704x.2012.749361 · Cited by 40
Hand vein recognition based on multiple keypoints sets
Wang, Y., Fan, Y., Liao, W., Li, K., Shark, L., & Varley, M. R. (2012) · International Conference on Biometrics · doi:10.1109/icb.2012.6199778 · Cited by 42
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Xiong, W., Xiong, Z., Cui, Y., & Lv, Y. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2980870 · Cited by 40
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Maeng, H.-J., Choi, H.-C., Park, U., et al. (2011) · doi:10.1109/ijcb.2011.6117486 · Cited by 40
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Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Metaxas, D., & Zhou, X. S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_35 · Cited by 39
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Huang, B.-R., Lin, C. H., & Lee, C. (2012) · doi:10.1109/icasid.2012.6325354 · Cited by 40
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Kim, H., & Lee, S. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.03.014 · Cited by 39
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Schmaltz, C., Rosenhahn, B., Brox, T., et al. (2011) · Machine Vision and Applications · doi:10.1007/s00138-010-0317-5 · Cited by 40
The intermediate matching kernel for image local features
Boughorbel, S., Tarel, J., & Boujemaa, N. (2006) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1555970 · Cited by 40
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Ommer, B., & Buhmann, J. M. (2006) · Lecture notes in computer science · doi:10.1007/11744078_25 · Cited by 40
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Iwata, T., Yamada, T., & Ueda, N. (2009) · Cited by 40
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Robust vision-based robot localization using combinations of local feature region detectors
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Schlag, I., & Arandjelović, O. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.342 · Cited by 40
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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