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 3,764
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 2,375
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 2,046
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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Image registration based on Genetic Algorithm and weighted feature correspondences
Yuan, Z., Ahrary, A., Yan, P., et al. (2009) · IEEE International Symposium on Consumer Electronics (ISCE) · doi:10.1109/isce.2009.5157002 · Cited by 3
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Djerida, A., Zhao, Z., & Zhao, J. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103258 · Cited by 3
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Umer, S., Ghorai, M., & Mohanta, P. P. (2017) · International Conference on Advances in Pattern Recognition · doi:10.1109/icapr.2017.8592958 · Cited by 3
Seafloor visual saliency evaluation for navigation with BoW and DBSCAN
Geng, Y., Wang, Z., Shi, C., et al. (2016) · OCEANS 2016 - Shanghai · doi:10.1109/oceansap.2016.7485697 · Cited by 3
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Ma, X., Yang, Y., Yi, Y., et al. (2021) · Optik · doi:10.1016/j.ijleo.2021.167912 · Cited by 3
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A fast multi-model approach for object duplicate extraction
Piccinini, P., Prati, A., & Cucchiara, R. (2009) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2009.5403114 · Cited by 3
Bouguila, N., & Mashrgy, M. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-642-55032-4_29 · Cited by 3
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Zeng, J., Zhan, L., Fu, X., et al. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2014.0372 · Cited by 3
Cramer-Rao bound on projective image registration from feature points
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Langer, M., & Kuhnert, K. ‐. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761165 · Cited by 3
Zhang, X., Hirai, S., & Zhang, P. (2010) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2010.5723368 · Cited by 3
Dynamic convolution for image matching
Soloviev, I., Kovalchuk, A. V., & Klinshov, V. V. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01373-2 · Cited by 4
“The detection of such points, can rely, for example, on convolution of the original image with a certain kernel, such as the difference of Gaussian functions [14].”
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Ochoa, D., Criollo, R., Liao, W., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2262827 · Cited by 3
Object instance recognition using best increasing subsequence
Harjono, K. D., & Kusuma, G. P. (2016) · doi:10.1109/kicss.2016.7951432 · Cited by 3
A Multi-stage 6D Object Pose Estimation Method of Texture-less Objects Based on Sparse Line Features
Yang, X., Li, K., Fan, X., et al. (2022) · doi:10.1109/ieem55944.2022.9989794 · Cited by 3
Precise Positioning for Autonomous Driving in both Indoor and Outdoor Environments
Henkel, P., Parimi, S., Fischer, J., et al. (2024) · Proceedings of the Institute of Navigation ... International Technical Meeting/Proceedings of the ... International Technical Meeting of The Institute of Navigation · doi:10.33012/2024.19534 · Cited by 3
“To filter good matches the common Lowe’s ratio test is applied according to Lowe (2004), enforcing a significant feature distance between the best and second best matches.”
MethodJian-guo, L., Liu, Q., Qiubing, L., et al. (2017) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2017.8009718 · Cited by 3
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Yadav, G. K., & Sethi, A. (2014) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/2683483.2683524 · Cited by 3
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Zhen, X., & Shao, L. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.769 · Cited by 3
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DelMarco, S. P., Tom, V. T., Webb, H. F., et al. (2019) · doi:10.1117/12.2518185 · Cited by 3
Models from image triplets using epipolar gradient features
Vincent, É., & Laganière, R. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2006.12.021 · Cited by 3
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Rituerto, A., Murillo, A. C., & Guerrero, J. J. (2013) · doi:10.1145/2526667.2526670 · Cited by 3
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Rupnik, E., & Deseilligny, M. P. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2020-71-2020 · Cited by 3
Analysis of Similarity Measurements in CBIR Using Clustered Tamura Features for Biomedical Images
Brancati, N., & Camastra, F. (2016) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-39345-2_1 · Cited by 3
A Short Overview of Feature Extractors for Knuckle Biometrics
Choraś, M. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-00969-8_51 · Cited by 3
Li, J., Zhou, R., Ruan, Z., et al. (2025) · IET Image Processing · doi:10.1049/ipr2.13317 · Cited by 3
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Wang, W., Zhang, F., & Geng, L. (2019) · International Conference on Intelligent Medicine and Image Processing (IMIP) · doi:10.1145/3332340.3332351 · Cited by 3
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Guruprasad, P., & Guruprasad (2015) · International Conference on Applied and Theoretical Computing and Communication Technology · doi:10.1109/icatcct.2015.7456904 · Cited by 3
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Yang, K., Yin, D., Zhang, J., et al. (2019) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai48974.2019.9010209 · Cited by 3
Yan, S., Zhang, M., Peng, Y., Liu, Y., & Tan, H. (2022) · Remote Sensing · doi:10.3390/rs14246301 · Cited by 3
Chiang, C.-C., Yang, Y.-Y., Wei-lin, L., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3327421 · Cited by 3
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Choudhary, M., Tiwari, V., Jain, S., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15473-z · Cited by 3
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Lin, Y.-S., Chang, S.-M., Tsai, J. C., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-17829-0_28 · Cited by 3
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Chen, J., Zhang, B., Qi, Y., et al. (2010) · doi:10.1109/icmult.2010.5630988 · Cited by 3
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Feng, B., Ren, K., Tao, Q., et al. (2020) · doi:10.1117/12.2575394 · Cited by 3
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Yang, C., Cheung, G., & Takamura, S. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451711 · Cited by 3
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Serra, J., Lalanne, R., Dufour, J.-E., et al. (2024) · Strain · doi:10.1111/str.12479 · Cited by 3
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Kootstra, G., Ypma, J., & de Boer, B. (2007) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2007.4522200 · Cited by 3
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“Classic manual feature extraction methods such as Local Binary Patterns (LBP) [5], Scale-Invariant Feature Transform (SIFT) [6], and Histogram of Oriented Gradients (HOG) [7] aim to capture key information features of images, thereby achieving efficient image classification.”
MethodRetinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging
Wang, J., Li, H., Hu, D., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-77786-8_12 · Cited by 3
Scene Classification by Feature Co-occurrence Matrix
Lang, H., Xi, Y., Hu, J., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16628-5_36 · Cited by 3
Nawaf, M. M., & Trémeau, A. (2013) · IET Computer Vision · doi:10.1049/iet-cvi.2012.0270 · Cited by 3
Mosaic aided navigation: Tools, methods and results
Indelman, V., Gurfil, P., Rivlin, E., & Rotstein, H. P. (2010) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans.2010.5507282 · Cited by 3
Modelling Software Architecture for Visual Simultaneous Localization and Mapping
Mishra, B., Griffin, R. J., & Sevil, H. E. (2021) · Automation · doi:10.3390/automation2020003 · Cited by 3
Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera
Sumikura, S., Sakurada, K., Kawaguchi, N., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_18 · Cited by 3
A Space Variant Gradient Based Corner Detector for Sparse Omnidirectional Images
Kerr, D., Coleman, S., & Scotney, B. W. (2010) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-010-0211-9 · Cited by 3
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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