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.
Cited by
Sorted by citations
Items 32,101–32,200 of 56,201
Chen, D.-Y., & Chiu, Y.-M. (2013) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2013.04.005 · Cited by 4
Chang, H.-K., Chen, W., Jhang, J.-S., & Liou, J.-C. (2023) · Sensors · doi:10.3390/s23239337 · Cited by 4
Improved Image Analysis Methodology for Detecting Changes in Evidence Positioning at Crime Scenes
Petty, M., Teng, S. W., & Murshed, M. (2019) · Digital Image Computing: Techniques and Applications · doi:10.1109/dicta47822.2019.8945934 · Cited by 4
Tracking Adaptation to Improve SuperPoint for 3D Reconstruction in Endoscopy
Barbed, O. L., Montiel, J. M. M., Fua, P., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_56 · Cited by 4
Réalité augmentée en extérieur. Etat de l'art
Zendjebil, I., Abasa, F., Didier, J.-Y., et al. (2009) · Techniques et sciences informatiques · doi:10.3166/tsi.28.857-890 · Cited by 4
A Gaussian filter-based window for efficient SAR image matching
Paul, S., Pavitra, Y. J., Rehana, S., et al. (2023) · Journal of Spatial Science · doi:10.1080/14498596.2023.2274998 · Cited by 4
3D Keypoints Detection from a 3D Point Cloud for Real-Time Camera Tracking
Lin, B., Tamaki, T., Slomp, M., et al. (2012) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.133.84 · Cited by 4
Generalized stereo for hybrid omnidirectional and perspective imaging
Lin, H., & Wang, M.-L. (2009) · doi:10.1109/iccvw.2009.5457555 · Cited by 4
Content-Based Spam Filtering on Video Sharing Social NetworksPreprint
da Luz, A., Junior, E. A. D. V., & Araújo, A. D. A. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1104.5284 · Cited by 4
Wang, B., Li, Y., Lu, Q., et al. (2012) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-25792-6_55 · Cited by 4
Simultaneous registration of gnomonic projections and central perspectives
Barazzetti, L., Previtali, M., & Scaioni, M. (2014) · The Photogrammetric Record · doi:10.1111/phor.12071 · Cited by 4
Suárez, M. P., Brea, V. M., Cabello, D., et al. (2013) · European Conference on Circuit Theory and Design · doi:10.1109/ecctd.2013.6662293 · Cited by 4
Extending 6D Object Pose Estimators for Stereo Vision
Pöllabauer, T., Emrich, J., Knauthe, V., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-8705-0_8 · Cited by 4
Comparison of 3D local and global descriptors for similarity retrieval of range data
Bayramoğlu, N., & Alatan, A. A. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.08.105 · Cited by 4
Multicore Computing for SIFT Algorithm in MATLAB® Parallel Environment
Cao, H., & Chen, J. (2012) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2012.152 · Cited by 4
Feature Based Image Registration using Heuristic Nearest Neighbour Search
Vijay, S. T., & Pournami, P. N. (2018) · International Computer Science and Engineering Conference · doi:10.1109/icsec.2018.8712669 · Cited by 4
PixelStitch: Structure-Preserving Pixel-Wise Bidirectional Warps for Unsupervised Image Stitching
Jin, H., Nie, L., Lin, C., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02611 · Cited by 4
“Besides, there is an increasingly complicated trend in hand-craft features required in traditional methods, from SIFT [26] keypoints [2] to line segments [21], large-scale edges [6], depth maps [23], semantic planes [17], and even SAM [3].”
Research on single-character image classification of Tibetan ancient books based on deep learning
Zhao, Z. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9824018 · Cited by 5
Design and control of UAV systems : a tri-rotor UAV case study
Mohamed, M. K. (2012) · [Thesis]. Manchester, UK: The University of Manchester; 2012. · Cited by 4
Geometric-algebra adaptive filters.
Lopes, W. B. (2016) · doi:10.11606/t.3.2016.tde-22092016-143525 · Cited by 4
A Fuzzy Control System for Capturing Target of Intelligence Spraying
Zhang, F., & Cang-xiu, C. (2006) · doi:10.1109/icat.2006.10 · Cited by 4
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Macêdo, S., Melo, G., & Kelner, J. (2015) · doi:10.1109/svr.2015.8 · Cited by 4
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Karpur, A., Perrotta, G., Martin-Brualla, R., et al. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00079 · Cited by 4
Advanced feature based digital video stabilization
Spampinato, G., Bruna, A. R., Guarneri, I., et al. (2016) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin.2016.7684716 · Cited by 4
Evaluation of salient point methods
Wu, S., & Lew, M. S. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502179 · Cited by 4
Torres, J. P., Codorniú, R. T., Baracaldo, R. L., Sariol, H. C., Peacok, T. M., Yperman, J., Adriaensens, P., Carleer, R., & Sauvanell, Á. B. (2020) · SN Applied Sciences · doi:10.1007/s42452-020-03835-3 · Cited by 4
Sindhu, C. P., & Valsan, V. (2017) · International Conference on Signal Processing, Communication and Networking (ICSCN) · doi:10.1109/icscn.2017.8085711 · Cited by 4
Banerjee, S., Dhar, M., & Sen, S. (2018) · doi:10.1109/edct.2018.8405057 · Cited by 4
Which Is Which? Evaluation of Local Descriptors for Image Matching in Real-World Scenarios
Bellavia, F., & Colombo, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29888-3_24 · Cited by 4
Patel, M. K., Bull, G., Egan, L. M., Swain, N., Rolland, V., Stiller, W. N., & Conaty, W. C. (2026) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2025.104379 · Cited by 4
Hügl, S., Eckardt, F., Lexow, G., Majdani, O., Lenarz, T., & Rau, T. (2017) · Comput. methods Biomech. Biomed. Eng. Imaging Vis. · doi:10.1080/21681163.2015.1137080 · Cited by 10
Shangri–La: A medical case–based retrieval tool
de Herrera, A. G. S., Schaer, R., & Müller, H. (2017) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23858 · Cited by 5
Pertile, M., Magnabosco, M., & Debei, S. (2010) · Journal of Physics Conference Series · doi:10.1088/1742-6596/238/1/012031 · Cited by 4
Evaluating the trackability of natural feature-point sets
Gruber, L., Zollmann, S., Wägner, D., et al. (2009) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2009.5336469 · Cited by 4
Hu, Q., & Ai, M. (2011) · IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) · doi:10.1109/icsdm.2011.5969072 · Cited by 4
Cross Fusion of Point Cloud and Learned Image for Loop Closure Detection
Yue, H., Cao, D., Liu, Z., Wang, T., & Chen, W. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3362681 · Cited by 4
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MethodMulti-view Intact Discriminant Space Learning for Image Classification
Dong, X., Wu, F., Jing, X., & Wu, S. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9951-0 · Cited by 4
Robust and accurate fundamental matrix estimation with propagated matches
Wan, Y., Miao, Z., & Tang, Z. (2010) · Optical Engineering · doi:10.1117/1.3490415 · Cited by 4
Real-Time Image Registration via Optical Flow Calculation
Popovic, V., Seyid, K., Çoğal, Ö., et al. (2017) · Springer eBooks · doi:10.1007/978-3-319-59057-8_9 · Cited by 4
FPGA based hardware accelerator for KAZE feature extraction algorithm
Kalms, L., Elhossini, A., & Juurlink, B. (2016) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2016.7929553 · Cited by 4
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Dehghani, F., Karimian, A., Sirous, M., et al. (2021) · Journal of Medical Signals & Sensors · doi:10.4103/jmss.jmss_9_20 · Cited by 4
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Jahanshahi, M. R., & Masri, S. F. (2011) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/41182(416)46 · Cited by 4
Nguyen, D. T., Jacquemoud, S., Lucas, A., et al. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114691 · Cited by 4
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Nguyen, N. V., Boucher, A., Ogier, J.-M., et al. (2009) · International Conference on Image and Graphics · doi:10.1109/icig.2009.68 · Cited by 4
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Khusheef, A. S., Kothapalli, G., & Tolouei‐Rad, M. (2014) · Australasian Journal of Paramedicine · Cited by 4
Responsive alignment for X-ray tomography beamlines
Vasconcelos, G. J. Q., Miqueles, E. X., & Costa, G. S. R. (2018) · Journal of Synchrotron Radiation · doi:10.1107/s1600577518012201 · Cited by 4
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Pal, D., Alladi, A., Pothireddy, Y., et al. (2020) · doi:10.1109/icdse50459.2020.9310145 · Cited by 5
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Liu, S., Chen, J., Sun, M., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2961184 · Cited by 4
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Huang, Q., Shang, G., Zhang, Y., et al. (2023) · doi:10.1109/iccad57390.2023.10323734 · Cited by 4
Rizk, M., Al-Deen, T. N., Diab, H. I., et al. (2018) · International Conference on Computer and Applications (ICCA) · doi:10.1109/comapp.2018.8460374 · Cited by 4
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Azevedo, C. L., Cardoso, J. L., & Ben-Akiva, M. E. (2014) · Transportation Research Board 93rd Annual MeetingTransportation Research Board · Cited by 4
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Li, J., Deng, B., Zhang, M., et al. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103890 · Cited by 4
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Huang, G. B., & Miller, E. G. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543185 · Cited by 4
Craciunescu, T., Murari, A., Lerche, E., et al. (2020) · Entropy · doi:10.3390/e22070775 · Cited by 4
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Qiao, W., Chen, B., Zhao, J., et al. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1802/3/032077 · Cited by 4
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Sha, M., & Boukerche, A. (2020) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc50000.2020.9219723 · Cited by 4
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“As one can observe, solutions BRIEF [26], SIFT [20], [27], and BRISK [28] were selected since they have the highest rate of inliers.”
MethodWang, Y., Li, P., Wang, D., et al. (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-b7-573-2016 · Cited by 4
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Mezzoudj, S. (2021) · Multimedia Information Retrieval · doi:10.5772/intechopen.94910 · Cited by 4
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Wan, W., Feng, L., Wu, Z., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.06473 · Cited by 4
Gu, I. Y., & Zulfiqar, H. (2011) · InTech eBooks · doi:10.5772/14839 · Cited by 4
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Optical character recognition based on local invariant features
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Learning Vocabulary-Based Hashing with AdaBoost
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Moving Object Detection from Mobile Platforms Using Stereo Data Registration
Sappa, A. D., Gerónimo, D., Dornaika, F., et al. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-24049-2_3 · Cited by 4
Zhang, Y., Zhu, Y. M., Liu, L., et al. (2024) · Remote Sensing · doi:10.3390/rs16132491 · Cited by 4
“Feature-based matching techniques are also extensively employed in TPs matching since they do not require a priori coarse offset and are computationally efficient [24].”
Remote Sensing Image Registration Using Equivariance Features
Lee, W., & Oh, S. (2021) · International Conference on Information Networking · doi:10.1109/icoin50884.2021.9333861 · Cited by 5
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Robust Vision-based Multiple Moving Object Detection and Tracking from Video Sequences
Khalifa, O. O., Malek, N. F. A., & Ahmed, K. I. (2018) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v10.i2.pp817-826 · Cited by 4
A Shape Derivative Based Approach for Crowd Flow Segmentation
Wu, S., Yu, Z., & Wong, H. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12307-8_9 · Cited by 4
Kumar, S., & Soundrapandiyan, R. (2020) · Journal of Electronic Imaging · doi:10.1117/1.jei.29.4.043007 · Cited by 4
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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