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.
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Fast and robust binary descriptor using intensity rank binning
Park, C., Kim, J., & Kweon, I. S. (2016) · Electronics Letters · doi:10.1049/el.2016.0863
AnyMatch: Supercharging Universal Multi-modal Image Matching with Large-Scale Single-View Images
Yang, M., Li, Z., Tang, L., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37718-0_12
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Gao, G. (2015) · CRC Press eBooks · doi:10.1201/b19318-12
The Combination of Background Subtraction and Convolutional Neural Network for Product Recognition
Thai, T.-T., Ha, S. V., Nguyen, T. D.-M., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21743-2_22
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Huang, L., Jiang, B., & Xie, Z. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01106-6
“We employ four classical feature extraction algorithms: HOG (Dalal and Triggs 2005), SIFT (Lowe 2004), FAST (Rosten and Drummond 2006), and Canny (Canny 1986).”
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Xiang, Y., & Li, F. (2010) · International Conference on Computer Engineering and Technology · doi:10.1109/iccet.2010.5486353
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Zhang, Q., & Lew, M. S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33885-4_71
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Júnior, M. M. R., Wiedmer, J. P. M., Marochi, P., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3643317
Deep Learning for Video Retrival
Ueki, K. (2017) · The Brain & Neural Networks · doi:10.3902/jnns.24.13
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Hassnaoui, H., BADRI, A. B., Sahel, A., et al. (2017) · International Journal of Computer Applications Technology and Research · doi:10.7753/ijcatr0606.1002
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MacDonald, D. (2008) · PubMed · doi:10.20381/ruor-12181
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Wang, C. (2011) · UWSpace (University of Waterloo)
Lee, S., & Lee, K. M. (2021) · Springer eBooks · doi:10.1007/978-3-030-63416-2_840
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Priyanka, T., Kalaichelvi, M., & Uma Maheswari, C. (2014) · International Journal of Computer Applications Technology and Research · doi:10.7753/ijcatr0301.1011
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Radha, K., & Nithya, J. (2013)
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Pang, Y., Li, K., Pan, J. S., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21963-9_42
Farag, A. A. (2013) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/icmt-13.2013.5
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Zhang, Q., Qi, C., Wang, C., & Xiao, B. (2016) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar.2016.7486746
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Xu, M., & Sun, Y. L. (2012) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2012.6513145
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Jun, J., Kim, T., Lee, J., Jin, T., Park, S., & Jeong, J. (2025) · WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS · doi:10.37394/23207.2025.22.156 · Cited by 1
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Multiple PiPs detection in unbounded video stream
Cui, C., Zhao, Y., Wei, S., et al. (2013) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2013.6618449
Chaudhury, S., Mallik, A., & Ghosh, H. (2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b18639-9
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Le, T., Kang, Y., & Sugimoto, A. (2013) · Medical Entomology and Zoology · doi:10.11371/iieej.42.214
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Xie, Y., & Guan, L. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3628432
McDonagh, J. (2016) · Lincoln Repository (University of Lincoln) · doi:10.24385/lincoln.24325690
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Nejat, P., Alsaafin, A., Alabtah, G., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3429527/v1
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“In practice, a high-quality feature point (e.g., from SIFT [34] matching) is selected as P for each frame pair, and this reference point may change for subsequent estimations, as only the relative transformation, not absolute localization, is needed.”
Image Measurement Attribute Extraction Method under Large Error Condition
Li, F., Du, X., Liu, A., et al. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2456/1/012006
USING AN LOW-COST STEREO CAMERA FOR AUTONOMOUS NAVIGATION OF MOBILE ROBOT
Чибуничев, А. Г., Makarov, A. P., & Полякова, Е. В. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b1-2020-423-2020
Video Retrieval Based on Tracked Features Quantization
Hiroaki, K., Pilet, J., Saitoh, H., et al. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.794
A Robust Instance-based Video Search Method by Re-ranking for Multiple Visual Codebooks
Wang, W., & Zhao, Z. (2012) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2012.44
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Santosh, K. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9181-1_2
Plant identification using local invariants: dense sift approach
Yildiran, S. T. (2015) · Sabanci University
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Chen, X., Wang, W., Ding, Z., & Shang, Y. (2025) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei68103.2025.11259279
“ Multiscale Analysis [22] : Use multi-resolution or multi-scale methods, such as wavelet transform, to calculate the Laplacian response of the image at different scales.”
Yongzhi, 徐. X., Xiaoduo, 王. W., Hao, 罗. L., et al. (2026) · Laser & Optoelectronics Progress · doi:10.3788/lop252646
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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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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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