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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Items 3,001–3,100 of 56,204
Kumar, S., Mohri, M., & Talwalkar, A. (2009) · Cited by 114
Dandawate, Y., & Kokare, R. (2015) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2015.7275707 · Cited by 109
Semantic manifold learning for image retrieval
Lin, Y., Liu, T.-L., & Chen, H.-T. (2005) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1101149.1101193 · Cited by 106
Recurrent Scale Approximation for Object Detection in CNN
Liu, Y., Li, H., Yan, J., Wei, F., Wang, X., & Tang, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.69 · Cited by 98
Tree quantization for large-scale similarity search and classification
Babenko, A., & Lempitsky, V. (2015) · 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.2015.7299052 · Cited by 106
Street View Motion-from-Structure-from-Motion
Klingner, B. M., Martín, D., & Roseborough, J. B. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.122 · Cited by 109
Global Structure-from-Motion Revisited
Pan, L., Baráth, D., Pollefeys, M., & Schönberger, J. L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73661-2_4 · Cited by 124
Prosdocimi, M., Burguet, M., Prima, S. D., et al. (2016) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2016.09.036 · Cited by 109
MVE—An image-based reconstruction environment
Fuhrmann, S., Langguth, F., Moehrle, N., Waechter, M., & Goesele, M. (2015) · Computers & Graphics · doi:10.1016/j.cag.2015.09.003 · Cited by 108
Novel Tactile-SIFT Descriptor for Object Shape Recognition
Luo, S., Mou, W., Althoefer, K. A., & Liu, H. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2432127 · Cited by 104
Poletaev, I., Tokarev, M. P., & Pervunin, K. S. (2019) · International Journal of Multiphase Flow · doi:10.1016/j.ijmultiphaseflow.2019.103194 · Cited by 112
Autonomous Vision‐based Micro Air Vehicle for Indoor and Outdoor Navigation
Schmid, K., Lutz, P., Tomić, T., Mair, E., & Hirschmüller, H. (2014) · Journal of Field Robotics · doi:10.1002/rob.21506 · Cited by 109
Toews, M., & Arbel, T. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.233 · Cited by 109
Detecting the shuttlecock for a badminton robot: A YOLO based approach
Cao, Z., Liao, T., Song, W. W., Chen, Z., & Li, C. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113833 · Cited by 112
Transform coding for fast approximate nearest neighbor search in high dimensions
Brandt, J. (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.2010.5539852 · Cited by 107
Robust local binary pattern feature sets for periocular biometric identification
Xu, J., Cha, M., Heyman, J. L., Venugopalan, S., Abiantun, R., & Savvides, M. (2010) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2010.5634504 · Cited by 112
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Goh, H., Thome, N., Cord, M., & Lim, J. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2307532 · Cited by 107
Müller‐Linow, M., Pinto, F., Scharr, H., & Rascher, U. (2015) · Plant Methods · doi:10.1186/s13007-015-0052-z · Cited by 108
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Liu, Y., Zha, H., & Qin, H. (2006) · 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.2006.278 · Cited by 106
Chertok, M., & Keller, Y. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.51 · Cited by 103
Approximate Correspondences in High Dimensions
Grauman, K. L., & Darrell, T. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0068 · Cited by 111
Modeling the spatial layout of images beyond spatial pyramids
Sánchez, J., Perronnin, F., & de Campos, T. E. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.07.019 · Cited by 107
Visual analysis of asphalt pavement for detection and localization of potholes
Yousaf, M. H., Azhar, K., Murtaza, F., & Hussain, F. (2018) · Advanced Engineering Informatics · doi:10.1016/j.aei.2018.09.002 · Cited by 112
Discriminative Dictionary Learning With Common Label Alignment for Cross-Modal Retrieval
Deng, C. D., Tang, X., Yan, J., Liu, W., & Gao, X. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2508146 · Cited by 99
Matching by Tone Mapping: Photometric Invariant Template Matching
Hel-Or, Y., Hel-Or, H. Z., & David, E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.138 · Cited by 106
Geomorphic contexts and science focus of the Zhurong landing site on Mars
Liu, J., Li, C., Zhang, R., et al. (2021) · Nature Astronomy · doi:10.1038/s41550-021-01519-5 · Cited by 108
ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning
Deng, Y., & Ma, J. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3231135 · Cited by 111
Max-Margin Multiple-Instance Dictionary Learning
Wang, X., Wang, B., Bai, X., Liu, W., & Tu, Z. (2013) · Cited by 111
Multi-View Object Retrieval via Multi-Scale Topic Models
Hong, R., Hu, Z., Wang, R., Wang, M., & Tao, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2614132 · Cited by 109
Depth-Aware Multi-Grid Deep Homography Estimation With Contextual Correlation
Nie, L., Lin, C., Liao, K., Liu, S., & Zhao, Y. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3125736 · Cited by 108
Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification
Luo, Y., Wen, Y., Tao, D., Gui, J., & Xu, C. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2495116 · Cited by 106
A Review on Computer Vision-Based Methods for Human Action Recognition
Al-Faris, M., Chiverton, J. P., Ndzi, D. L., & Ahmed, A. I. (2020) · Journal of Imaging · doi:10.3390/jimaging6060046 · Cited by 119
Takeo Kanade, A., & Hebert, M. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2200554 · Cited by 105
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Zhou, G., Liu, W., Zhu, Q., Lu, Y., & Liu, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3215802 · Cited by 112
Hu, B., Tang, Y., Chang, E., Fan, Y., Lai, M., & Xu, Y. L. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2852639 · Cited by 110
Cortical response selectivity derives from strength in numbers of synapses
Scholl, B., Thomas, C. I., Ryan, M. A., Kamasawa, N., & Fitzpatrick, D. (2020) · Nature · doi:10.1038/s41586-020-03044-3 · Cited by 102
Object Tracking by Oversampling Local Features
Pernici, F., & Bimbo, A. D. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.250 · Cited by 104
Three-dimensional point cloud recognition via distributions of geometric distances
Mahmoudi, M., & Sapiro, G. (2008) · Graphical Models · doi:10.1016/j.gmod.2008.10.002 · Cited by 108
Yu, D., Xu, Q., Guo, H., Zhao, C., Lin, Y., & Li, D. (2020) · Sensors · doi:10.3390/s20071999 · Cited by 118
3-line RANSAC for orthogonal vanishing point detection
Bazin, J., & Pollefeys, M. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385802 · Cited by 106
Object-Based Activity Recognition with Heterogeneous Sensors on Wrist
Maekawa, T., Yanagisawa, Y., Kishino, Y., Ishiguro, K., Kamei, K., Sakurai, Y., & Okadome, T. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12654-3_15 · Cited by 105
Text-based image retrieval using progressive multi-instance learning
Li, W., Duan, L., Xu, D., & Tsang, I. W.-H. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126478 · Cited by 107
Semantic structure from motion with points, regions, and objects
Bao, S. Y., Bagra, M., Chao, Y.-W., & Savarese, S. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247992 · Cited by 107
Reduced SIFT Features For Image Retrieval And Indoor Localisation
Ledwich, L., & Williams, S. B. (2004) · Cited by 107
Kakaletsis, E., Symeonidis, C., Tzelepi, M., Mademlis, I., Tefas, A., Nikolaidis, N., & Pitas, I. (2021) · ACM Computing Surveys · doi:10.1145/3472288 · Cited by 117
Improving "bag-of-keypoints" image categorisation: Generative Models and PDF-Kernels
Farquhar, J. D. R., Szedmák, S., Meng, H., & Shawe-Taylor, J. S. (2005) · Brunel University Research Archive (BURA) (Brunel University London) · Cited by 107
De Silva, T., Uneri, A., Ketcha, M. D., Reaungamornrat, S., Kleinszig, G., Vogt, S., Aygun, N., Lo, S.-F., Wolinsky, J.-P., & Siewerdsen, J. H. (2016) · Physics in Medicine & Biology · doi:10.1088/0031-9155/61/8/3009 · Cited by 107
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BackgroundConsensus learning guided multi-view unsupervised feature selection
Tang, C., Chen, J., Liu, X., Li, M., Wang, P., Wang, M., & Lü, P. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.06.016 · Cited by 103
Campbell, J. D., Gibbons, P. B., Nath, S., Pillai, P. S., Seshan, S., & Sukthankar, R. (2005) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1101149.1101162 · Cited by 107
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Xu, X., Li, W., & Xu, D. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2405574 · Cited by 104
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Whyte, O., Šivic, J., & Zisserman, A. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.116 · Cited by 102
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Choi, Y., Lee, T., & Oh, S. (2007) · Autonomous Robots · doi:10.1007/s10514-007-9050-y · Cited by 107
Hussain, L., Ahmed, A., Saeed, S., et al. (2017) · Cancer Biomarkers · doi:10.3233/cbm-170643 · Cited by 107
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A descriptor for large scale image retrieval based on sketched feature lines
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Aicardi, I., Chiabrando, F., Grasso, N., Lingua, A. M., Noardo, F., & Spano, A. T. (2016) · 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-xli-b1-835-2016 · Cited by 141
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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.
What counts as a claim ›How we measure confidence ›How we check this ›
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