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
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Kesorn, K. (2013) · KKU ENGINEERING JOURNAL · doi:10.5481/kkuenj.2013.40.1.1
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Zhang, S., Yang, M., Wang, Y., et al. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-1904-0_34
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Jad, N. V., & Hamde, S. T. (2019) · Lecture notes in networks and systems · doi:10.1007/978-981-13-3765-9_4
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Geração automática de orto-mosaicos de fotos aéreas de arquivo do Concelho de Coimbra
Gonçalves, J. A., Manta, V., & Carvalho, M. (2015) · Imprensa da Universidade de Coimbra eBooks · doi:10.14195/978-989-26-0983-6_44
Absolute Localization by Mono-camera for a Vehicle in Urban Area using Street View
Yu, L. (2018) · theses.fr (ABES) · doi:10.70675/8b314205zb09ez4208za110zd1ebb59a823c
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Das, B., Swain, D., Balabantaray, B. K., et al. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-4859-2_51
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Complete 3D Modeling from Rotational Devices
Chen, S., Liu, D. W., Wang, X., et al. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.317-319.843
Scene Recognition for Complicated UAV Images Based on Land Surface Classification of Superpixel
Shi, Y., Li, H., Ding, W., et al. (2017) · doi:10.1145/3080845.3080847
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Variable-Weight and Multi-index Similarity Measure for Feature Fusion
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Liu, L. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/166053
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Xu, X., & Zhang, X. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1104.1472
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“SIFT [5] is a representative example of these methods.”
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Fernández, R. V. (2019) · doi:10.20868/upm.thesis.64067
The Evaluation Methods Based on Image Features Comparison
Xu, C. L. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.644-650.4197
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Li, C., Zhu, S., Su, R., & Liu, J. (2025) · International Conference on Computer Engineering and Intelligent Control (ICCEIC) · doi:10.1109/icceic67916.2025.11309098
Scene Interpretation in Images and Videos
Jakkoju, C., Jawahar, C. V., & Krishna, M. (2011)
Siddiqui, J. R., & Khatibi, S. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.694
Towards Geometric Modeling of the Atria using Optical Coherence Tomography
Gan, Y., Gutbrod, S. R., Efimov, I. R., et al. (2016) · doi:10.1364/cancer.2016.jm3a.26
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Liu, G. (2010) · Chinese Conference on Pattern Recognition · doi:10.1109/ccpr.2010.5659152
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Neto, O. M. D. S., & Rudek, M. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-62578-7_7
Кривоножко, А. М., Явтушенко, В., Самокиш, А. В., et al. (2020) · Системи озброєння і військова техніка · doi:10.30748/soivt.2020.63.03
Blind Facial Image Quality Enhancement Using Non-Rigid Semantic Patches
Hait-Fraenkel, E., & Gilboa, G. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2686003
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Balasalle, J. (2011) · Digital Commons - DU (University of Denver)
Bhalerao, R. H., & Gedam, S. S. (2011) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2011.6049190
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Gao, Z., Zhao, Z., Liu, T., et al. (2009) · doi:10.1109/cinc.2009.42
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Nakshbandi, M.-M., Sharawy, Z., & Grigorescu, S. M. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097447
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Li, K., Jia, H., Chen, M., et al. (2022) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis58238.2022.00106
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Zielke, O. (2024) · Springer remote sensing/photogrammetry · doi:10.1007/978-3-031-59306-2_4
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Chen, H., Kang, Z., Lai, T., et al. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-7273-8_19
Steedly, D., & Essa, I. A. (2004)
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Yan, C., Wang, K., Chang, L., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6661504
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Snyder, J. S. (2015)
Image processing techniques to extract symbolic features from Atari video games
Ferrando, H. M. (2017)
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Shape band: A deformable object detection approach
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Yang, Z. Q., Kong, Y., & Fu, Y. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.15
Visual recognition based on randomized visual dictionaries
Zhang, R., Li, B., & Gao, H. (2012) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2012.6511431
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Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0008
Gao, Q., Xu, Y., Amason, J., Loksztejn, A., Cousins, S. W., Pajić, M., & Hadziahmetovic, M. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.31
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Xue, W., Mou, X., & Lei Zhang, A. (2015) · Communications in computer and information science · doi:10.1007/978-3-662-48558-3_42
Research on binocular ranging method based on feature point extraction and matching
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Designing and selecting features for MR image segmentation
Yang, M., Yuan, Y., Li, X., et al. (2011) · doi:10.1109/acpr.2011.6166535
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Sanchez, A. D. (2018) · doi:10.4321/repisalud.6730
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Chavéz-García, R. O., Montes, M., & Sucar, L. E. (2011)
Research on the Detection and Recognition Algorithm of Click Chinese Character Verification Code
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Zhang, D., Liu, B., Sun, C., & Wang, X. (2011) · Computer and Information Science · doi:10.5539/cis.v4n3p116
Electron Neutrino Classification in Liquid Argon Time Projection Chamber Detector
Płoński, P., Stefan, D., Sulej, R., et al. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-26227-7_7
Image classification with limited data information
Cheng, H. (2023) · doi:10.32657/10356/174167
Image Processing for Feature Extraction
Li, H. (2025) · Springer eBooks · doi:10.1007/978-981-96-7344-5_2 · Cited by 1
Panoramic Stitching of Reactor Core Image
Li, X., Jiang, H., Wang, X., et al. (2022) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai55851.2022.9904086
An improved method to detect and locate vehicles in images
Zhou, L., Shang, E., Song, J., et al. (2012) · doi:10.1109/omee.2012.6343587
Tang, S., & Shen, Q.-Q. (2026) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice68970.2026.11465686
“Early methods relied on handcrafted features and traditional techniques[3], such as SVM[4], SIFT[5], and HOG[6], which are computationally expensive and limited in capturing multi-scale semantic information.”
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Pertoft, J. (2016) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Saylan, A. A., Önal, O., Ören, A. H., et al. (2018) · Springer series in geomechanics and geoengineering · doi:10.1007/978-3-319-97112-4_161
Salhi, A., Melouah, N., Hayet, F. M., et al. (2017) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661817010114
Topological Localization of Mobile Robots in Real Indoor Environment
Park, Y.-B., Suh, I.-H., & Choi, B.-U. (2009) · The Journal of Korea Robotics Society
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Zhao, Y., Feng, Y., Wang, Y., et al. (2021) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2021-01-7017
Data Acquisition in Geographic Information Systems
Škaloud, J., Cramer, M., & Haala, N. (2022) · Springer handbooks · doi:10.1007/978-3-030-53125-6_9
Liu, Y. F., & Zhang, J. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108768
Study and implementation of visual SLAM algorithms in photogrammetry and computer vision
Petsa, M., & Grammatikopoulos, L. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-31574-9.00018-8
Features extraction based on the Discrete Hartley Transform for closed contour
Martí-Puig, P., Reig-Bolaño, R., & Danés, J. (2015) · AI Communications · doi:10.3233/aic-140620
Region Contrastive Camera LocalizationPreprint
Sarıgül, M., & Karacan, L. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4105115
Accurate matching method of multimodal image based on phase congruency and local mutual information
Zhao, C., Zhao, H., & Zhao, G. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2070198
Image retrieval using morphology based Scale Invariant Feature Transform
W. Han, W. S. S. &. Z. Y. Y. (2015) · CRC Press eBooks · doi:10.1201/b17732-36
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Huang, Y., Li, W., Wu, S., et al. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-54528-3_26
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Fani, A., Doan, M., Le, I., et al. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122074
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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