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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 2,030
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 48,601–48,700 of 56,203
Gom cụm kết quả tìm kiếm video với hướng tiếp cận kết hợp đa đặc trưng
Phúc, N. Q. (2016) · Journal on Information Technologies & Communications · doi:10.32913/mic-ict-research-vn.v2.n36.244
Cross-modal mapping between distributional semantics and the visual world
Lazaridou, A., Bruni, E., & Baroni, M. (2014)
Hu, Y., Zhang, L., Gao, S., et al. (2026) · Measurement · doi:10.1016/j.measurement.2026.121343
Printing Digital Recognition Method Based on the Cascade Classification
Pu, H., Bao, W., & Zhang, X. (2012) · Advanced materials research · doi:10.4028/www.scientific.net/amr.546-547.599
Encoder-shared visual state space network for anterior segment reconstruction
Qian, G., Wang, H., Luo, S., et al. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102631
Dynamic Scenes and Camera Networks
Meingast, M. L., Meingast, M. L., Meingast, M. L., et al. (2013)
Research on Campus Indoor and Outdoor Unmanned Vehicle Navigation Technology
Cheng, X., Li, Z., Zhu, Y., et al. (2022) · International Conference on Advances in Image Processing (ICAIP) · doi:10.1145/3577117.3577128
Motion Equivariance OF Event-based Camera Data with the Temporal Normalization TransformPreprint
Wang, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.12801
Nuisance free recognition of hand postures over a tabletop display
Carreira, J., & Peixoto, P. (2006)
Yingbo, 刘. L., Shuaiqi, 马. M., & Huan, 程. C. (2026) · Laser & Optoelectronics Progress · doi:10.3788/lop260548
Less Biased Noise Scale Estimation for Threshold-Robust RANSAC
Edstedt, J. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00264 · Cited by 5
“The feature matchers SIFT [17] SuperGlue [26], LightGlue [16], ASpan-Former [4], DKM [8], and RoMa [9] are represented.”
A Survey on Software Applications used for Object Recognition by the Visually Impaired
Shagufta, S., Bagwan, R., & Joshi, D. (2014)
Feature track summary visualization for sequential multi-view reconstruction
Recker, S., Hess-Flores, M., & Joy, K. I. (2013) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2013.6749337
3D Face recognition Robust to Expression Occlusion and Poses
Deshmukh, R. R. (2014)
-, S. G., & -, K. P. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i06.33855
Detection of Aircrafts on a Collision Course using Spatio-Temporal HOG
Rozantsev, A., Christoudias, C. M., Lepetit, V., & Fua, P. (2013)
Object Retrieving from Image Database
Bao, N., & Shirai, Y. (2011) · IEICE Technical Report; IEICE Tech. Rep.
Xác định món ăn đặc sản Việt Nam với sự kết hợp của mạng học sâu và bản thể học
Mã, T. T., Châu, N. K., Thạch, M. H., et al. (2023) · Can Tho University Journal of Science · doi:10.22144/ctujos.2023.193
Broken Detection of the Traffic Sign by using the Location Histogram Matching
Liu, Y., Lee, S., Kwon, S.-G., et al. (2012) · Journal of Korea Multimedia Society · doi:10.9717/kmms.2012.15.3.312
ISAR Cross-Range Scaling Method Using Feature Extraction and Matching
Ryu, B., Lee, K.-M., Lee, I.-H., et al. (2022) · The Journal of Korean Institute of Electromagnetic Engineering and Science · doi:10.5515/kjkiees.2022.33.2.114
Zhang, H., Xu, Y., Hong, Z., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3639687
Istighfarin, N. F., Choi, B., & Jo, H. (2025) · International Journal of Control Automation and Systems · doi:10.1007/s12555-025-0527-2
“Based on visual inspection and prior work [15], we initially hypothesized that prompts such as building, window, and door would correspond to edge-rich, semantically meaningful regions and therefore yield higher alignment.”
An X-ray Image Panorama System Using Robust Feature Matching and Per ception-Based Image Enhancement
Wang, W., & Gwun, O. (2012) · Journal of Korea Multimedia Society · doi:10.9717/kmms.2012.15.5.569
An Efficient And Robust Framework For Collaborative Monocular Visual Slam
Das, D., Maity, S., & Dhara, B. C. (2021) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss52988.2021.9663736
Augmenting Mobile Robot Geometric Map with Photometric Information
Skrzypczyński, P. (2009) · Advances in intelligent and soft computing · doi:10.1007/978-3-540-93905-4_1
EasyFlow: increasing the convergence basin of variational image matching with a feature‐based cost
Braux-Zin, J., Dupont, R., Bartoli, A., et al. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0090
An infrared image detection algorithm for power equipment based on search optimization for YOLOv5
Wang, X. (2025) · Journal of Measurements in Engineering · doi:10.21595/jme.2024.24408
Learning Hierarchical Features Using Sparse Self-organizing Map Coding for Image Classification
Aly, S. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-13461-1_31
Co-localization in Noisy Images through Minimizing a Ratio Function
Wang, C., Xu, J. J., Zhang, Y., et al. (2015) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv.2015.53
An Evaluation of Camera Calibration Methods Using Digital Low Cost Cameras
Zollner, H., & Kampel, M. (2010)
Shi, Y., & Shen, D. G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-79982-5_45
Topic Uncovering and Image Annotation via Scalable Probit Normal Correlated Topic Models
Yu, X. (2015) · RIT Scholar Works (Rochester Institute of Technology)
2.5D local feature matching system
Strachan, E., & Siebert, J. P. (2011) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)
3D Modeling and Simulation using Image Stitching
N.Braganza, S., R.Langer, S., Gaikwad, S. R., et al. (2015) · International Journal of Computer Applications · doi:10.5120/20246-2608
Face identification and clusteringPreprint
Dhingra, A. (2017) · arXiv (Cornell University) · doi:10.7282/t3r21487
A comparative study of feature detection and description methods for a RGB-D SLAM system
Sarabia, J. A. G., Gonzalez-Fraga, J. A., & Pellegrin, L. (2021) · doi:10.1117/12.2594630
Robust Keypoint Detection and Matching on Fisheye Images by Self-Supervised Learning
Tian, W., Cai, P., Wen, Y., et al. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4024774
Zhang, H., & Zhou, Q. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6459-7_20
Zuverlässiges Structure-From-Motion für Bildpaare
Cheremukhin, S. (2012) · Informatik-Spektrum · doi:10.1007/s00287-012-0637-0
Kumar, K., Nandan, D., & Mishra, R. K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-0751-9_39
Video Near-duplicate Detection
Satoh, S. (2008) · Encyclopedia of Multimedia · doi:10.1007/978-0-387-78414-4_73
LiDAR Enhanced Structure-from-MotionPreprint
Zhen, W., Hu, Y., Yu, H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03369
Local Phase Features in Chromatic Domain for Human Detection
Ragb, H. K., & Asari, V. K. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-8356-1.ch033
Vivid tennis player rendering system using broadcasting game videos
Chen, C., Lai, J.-H., & Chien, S. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5582612
Key-Points Based Enhanced CMFD System Using DBSCAN Clustering Algorithm
Soni, B., & Das, P. K. (2022) · Studies in computational intelligence · doi:10.1007/978-981-16-9041-9_6
A Filtration Strategy Based on FD-CRF for Image Matching
Huang, S., & Wang, W. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.566
Video Deraining via Temporal Discrepancy Learning
Fan, Y., Ma, L., & Liu, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18916-6_1
Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification
Bi, Y., Zhang, M., & Xue, B. (2021) · doi:10.26686/wgtn.13884998.v1
Super-resolution based on high-frequency feature guidance for small-scale pedestrian detection
Shao, Q., Cu, Y., Guo, B., et al. (2025) · doi:10.1117/12.3061640
Image Mosaic Research and Realization Based on LoFTR AlgorithmPreprint
Tian, A., Wang, K., Zhang, L., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-1107577/v1
Song, X., Wang, F., Li, R., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4146146
Finger Vein Verification System based on Three Methodologies of Feature Extraction
Alasadi, A. H. H., & Zainab, N. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017915144
Gao, Z., Jiang, J., Li, C., et al. (2025) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2025.3635112
Image modeling system development for robust descriptor of environmental change
Choi, G. M., Junz, H. I., Kim, R. K., et al. (2013) · doi:10.1109/fcv.2013.6485461
Xiao, Z., Sun, T., Zhang, J., et al. (2023) · Journal of Image and Graphics · doi:10.11834/jig.220774
XJTU at TRECVID2008 High-Level Feature Extraction
Wang, Z., Liu, G., Qian, X., et al. (2008)
Sélection de singularités locales par stimulation de carte GHSOM
Lefebvre, G., García, C., Salotti, J., et al. (2007) · HAL (Le Centre pour la Communication Scientifique Directe)
Maier, A., Christlein, V., Breininger, K., Würfl, T., Unberath, M., & Rieß, C. (2026) · doi:10.1515/9783111386690-021
Stereo Analysis of Archaelogical Scenes Using Monogenic Signal Representation
Alifragis, M., & Tzafestas, C. S. (2010) · Communications in computer and information science · doi:10.1007/978-3-642-11840-1_10
Learning Visual Concepts from Social Images
Li, Z. (2011)
Composed complex-cue histograms: An investigation of the information content in receptive field base
Linde, O., & Lindeberg, T. (2013) · Journal of Mathematical Imaging and Vision
Multi-Scale Residual Image Stitching Network Combining Homography and Thin-Plate Spline Motion
Wang, C., Xing, Y., & Lin, Y.-C. (2024) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace61206.2024.10548389
“The methods involved in the experiment include SIFT [8] + RANSAC [9], APAP [10], ELA [11], LPC [12] in traditional methods, and UDIS [2] and UDIS++ [4] based on deep learning.”
MethodEnhancing Prostate Cancer Prediction: A Fusion of HAAR, LBP, and SIFT Features with CNN
Harshanya, M., Priyanka, S. A., Shanmugasundari, S., & Balasaravanan, P. (2023) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai59313.2023.10452441
Application research on 3D reconstruction based on Kinect
Xiao, Z., Jiang, J., Jiawei, H., et al. (2015) · Advances in computer science research · doi:10.2991/isci-15.2015.122
Developing a macro-micro teleoperation system with snake robots for minimally invasive surgeries
Razjigaev, A. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.235042
A proposal of prior probability-oriented clustering in feature encoding strategies
Shinomiya, Y., & Hoshino, Y. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210146
An Image Location System Based on SIFT Algorithm to Alternative to GPS
Wang, T.-M., & Lin, C. (2023) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie51358.2023.10227940
Recherche d'images en mobilité : le système IOTA-EyeSnap.
Mulhem, P., Chevallet, J., & Cubaud, N. (2013) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.24348/coria.2013.coria2013_68
A robust method for the recognition of palmprints
Nibouche, O., Wang, H., Varadarajan, S., et al. (2017) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078487
Scale Space Based Grammar for Hand Detection
Prokaj, J., & Lobo, N. D. V. (2006) · Lecture notes in computer science · doi:10.1007/11821045_2
Efficient Local Image Descriptors
Lukianitsa, A. A. (2017) · Computational Mathematics and Modeling · doi:10.1007/s10598-017-9360-7
Max-margin analysis based patch sampling for discovery of mid-level parts
Yang, L., & Xie, X. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351194
Imaging Cellular Architecture in Three Dimensions Through Electron Microscopy
Caffrey, B. J., & Subramaniam, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-821618-7.00113-9
Multimodal Remote Sensing Image Registration via Cross-Modal Structural Information
Deng, Z. G., Jiang, Z., Guo, P., et al. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11243685
Détection et description de points clés par apprentissage
Loiseau--Witon, N. (2023) · theses.fr (ABES) · doi:10.70675/16d2e4a4zd375z4ab8za649zc1ccae437732
Products recognition on shop-racks from local scale-invariant features
Zawistowski, J., Kurzejamski, G., Garbat, P., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2225610
MAC-I2P: I2P Registration with Modality Approximation and Cone–Block–Point Matching
Sun, Y., Zhang, L., & Zhao, S. (2025) · Applied Sciences · doi:10.3390/app152011212
Hussain, W., Naveed, M., Khan, A., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3668564/v1
Using Motion and Internal Supervision in Object RecognitionPreprint
Harari, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05455
Leveraging Dual Deep Learning and Grade-Shift Algorithm for Intelligent Coin Grading
Getahun, M. N., Hammoud, M., Passerone, R., Sergiyenko, O., & Somov, A. (2026) · Journal on Computing and Cultural Heritage · doi:10.1145/3822566
“The former study used Scale Invariant Feature Transform (SIFT) [29] and the later used SIFT and Rotation and Flipping Invariant Regional Binary Patterns (RFR) [22] features to determine distinction between coin grades.”
Fast thresholding of high dimensional Euclidean distances using binary squaring
Röwekamp, J. H. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900111
Development of a Free-Viewpoint Bladder Observation System for Cystoscopy
Kaneko, S., Nosato, H., & Sudo, K. (2025) · doi:10.1109/embc58623.2025.11254227
“It utilizes scale-invariant feature transform (SIFT) [6] for feature extraction and automatically estimates camera parameters and reconstructs a 3D point cloud from multiple images.”
Deep Features for training Support Vector MachinePreprint
Nanni, L., Ghidoni, S., & Brahnam, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.03488
Image Matching-based Mobile Application Providing Indoor Place Information
Ko, S. H., Yang, M. H., Oh, S. H., et al. (2016) · Journal of Korea Multimedia Society · doi:10.9717/kmms.2016.19.8.1424
Discriminable Points That Stick Out of Their Environment
Klein, D. A., & Cremers, A. B. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40602-7_41
Zhao, Z., Wang, F., You, H., Li, P., & Tingtao, Z. (2024) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc61627.2024.10837034
“The paper selects six feature algorithms, including the scale-invariant feature transform (SIFT) [7], the speeded up robust feature (SURF) [8], KAZE [9], accelerated-KAZE (AKAZE) [10], the oriented FAST and rotated BRIEF (ORB) [11], and the fast retina keypoint (FREAK) [12], as well as the (SMSER)…”
Highly Accurate Image Matching Using Inlier Extraction of Images via Upper Confidence Bound Policy
Shimada, M., & Kamata, H. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-1531-5_7
Kramm, S., Miché, P., & Bensrhair, A. (2008) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2008.4732707
EIVF: Efficient IVFPQ Search for On-Device ARM Processors
Bahn, D. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463912 · Cited by 1
Efficient color feature extraction and matching for motion estimation and mapping
Hwang, H., & Kweon, I.-S. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4651244
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 ›
Are you an author, or do you see a problem with this page? Request a review
