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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Robust monocular SLAM in dynamic environments
Tan, W., Liu, H.-M., Dong, Z., Zhang, G., & Bao, H. (2013) · International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2013.6671781 · Cited by 306
MixVPR: Feature Mixing for Visual Place Recognition
Ali-bey, A., Chaib-draa, B., & Giguère, P. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00301 · Cited by 262
Periocular biometrics in the visible spectrum: A feasibility study
Park, U., Ross, A. A., & Jain, A. K. (2009) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2009.5339068 · Cited by 260
A search-classify approach for cluttered indoor scene understanding
Nan, L., Xie, K., & Sharf, A. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366156 · Cited by 240
Advanced perception, navigation and planning for autonomous in-water ship hull inspection
Hover, F. S., Eustice, R. M., Kim, A., Englot, B., Johannsson, H., Kaess, M., & Leonard, J. J. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364912461059 · Cited by 249
Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features
Yu, Z., Jiang, X., Zhou, F., Qin, J., Ni, D., Chen, S., Lei, B., & Wang, T. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2866166 · Cited by 253
Bejiga, M. B., Zeggada, A., Nouffidj, A., & Melgani, F. (2017) · Remote Sensing · doi:10.3390/rs9020100 · Cited by 266
Entropy-based active learning for object recognition
Holub, A. D., Perona, P., & Burl, M. C. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563068 · Cited by 262
Low-Cost and Open-Source Solutions for Automated Image Orientation – A Critical Overview
Fabio Remondino, A., Pizzo, S. D., Kersten, T. P., & Troisi, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34234-9_5 · Cited by 251
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Zhang, X., Peng, F., & Long, M. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2838334 · Cited by 254
Exploring features in a Bayesian framework for material recognition
Liu, C., Sharan, L., Adelson, E. H., & Rosenholtz, R. E. (2010) · 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.2010.5540207 · Cited by 244
High-quality passive facial performance capture using anchor frames
Beeler, T., Hahn, F., Bradley, D., Bickel, B., Beardsley, P. A., Gotsman, C., Sumner, R. W., & Groß, M. H. (2011) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1964921.1964970 · Cited by 242
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Guo, Y., Gao, Y., & Shen, D. G. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2508280 · Cited by 258
Sparse points matching by combining 3D mesh saliency with statistical descriptors
Castellani, U., Cristani, M., Fantoni, S., et al. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01162.x · Cited by 252
Multi-scale gray level co-occurrence matrices for texture description
de Siqueira, F. R., Schwartz, W. R., & Pedrini, H. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.09.042 · Cited by 252
Roayaei Ardakany, A., Gezer, H. T., Lonardi, S., & Ay, F. (2020) · Genome Biology · doi:10.1186/s13059-020-02167-0 · Cited by 250
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Towards autonomous robotic butlers: Lessons learned with the PR2
Bohren, J., Rusu, R., Jones, E., Marder-Eppstein, E., Pantofaru, C., Wise, M., Mosenlechner, L., Meeussen, W., & Holzer, S. (2011) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980058 · Cited by 276
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Zhou, H., Ummenhofer, B., & Brox, T. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_50 · Cited by 226
2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors
Bansal, M., Kumar, M., & Kumar, M. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10646-0 · Cited by 253
Unsupervised 3D Object Recognition and Reconstruction in Unordered Datasets
Brown, M., & Lowe, D. G. (2005) · International Conference on 3-D Digital Imaging and Modeling (3DIM) · doi:10.1109/3dim.2005.81 · Cited by 248
Self-citationOverview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
Holm, E. A., Cohn, R., Gao, N., Kitahara, A. R., Matson, T. P., Lei, B., & Yarasi, S. R. (2020) · Metallurgical and Materials Transactions A · doi:10.1007/s11661-020-06008-4 · Cited by 280
Non-rigid dense correspondence with applications for image enhancement
HaCohen, Y., Shechtman, E., Goldman, D. B., & Lischinski, D. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964965 · Cited by 247
CSFL: A novel unsupervised convolution neural network approach for visual pattern classification
Rehman, S. U., Tu, S., Huang, Y., & Liu, G. (2017) · AI Communications · doi:10.3233/aic-170739 · Cited by 248
Bailer, C., Taetz, B., & Stricker, D. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.457 · Cited by 236
Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction
Luo, Y., Tao, D., Ramamohanarao, K., Xu, C., & Wen, Y. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2445757 · Cited by 247
Multiple Kernel Learning for Dimensionality Reduction
Lin, Y., Liu, T.-L., & Fuh, C. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.183 · Cited by 245
Pinto, N., Doukhan, D., DiCarlo, J. J., & Cox, D. D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000579 · Cited by 248
Sewa db: A rich database for audio-visual emotion and sentiment research in the wild
Kossaifi, J., Walecki, R., Panagakis, Y., Shen, J., Schmitt, M., Ringeval, F., Han, J., Pandit, V., Toisoul, A., Schuller, B., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2944808 · Cited by 243
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Lu, X. B., Lin, Z., Jin, H., Yang, J., & Wang, J. Z. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2477040 · Cited by 241
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Huang, G. B., Mattar, M., Lee, H., & Miller, E. G. (2012) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 270
Local line directional pattern for palmprint recognition
Luo, Y., Zhao, L.-Y., Zhang, B., Jia, W., Xue, F., Lu, J., Zhu, Y., & Xu, B.-Q. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.025 · Cited by 246
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Parekh, V. S., & Jacobs, M. A. (2019) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2019.1585805 · Cited by 255
Landmark classification in large-scale image collections
Li, Y., Crandall, D., & Huttenlocher, D. P. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459432 · Cited by 245
Jiang, H., Hu, X., Li, K., Zhang, J., Gong, J., & Zhang, M. (2020) · Remote Sensing · doi:10.3390/rs12030484 · Cited by 246
Scalable logo recognition in real-world images
Romberg, S., Pueyo, L. G., Lienhart, R. W., & van Zwol, R. (2011) · International Conference on Multimedia Retrieval · doi:10.1145/1991996.1992021 · Cited by 248
Scene Modelling, Recognition and Tracking with Invariant Image Features
Skrypnyk, I., & Lowe, D. G. (2005) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2004.53 · Cited by 247
Self-citationSurvey of computer vision algorithms and applications for unmanned aerial vehicles
Al-Kaff, A., Martín, D., García, F., Escalera, A. D. L., & María Armingol, J. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.033 · Cited by 246
AFEW-VA database for valence and arousal estimation in-the-wild
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Corner detector based on global and local curvature properties
He, X., & Yung, N. H. C. (2008) · Optical Engineering · doi:10.1117/1.2931681 · Cited by 245
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Yan, Y., Ni, B., Song, Z., Ma, C., Yan, Y., & Yang, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_42 · Cited by 250
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Wei, S., Ji, S., & Lü, M. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2954461 · Cited by 259
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Hamdoun, O., Moutarde, F., Stanciulescu, B., & Steux, B. (2008) · ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) · doi:10.1109/icdsc.2008.4635689 · Cited by 238
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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.
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.
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