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 1,801–1,900 of 56,204
Figure-ground segmentation improves handled object recognition in egocentric video
Ren, X., & Gu, C. (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.2010.5540074 · Cited by 167
GNeRF: GAN-based Neural Radiance Field without Posed Camera
Meng, Q., Chen, A., Luo, H., Wu, M., Su, H., Xu, L., He, X., & Jingyi Yu, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00629 · Cited by 186
Qadeer, W., Hameed, R., Shacham, O., et al. (2013) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/2485922.2485925 · Cited by 170
A robust Graph Transformation Matching for non-rigid registration
Aguilar, W., Frauel, Y., Escolano, F. J., et al. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.05.004 · Cited by 170
Learning to Hash with Binary Deep Neural Network
Do, T.-T., Doan, A.-D., & Cheung, N. M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_14 · Cited by 181
Land-use scene classification using multi-scale completed local binary patterns
Chen, C., Zhang, B., Su, H., Li, W., & Wang, L. (2015) · Signal Image and Video Processing · doi:10.1007/s11760-015-0804-2 · Cited by 170
Underwater Target Detection Algorithm Based on Improved YOLOv5
Lei, F., Tang, F., & Li, S. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10030310 · Cited by 177
Learning semantic visual vocabularies using diffusion distance
Liu, J., Yang, Y., & Shah, M. A. (2009) · 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.2009.5206845 · Cited by 176
Curious George: An attentive semantic robot
Meger, D., Forssén, P.-E., Lai, K., Helmer, S., McCann, S., Southey, T., Baumann, M., Little, J. J., & Lowe, D. G. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.03.008 · Cited by 166
Self-citationGopakumar, G., Murali, S., Siva, G. S., & Subrahmanyam, G. R. K. S. (2017) · Journal of Biophotonics · doi:10.1002/jbio.201700003 · Cited by 168
Multi-target tracking using CNN-based features: CNNMTT
Mahmoudi, N., Ahadi, S. M., & Rahmati, M. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6467-6 · Cited by 166
A comparison of methods for non-rigid 3D shape retrieval
Lian, Z., Godil, A., Bustos, B., et al. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.07.014 · Cited by 170
Learning scale-variant and scale-invariant features for deep image classification
van Noord, N., & Postma, E. O. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.06.005 · Cited by 171
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Jens Leitloff, A., Hinz, S., & Stilla, U. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2043109 · Cited by 168
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Rahmani, R., Goldman, S. A., Zhang, H., Cholleti, S. R., & Fritts, J. E. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.112 · Cited by 168
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Xu, Y., Dong, J., Zhang, B., & Xu, D. (2016) · CAAI Transactions on Intelligence Technology · doi:10.1016/j.trit.2016.03.005 · Cited by 169
. Hyper-class augmented and regularized deep learning for fine-grained image classification
Xie, S., Yang, T., Wang, X., & Lin, Y. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298880 · Cited by 169
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Tuytelaars, T., & Schmid, C. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408924 · Cited by 168
Manoonpong, P., Parlitz, U., & Wörgötter, F. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00012 · Cited by 173
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Dahiya, K., Singh, D., & Mohan, C. K. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727586 · Cited by 171
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Fulkerson, B., Vedaldi, A., & Soatto, S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_15 · Cited by 172
Automated detection of third molars and mandibular nerve by deep learning
Vinayahalingam, S., Xi, T., Bergé, S. J., Maal, T., & de Jong, G. (2019) · Scientific Reports · doi:10.1038/s41598-019-45487-3 · Cited by 170
Intrinsic shape context descriptors for deformable shapes
Kokkinos, I., Bronstein, M. M., Litman, R., & Bronstein, A. M. (2012) · 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.2012.6247671 · Cited by 162
Deep Adversarial Multi-view Clustering Network
Li, Z., Wang, Q., Tao, Z., Gao, Q., & Yang, Z. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/409 · Cited by 184
Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)
Alonso, K., Bachmann, M., Burch, K., et al. (2019) · Sensors · doi:10.3390/s19204471 · Cited by 169
Self-Supervised Visual Descriptor Learning for Dense Correspondence
Schmidt, T., Newcombe, R., & Fox, D. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2634089 · Cited by 148
Object detection in remote sensing imagery using a discriminatively trained mixture model
Cheng, G., Han, J., Guo, L., Qian, X., Zhou, P., Yao, X., & Hu, X. (2013) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2013.08.001 · Cited by 168
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Sharan, L., Rosenholtz, R. E., & Adelson, E. H. (2014) · Journal of Vision · doi:10.1167/14.9.12 · Cited by 173
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Khan, M. M., Zhang, Q., Haq, I. U., Butt, S. A., & Jadoon, A. (2017) · BioMed Research International · doi:10.1155/2017/3640901 · Cited by 172
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Lindenberger, P., Sarlin, P.-E., Larsson, V., & Pollefeys, M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00593 · Cited by 202
Gong, J., Caldas, C. H., & Gordon, C. (2011) · Advanced Engineering Informatics · doi:10.1016/j.aei.2011.06.002 · Cited by 174
Fritz, A., Kattenborn, T., & Koch, B. (2013) · 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/isprsarchives-xl-1-w2-141-2013 · Cited by 169
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Khan, N. Y., Brendan Mccane, A., & Wyvill, G. (2011) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2011.90 · Cited by 165
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Color-Based Segmentation of Sky/Cloud Images From Ground-Based Cameras
Dev, S., Lee, Y. H., & WINKLER, S. S. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2558474 · Cited by 171
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Garcia-Fidalgo, E., & Ortiz, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2849609 · Cited by 167
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Liu, Y., & Wang, Z. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.06.021 · Cited by 168
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Gilbert, A., Illingworth, J., & Bowden, R. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.144 · Cited by 167
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Efficient Mining of Frequent and Distinctive Feature Configurations
Quack, T., Ferrari, V., Leibe, B., & Gool, L. V. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408906 · Cited by 168
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Yang, Y. H., & Newsam, S. D. (2008) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2008.4712139 · Cited by 171
Peeters, C., Leclère, Q., Antoni, J., et al. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.02.031 · Cited by 167
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Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., & Huang, X. (2019) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2907718 · Cited by 167
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Nguyen, M. H., Torresani, L., la Torre, F. D., & Rother, C. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459426 · Cited by 163
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Xu, H., Wang, J., Zhu Li, B., Zeng, G., Li, S., & Yu, N. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126424 · Cited by 164
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Li, H., Dou, X., Tao, C., Wu, Z., Chen, J., Peng, J., Deng, M., & Zhao, L. D. (2020) · Sensors · doi:10.3390/s20061594 · Cited by 278
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Ye, Y., Tang, T., Zhu, B., Yang, C., Li, B., & Hao, S. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3167644 · Cited by 172
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Li, X., & Guskov, I. (2005) · doi:10.5555/1281920.1281955 · Cited by 167
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Yang, L., Hsieh, C.-K., Yang, H., Pollak, J. P., Dell, N. L., Belongie, S., Cole, C. L., & Estrin, D. L. (2017) · ACM Transactions on Information Systems · doi:10.1145/3072614 · Cited by 186
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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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