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
Items 55,801–55,900 of 56,204
Elmaghraby, O., de Araujo, P. R. M., Abdelaziz, S. I. K., et al. (2025) · 2020 IEEE International Systems Conference (SysCon) · doi:10.1109/syscon64521.2025.11014855
“As a result of the recent rise in deep learning techniques, newer detectors and matchers made their debut in the literature, and how such new methods compare to the crowned giants, such as SIFT [2] and ORB [3], remains an open question for us to address.”
Improving Raw Image Storage Efficiency by Exploiting SimilarityPreprint
Zhang, B., Wang, C., Zhou, B. B., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.05442
3D sparse feature model using short baseline stereo and multiple view registration
Watson, M., Bhatti, A., & Nahavandi, S. (2011) · International Conference on Mechatronics
Ma, R., Wang, Y., Yang, Z., et al. (2026) · IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) · doi:10.1109/ivmsp68335.2026.11618813
“The Scale-Invariant Feature Transform (SIFT) algorithm is used to extract feature points from adjacent key frames because it provides local features that are robust to scale and rotation variations [19].”
Vehicles recognition from partial images
Alwan, S., El-Abed, M., & Zantout, R. (2016) · International Conference on Advances in Computational Tools for Engineering Applications · doi:10.1109/actea.2016.7560146
Evaluation of technologies for autonomous visual inspection of additive manufacturing
Finney, G. A., Persons, C. M., Whitten, J. R., et al. (2019) · doi:10.1117/12.2520279
Cascaded Regressions of Learning Features for Face Alignment
Tran, N.-T., Ababsa, F., Fredj, S. B., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25903-1_61
Person re-identification for 365-day video surveillance based on stride convolutional neural network
Wang, S., Zhang, X., Li, R., et al. (2019) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2524371
Interactive semantic segmentation of aerial images with deep neural networks
Lenczner, G. (2022) · theses.fr (ABES) · doi:10.70675/ee3ef5caz8533z4cc0zae24z7acef8f26be8
Detection of Similar Advertisements in Media Databases
Paleček, K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25775-9_18
Airport‐YOLO: A Fixed‐Camera Solution for Airport Object Detection
Zhao, Y., Kong, Z., Hu, Y., Wen, Z., & Wang, S. (2026) · IET Image Processing · doi:10.1049/ipr2.70387 · Cited by 1
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Lu, S., Wei, X., Xiao, T., et al. (2017) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789813143685_0007
Dhewangkoro, H., & Andono, P. N. (2013)
Ling, X., Liu, J., Duan, Z., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4646563
Affonso, A. A. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.106.2023.tde-02022024-211517
Robust Alignment of Multi-Exposed Images with Saturated RegionsPreprint
Jiang, J., Li, Z. G., Xie, S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10872
SIFT Feature Matching Algorithm with Local Shape Context
Zhong, J., Tan, J., & Gu, L. (2013) · International Journal of Applied Mathematics & Statistics/International journal of applied mathematics and statistics
Ali, I., & Shar, N. (2025) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1735
Research on stereo matching methods for long distance sea surface image
Yang, Y., Lu, C., & Chen, C.-H. (2019) · International Conference on Engineering, Applied Sciences, and Technology (ICEAST) · doi:10.1109/iceast.2019.8802587
Self-supervised vision transformers for classification in unconventional shales
Mohammad, R., Devegowda, D., Curtis, M. E., Rai, C., Mudduluru, S., & Maryada, S. K. (2026) · The Leading Edge · doi:10.1190/tle-2025-0061
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Li, W. H., Han, D., Wang, Y., et al. (2006) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2006.1714022
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Heesch, D. C., & Petrou, M. (2007) · Machine learning for signal processing ... · doi:10.1109/mlsp.2007.4414291
Coelho, M. M. C., da Silva, F. A., Pazoti, M. A., et al. (2016) · Colloquium Exactarum · doi:10.5747/ce.2016.v08.n1.e150
Multi-step Bridged Refinement for classifying cross-domian data
Qin, J.-W., Peng, H., Liang, P., et al. (2010) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2010.5580734
Figure-Ground Segmentation—Object-Based
Leibe, B. (2011) · Springer eBooks · doi:10.1007/978-0-85729-997-0_4
Khethisa, J. A. (2017) · Open University of Cape Town (University of Cape Town)
Shape detection of structural changes in long time-span aerial image samples by new saliency methods
Kovács, A., & Szirányi, T. (2010)
Robust Feature Matching Using Haze Removal Based on Transmission Map for Aerial Images
Kwon, O. S. (2016) · Journal of Korea Multimedia Society · doi:10.9717/kmms.2016.19.8.1281
Nesne tanima ve yerseme icin uretimsel bir model
Ulusoy, İ., & Bishop, C. (2006) · IEEE Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2005.1567617
MULTI-VIEW SURFACE RECONSTRUCTION BY QUASI-DENSE WIDE BASELINE MATCHING
Kannala, J., Ylimäki, M., Koskenkorva, P., et al. (2011) · Series in computer vision · doi:10.1142/9789814343008_0020
A Comparison of Two Typical Local Feature Matching Algorithm: SIFT and MSER
Jiang, X. C., Liu, X., Tang, T., et al. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.687-691.4119
Local Structure Preserving Based Subspace Analysis Methods and Applications
Cheng, J., & Lu, H. (2014) · Studies in computational intelligence · doi:10.1007/978-3-642-54851-2_5
Building Recognition Guided by Wi-Fi Based Localization
Bong, W., & Kim, Y. (2014) · Lecture notes in electrical engineering · doi:10.1007/978-94-017-8798-7_61
A study of sequence and function divergence of pilz proteins
Cheang, Q. W. (2018) · doi:10.32657/10356/73213
Image categorization using a semantic hierarchy model with sparse set of salient regions
Liu, C., Zheng, Y., & Gong, S. (2013) · Frontiers of Computer Science · doi:10.1007/s11704-013-2410-1
Robust object re-identification with Grassmann subspace feature for key points aggregation
Noor, D. F., Li, Z., & Nagar, A. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533163
Face recognition using user specific features
Singh, S., Arya, K. V., & Sinha, M. (2014) · doi:10.1109/iciinfs.2014.7036505
Automatic Alignment of 3D Point Clouds to Orthographic Images
Xiong, Z. M., Wan, G., & Cao, X. F. (2012) · Advanced materials research · doi:10.4028/www.scientific.net/amr.591-593.1265
Discovering Intellectual Structure from Digital Archives
Hamagami, T., & Sawada, K. (2014) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.134.1282
Hilbert-Huang Transform-based Local Regions Descriptors
Han, D. W., Li, W., Guo, W., & Li, Z. (2007) · British Machine Vision Conference (BMVC) · doi:10.5244/c.21.16
Self-supervised scale and affine invariant network for image corner detection and description
Liu, K., Zhao, Y., & Lu, J. G. (2025) · doi:10.1109/ijcnn64981.2025.11229126
“…[14]; (3) the FOGDD based methods [2], [13] cannot accurately describe the differences between edges and corners; (4) the SOGDD based methods [3], [14] have not considered how to describe the differences between corners and blobs; (5) the product of the absolute values of FOGDDs and SOGDDs has…”
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Face Recognition Security Enhancement Using DeepFake and FaceNet
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Sinatra, J., & Andono, P. N. (2013)
Mu, D., Fu, L., Cui, T., et al. (2025) · International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) · doi:10.1109/caibda65784.2025.11183185
“Depending on technical approaches, keypoint detection methods are broadly categorized into traditional image processing-based methods (e.g., SIFT [6] , SURF [7] ) and deep learning-based methods.”
VO-Sim: A generic framework for tuning and evaluating visual odometry systems
Alaa, I., & Wassal, A. G. (2018) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans.2018.8373411
Method of Dataset Filling and Recognition of Moving Objects in Video Sequences based on YOLO
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“Often algorithms use key points [19] (feature points) of the image for their work.”
BackgroundVisual Autonomy via 2D Matching in Rendered 3D Models
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Behavior analysis of video object in complicated background
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Takanashi, Y., Shibata, Y., & Kanazawa, Y. (2007)
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Multi-view mosaic algorithm of path analysis by multiple cameras
Han, H., Deng, N., Fang, Y., et al. (2014) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2014.7053686
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“Lowe’s NNDR [9] and RANSAC [15] are widely used to establish reliable correspondences by filtering out outliers.”
Case deletion for fundamental matrix computation
McCane, B. (2014) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1145/2683405.2683416
Grownbb: Gromov–Wasserstein learning of neural best buddies for cross-domain correspondence
Tang, R., Wang, W., Yu, H., & Feng, X. (2024) · The Visual Computer · doi:10.1007/s00371-023-03251-9
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Fei, Y., Sun, B., Kang, Y., et al. (2024) · IEEE International Conference on Signal, Information and Data Processing (ICSIDP) · doi:10.1109/icsidp62679.2024.10868479
Chang, S., Li, L., Hong, B., Liu, J., Xu, Y., Pang, K., Zhang, L., Han, H., & Chen, X. (2023) · Figshare · doi:10.6084/m9.figshare.24189146
High dynamic range for dynamic scenes
Pece, F. (2009) · UCL Discovery (University College London)
Face Granulation Scheme for Identity Proving After Plastic Surgery
Tom, M., & Kizhakkethottam, J. J. (2014) · International Journal of Engineering Trends and Technology · doi:10.14445/22315381/ijett-v13p228
Personalized Video Recommendation Using Rich Contents from VideosPreprint
Du, X., Yin, H., Chen, L., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.06935
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Zhou, Y., Liu, Y., Chen, J., Zhu, G., & Fan, G. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.125088
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Shuai, P., Jiang, P., Deng, F., et al. (2022) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai55851.2022.9904228
A Registration Method Based on Ordered Point Clouds for Key Components of Trains
Yang, K., Deng, X., Bai, Z., et al. (2024) · Sensors · doi:10.3390/s24248146
“The SIFT algorithm [34] employs image pyramids and scale-space analysis to extract feature points, determine their orientation, and create descriptions based on scale and orientation, providing a robust foundation for feature matching.”
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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
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