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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Xu, P., Liu, X., Yao, H., Zhang, Y., & Tang, S. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738050 · Cited by 2
Optimized algorithm for Gaussian second order derivative filters in fast Hessian detector
Han, B., Jia, X., & Wang, Y. (2010) · doi:10.1109/iccsit.2010.5564462 · Cited by 2
DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep LearningPreprint
Bian, Y., Wenskovitch, J. E., & North, C. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.15800 · Cited by 2
Automatic Detection of Robots’ Manipulator End-Effector Position Using Sift Algorithm
Maric, P. S., & Djalic, V. (2011) · Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium · doi:10.2507/22nd.daaam.proceedings.775 · Cited by 2
Katramados, I., & Breckon, T. P. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532367 · Cited by 2
Tracking multiple pedestrians through detection failures in videos
Jiang, Z., & Xu, B. (2015) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais.2015.7338705 · Cited by 2
Single-scan multiple object detection based on template matching using only effective pixels
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Li, X., He, L., & He, X. (2023) · doi:10.1117/12.2680008 · Cited by 2
Prakash, S., & Gupta, P. (2015) · Augmented vision and reality · doi:10.1007/978-981-287-375-0_5 · Cited by 2
Object retrieval in past video using bag-of-words model
Nguyen-Hoang, M.-T., Le, T.-K., Ninh, V.-T., Che, Q.-H., Nguyen, V.-T., & Tran, M. (2017) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais.2017.8217565 · Cited by 2
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Ngo, P., Kenmochi⋆, Y., Sugimoto, A., et al. (2016) · Discrete Applied Mathematics · doi:10.1016/j.dam.2016.05.005 · Cited by 2
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Wang, Q., Xu, H., Li, S., et al. (2010) · doi:10.1109/icise.2010.5691608 · Cited by 2
IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS
Rézio, A. C. C., Schwartz, W. R., & Pedrini, H. (2012) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0003861701350144 · Cited by 2
Machine Vision System for Inline Inspection in Carbide Insert Production
Schmitt, R. H., Scholl, I., Cai, Y., et al. (2010) · Springer eBooks · doi:10.1007/978-1-84996-432-6_77 · Cited by 2
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Lemaître, A., & Camillerapp, J. (2013) · Cited by 2
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Yan, L., Lixin, H., & Feng, G. (2016) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2016.7474853 · Cited by 2
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Fu, Z., Luo, B., Wu, C., et al. (2016) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits.2016.7546457 · Cited by 2
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Gupta, R. K. (2014) · Cited by 2
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Zhu, Q., Yigitbasi, N., & Pillai, P. S. (2011) · doi:10.1109/ocs.2011.9 · Cited by 2
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Zhu, C., Dong, H., & Zhang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2951471 · Cited by 2
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Aly, M., Munich, M. E., & Perona, P. (2012) · doi:10.1109/wacv.2012.6162995 · Cited by 2
Guo-Rong, C., Shaozi, L., Wu, Y., et al. (2011) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2011.9727838 · Cited by 2
Jinka, S. S., & Sharma, A. K. (2018) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3293353.3293426 · Cited by 2
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Xu, S., Wang, Y., Luo, X., Yoon, B.-J., & Qian, X. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00217 · Cited by 2
“Scale-equivariance and invariance : The concept of scale-equivariance and invariance was first proposed in image processing and computer vision [33, 34].”
Development of An Effective 3D Mapping Technique for Heritage Structures
Mahinda, M. C. P., Udhyani, H. P. A. J., Alahakoon, P. M. K., et al. (2021) · doi:10.1109/eecon52960.2021.9580945 · Cited by 2
Improved-YOLOv3 network for object detection in simulated Space Solar Power Systems images
Wang, G., Lei, N., & Liu, H. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9189240 · Cited by 2
Target Recognition in SAR Image via Keypoint based Local Descriptor—FoundationPreprint
Dong, G., & Chanussot, J. (2018) · Preprints.org · doi:10.20944/preprints201805.0116.v1 · Cited by 2
Image based reconstruction for cystoscopy
Brischwein, M., Wittenberg, T. C., & Bergen, T. (2015) · Current Directions in Biomedical Engineering · doi:10.1515/cdbme-2015-0113 · Cited by 2
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Wang, Q., Liang, Y., Wang, Z., et al. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-32-9686-2_36 · Cited by 2
Visual SLAM Location Methods Based on Complex Scenes: A Review
Zhang, H.-X., & Peng, J.-S. (2020) · International Conference on Adaptive and Intelligent Systems · doi:10.1007/978-3-030-57881-7_43 · Cited by 2
Author Profiling: Gender Prediction from Tweets and Images: Notebook for PAN at CLEF 2018.
HaCohen‐Kerner, Y., Yigal, Y., Shayovitz, E., Miller, D., & Breckon, T. P. (2018) · Durham Research Online (Durham University) · Cited by 2
Se, S., & Pears, N. E. (2020) · Springer eBooks · doi:10.1007/978-3-030-44070-1_2 · Cited by 2
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Birk, A. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509322 · Cited by 2
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Güçlü, O., & Can, A. B. (2016) · doi:10.1109/icarsc.2016.42 · Cited by 2
Processo automático de reconhecimento de texto em imagens de documentos de identificação genéricos.
Romero, R. V. (2018) · doi:10.11606/d.3.2018.tde-05032018-151842 · Cited by 2
SO-PERM: Pose Estimation and Robust Measurement for Small Objects
Wang, J., & Yu, N. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892659 · Cited by 2
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Liu, C., Zhang, Z., He, Y., et al. (2025) · Electronics · doi:10.3390/electronics14050940 · Cited by 2
EgoVision a YOLO-ViT hybrid for robust egocentric object recognition
Sadima, U. E., Alkhrijah, Y. M., Hamid, D., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-18341-y · Cited by 2
“The early methods that used Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) 3,4 succeeded in controlled environments yet faced difficulties with real-world variations, including lighting changes, occlusions, and deformations.”
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine LearningPreprint
Tramèr, F., Dupré, P., Rusak, G., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.03194 · Cited by 2
Analyzing Classical and LDI Depth-Aware Image Stitching for Enhanced Virtual View Representation
Venjarski, J., Tibenský, Š., & Rozinaj, G. (2023) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip58668.2023.10180238 · Cited by 2
A new generalized affine moment invariants for shape retrieval and object recognition
Ding, H., Li, X., Zhao, H., et al. (2012) · doi:10.1109/isict.2012.6291609 · Cited by 2
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Kasamwattanarote, S., Uchida, Y., & Satoh, S. (2016) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2015edp7193 · Cited by 2
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Local scales on curves and surfaces
Le, T., & Mémoli, F. (2012) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2012.03.001 · Cited by 2
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Wang, K., Chen, Y., Gao, P., & Karimian, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4758755 · Cited by 2
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Tanaka, K., Aoki, T., Tomita, T., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3516082 · Cited by 2
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Rodriguez, M. P., Facciolo, G., von Gioi, R. G., et al. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191245 · Cited by 2
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Sinha, A., Murez, Z., Bartolozzi, J., et al. (2020) · arXiv (Cornell University) · Cited by 2
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Mitchell, H. B. (2010) · Springer eBooks · doi:10.1007/978-3-642-11216-4_3 · Cited by 2
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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
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.
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