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 50,601–50,700 of 56,203
Computer vision component to environment scanning
Soares, P. E. P. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Robust Feature Detection Using 2D Wavelet Transform under Low Light Environment
Kim, Y.-O., Lee, J. H., Cho, W., et al. (2007) · doi:10.5772/4970
Privacy Preserving Image Matching Scheme with Aggregated Local Descriptors
Honi, D. G., Abdulmalik, H. A., Abdulsada, A. I., et al. (2021) · doi:10.1109/csitss54238.2021.9683330
A Reinforcement Learning Approach for Sequential Spatial Transformer NetworksPreprint
Azimi, F., Raue, F., Hees, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.14295
Li, S., & Shan, J. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.06.027
Detection and Application of Mobile Target Image Based on Artificial Intelligence
Hao, S., Wang, C., & Cao, W. (2022) · Mobile Information Systems · doi:10.1155/2022/2359331
Wang, Z., Zhong, C., Shang, Z., et al. (2026) · Springer proceedings in physics · doi:10.1007/978-981-92-4349-5_31
Symbol Spotting for Document Categorization
Rusiñol, M., & Lladós, J. (2010) · Springer eBooks · doi:10.1007/978-1-84996-208-7_3
Overview of Automated Liquor Picking Technology
Xiao, Y., Liu, Z., Zhang, C., et al. (2025) · doi:10.1145/3768184.3768190
“…for two classification methods, one is based on the classification of image features, the method is mainly based on the differences between different categories of images, the use of the corresponding algo-rithm to extract the relevant features, such as SIFT [ 3 ] , HOG [ 4 ] , LBP [ 5 ] and so on.”
Sentence Embedding Generation Method for Differential Privacy Protection
Liu, Y., Wang, W., & Hua, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9101-2_5
A Far Sign Recognition by Applying Super-Resolution to Extracted Regions from Successive Frames
Yamauchi, H., Kojima, A., & Miyamoto, T. (2010) · InTech eBooks · doi:10.5772/8932
Studying the Effect of Digital Stain Separation of Histopathology Images on Image Search Performance
Cheeseman, A. K., Tizhoosh, H. R., & Vrscay, E. R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50516-5_23
Object Spotting in Historical Documents
Das, S., & Mandal, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-57907-4_5
Deep Learning Based Multimodal Retrieval
Chen, J. (2023) · theses.fr (ABES) · doi:10.70675/40665356z7b38z4e8fz8dceze1c899d3ae06
Johnson, M. K. (2012) · Springer eBooks · doi:10.1007/978-1-4614-0757-7_9
Optical Geolocation for small Unmanned Aerial System
Dolph, C. V., McSwain, R. G., Humphreys, W. M., et al. (2019) · AIAA SciTech Forum · doi:10.2514/6.2019-1055
Object Recognition with Näive Bayes-NN via Prototype Generation
Escalante, H. J., Sotomayor, M., Montes, M., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07491-7_17
Multimodal Document Understanding with Unified Vision and Language Cross-Modal Learning
Bakkali, S. (2022) · theses.fr (ABES) · doi:10.70675/aeb1fb70za11fz410fzaf33z6a28e0f0f154
An efficient interest point detection technique for real-time applications
Pooja, H. R., & Shahid, M. (2013) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip.2013.6707629
Visual servo control for a human-following robot
Burke, M. (2011) · SUNScholar (Stellenbosch University)
Image Registration Using Structural Corners
Zhao, S. (2007) · doi:10.1109/sitis.2007.79
Image Hashing Through Spatio-triad Relationship
Sowmya, K. N., & Chennamma, H. R. (2022) · International Journal of Computer Network and Information Security · doi:10.5815/ijcnis.2022.05.05
From Biomedical Image Analysis to Biomedical Image Understanding Using Machine Learning
Romero, E., & Gonzalez, F. A. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch809
Learning and Recognition of 3D Visual Objects in Real-Time
Hamid, S., & Hengst, B. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10439-8_16
Geração de imagens artificiais e quantização aplicadas a problemas de classificação
Thumé, G. S. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2016.tde-16122016-150334
Signboard extraction from stereo image with fuzzy c-varieties
Moriyama, Y., Orii, H., Kawano, H., et al. (2010) · International Symposium on Communications and Information Technologies · doi:10.1109/iscit.2010.5664853
Huda, M. A., Jauhari, K., Yuliansyah, E., et al. (2020) · WSEAS TRANSACTIONS ON APPLIED AND THEORETICAL MECHANICS · doi:10.37394/232011.2020.15.1
Deep learning architecture advancements for accurate and robust image registration
Walvoord, D. J., & Couwenhoven, D. W. (2019) · Signal processing, sensor fusion, and target recognition. Conference · doi:10.1117/12.2522044
Taking the Most of Existing Image Descriptors: A Hybrid Approach
Martins, V. L., Ribeiro, T. P., Martinez, A., & Abdala, D. D. (2017) · Workshop of Computer Vision (WVC) · doi:10.1109/wvc.2017.00024
Combining Disparate Information for Machine Learning.
Hsiao, K.-J. (2014) · Deep Blue (University of Michigan)
Mediacampaign — A multimodal semantic analysis system for advertisement campaign detection
Rehatschek, H. E., Sorschag, R., Rettenbacher, B., et al. (2008) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2008.4564932
Kuznetsov, N., ALEKSEENKO, A., Berkut, I., et al. (2026) · IEEE International Conference of Young Professionals in Electron Devices and Materials (EDM) · doi:10.1109/edm69524.2026.11632032
“Later, more efficient feature detectors were proposed, such as SURF [2] and ORB [3].”
Using Recognition to Guide a Robot's Attention
Thomas, A., Ferrari, V., Leibe, B., et al. (2009) · The MIT Press eBooks · doi:10.7551/mitpress/8344.003.0036
A Review of Computer Vision Methods in Network SecurityPreprint
Zhao, J., Masood, R., & Seneviratne, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03318
Hierarchical feed-forward models for robust object recognition
Bax, I. (2007) · PUB – Publications at Bielefeld University (Bielefeld University)
Functional model for implementing differentiated learning in online education
Muminov, B. B., & Ruziyeva, G. (2025) · AIP conference proceedings · doi:10.1063/5.0299791
Koh, J.-W., & Sung, H. (2026) · Remote Sensing · doi:10.3390/rs18121889
A Log-Polar Feature Guided Iterative Closest Point Method for Image Registration
Zhou, S., & Zhang, Y. (2017) · International Conference on Multimedia and Image Processing (ICMIP) · doi:10.1109/icmip.2017.64
Object localization in images based on clustering of attention zones
Kosarevych, R., Lutsyk, O., Rusyn, B., et al. (2025) · Vìdbìr ì obrobka ìnformacìï. · doi:10.15407/vidbir2025.53.071
Indoor Scene Understanding using Non-Conventional Cameras
Labrador, C. F. (2020) · theses.fr (ABES) · doi:10.70675/7af47b94z536cz49cdza539zc99752cca57b · Cited by 2
Object Classification Based on Fractional HoG Features
Liu, Y., & Shen, X. (2011) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.65.491
Power, R., Zaffar, M., Ferrarini, B., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2021) · Open Access at Essex (University of Essex) · doi:10.48550/arxiv.2109.11002 · Cited by 1
Lalys, F., & Jannin, P. (2012) · OpenGrey (Institut de l'Information Scientifique et Technique)
A Hybrid Tracking Approach for Autonomous Docking in Self-Reconfigurable Robotic Modules
Sohal, S. S. (2019) · VTechWorks (Virginia Tech)
Research on Multi-label Clothing Image Classification Based on Convolutional Neural Network
Hong, Y. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-63784-2_105
A clinical C-arm base-tracking system using computer vision for intraoperative guidance
Haliburton, L. (2017) · cIRcle (University of British Columbia) · doi:10.14288/1.0355252
Vidula, T. V., & Nair, V. V. (2014) · doi:10.1109/compsc.2014.7032640
Keypoint Recognition for 3D Head Model Using Geometry Image
Lu, F., Fu, Q., Long, X., et al. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.654.287
Virtual Jizai-Ryu: Hi-Fidelity Interactive Virtual Exhibit with Digital Display Case
Ban, Y., Kajinami, T., Narumi, T., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20618-9_40
Polarimetric Multi-View Inverse RenderingPreprint
Zhao, J., Monno, Y., & Okutomi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08830
ROS2D: Image feature detector using rank order statistics
Yousif, K., Taguchi, Y., Ramalingam, S., et al. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989524
Human Detection Using Multiple Classifiers Based on Subtraction Stereo with HOG Features
Arie, M., Moro, A., Terabayashi, K., et al. (2012) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.78.494
Tengfei, 刘. L., Yuanwei, 毕. B., Hao, 刘. L., et al. (2026) · Laser & Optoelectronics Progress · doi:10.3788/lop251773
A Biomimetic Vision Architecture
Draper, B. A. (2019) · doi:10.2390/biecoll-icvs2007-57
Yusefi, A., Durdu, A., & Sungur, C. (2020) · DergiPark (Istanbul University)
An implementation of image statiching based on binocular cameras
Wu, S., Yu, S., Chen, W., et al. (2011) · doi:10.1109/ivsurv.2011.6157031
Lightweight and Efficient Multi-Task YOLO for Sensitive Information and Privacy Protection
Ren, P., Yang, S., Zhao, N., Cheng, M., & Guo, Y. (2025) · International Conference on Machine Learning and Computer Application (ICMLCA) · doi:10.1109/icmlca66850.2025.11336299
Hybrid deformable registration method joint using TPS and B-spline in CT image
Zhu, Y., Zhu, Q., Wang, L., et al. (2012) · doi:10.1109/bhi.2012.6211615
Ahmad, S. S., & Lu, Z. (2010) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-61520-903-3.ch004
Learning Spatial and Geometric Information for Robust Features
Zhou, J., Li, Y., & Chen, H. (2023) · International Conference on Consumer Electronics and Computer Engineering (ICCECE) · doi:10.1109/iccece58074.2023.10135293
Yamada, S., & Shimizu, I. (2011) · Medical Entomology and Zoology
Robustness Evaluation of Local Descriptors
Li, B., & Hu, X. (2016) · International Conference on Artificial Intelligence Technologies and Applications (ICAITA) · doi:10.2991/icaita-16.2016.18
Computational visual attention model capable of exploring similarity
Lin, R.-J., Lin, W., & Huang, Y. (2011) · doi:10.1109/cimsivp.2011.5949238
Exploring Model Based Pose Estimation Using Directional Chamfer Matching
Kolden, D. (2019) · NORA - Norwegian Open Research Archives
Deng, X., & Kang, J. (2025) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ade9c6
SAR Target Zero-Shot Recognition with Optical Image Assistance
Chen, J., Yong, Q., Du, L., et al. (2024) · IEEE International Conference on Signal, Information and Data Processing (ICSIDP) · doi:10.1109/icsidp62679.2024.10868138
“In the research field of SAR target recognition, early scholars extract handcrafted features from SAR images to describe targets, such as scale-invariant feature transform (SIFT), and further achieve category identification[2].”
Establishing a relative positioning system to achieve mobile localization
Copeland, B. W. (2017) · DSpace@MIT (Massachusetts Institute of Technology)
Multi-object Template Matching Using Radial Ring Code Histograms
Zheng, S., Zhang, B., & Yang, H. S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21963-9_51
Zhirkov, E. A., & Muratov, E. R. (2026) · Известия высших учебных заведений. Приборостроение · doi:10.17586/0021-3454-2026-69-3-201-210
Contributions au recalage d'images et à la reconstruction 3D de scènes rigides et déformables
Bartoli, A. (2008) · HAL
ENHANCEMENT STRATEGIES FOR FRAME-TO-FRAME UAS STEREO VISUAL ODOMETRY
Kersten, J., & Rodehorst, V. (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/isprsarchives-xli-b3-511-2016
Fast Template Matching Using Pruning Strategy with Haar-like Features
Nguyen, V.-T., Le, K.-D., Tran, M., et al. (2012) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2012.155
Integrated Deconvolution Keypoint Detector and Descriptor Network
Jin, D., & Xu, J. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956255
Perspective Invariant Angle Ordering
Shaw, D., & Barnes, N. M. (2009) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2009.50
Image Stitching Algorithm Based on Area Blocking and Optimal Seam
Mu, Q., & Tang, Y. (2019) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2019.00073
Flier Meet: Crowdsensing Enabled Urban Public Information Reposting and Sharing
Chen, H., Nan, W., Guo, B., et al. (2015) · doi:10.1109/uic-atc-scalcom-cbdcom-iop.2015.84
Enhancing Face Recognition Using PC-SIFTTechnique: A Hybrid Approach
Ahlawat, D., & Nehra, V. (2017) · International Journal of Signal Processing Image Processing and Pattern Recognition · doi:10.14257/ijsip.2017.10.9.02
Relative Spatial Weighting of Features for Localizing Parts of Faces
Bellati, J., & Borges, D. L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41827-3_52
Constructing interactive multi-view videos based on image-based rendering
Chang, S. M., Tsai, J. C., Yen, S. H., et al. (2015) · International Journal of Computational Science and Engineering · doi:10.1504/ijcse.2015.070996
Long, B., Zhou, J., Zhang, D., et al. (2026) · Cybernetics & Systems · doi:10.1080/01969722.2026.2709344
Abeysinghe, S. S. (2007) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k7930rc3
University of Marburg at TRECVID 2010: Semantic Indexing
Mühling, M., Ewerth, R., & Freisleben, B. (2010)
Affective Image Recognition via Data Augmentation and Label Smoothing
Yuan, Z. (2026) · International Conference on Computer Information and Big Data Applications (CIBDA) · doi:10.1145/3813822.3813868
“Early studies mainly relied on traditional vision methods, extracting handcrafted low-level features(e.g.,HOG [3], SIFT[11], andLBP[14]) andcombiningthem with conventional machine-learning classifiers such as SVM [2] and random forests.”
A managerial approach of visual features in mobile robot SLAM
Wang, Y., & Zhang, Y. (2011) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2011.5970742
New idea for SIFT to evaluate the matching result
Youying, Z., & Tadahiro, K. (2017) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip.2017.8313777
Multimedia Databases: Description, Analysis, Modeling, and Retrieval
Oria, V., Li, Y., Dorai, C., et al. (2022) · Chapman & Hall/CRC eBooks · doi:10.1201/9780429171529-107
Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis
Jia, H. B., & Huang, J. (2022) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3579654.3579708
Research on Target Area Mechatronics System for ICF Laser Driver
Wang, L., Jing, Y., Xiang, Y., et al. (2015) · Advances in computer science research · doi:10.2991/iiicec-15.2015.167
LOCAL FEATURE EXTRACTION IN IMAGES
Sergiyenko, A., Serhiienko, P. A., & Орлова, М. (2021) · Information, computing and intelligent systems · doi:10.20535/2708-4930.2.2021.244191
Filtering feature mismatches using genetic algorithm and fundamental matrix
Kim, J., & Jun, H. (2013) · doi:10.1109/ifost.2013.6616902
Distribution-aware FAST feature point extraction and matching based on FPGA
Jin, S., Wang, F., Zhang, Z., et al. (2024) · doi:10.1117/12.3021714
Enhancement via Integrating High Order Coding Information
Huang, Y., & Tan, T. (2014) · SpringerBriefs in computer science · doi:10.1007/978-3-662-45000-0_7
Network illustration Re-Ranking Using Uncertainty aspect Semantic Signatures
Venkatesh, G., & Prasad, K. M. (2015)
Neural Codes for Image Retrieval
Stutz, D. (2015)
Face Recognition Using ALLE and SIFT for Human Robot Interaction
Pan, Y., Ge, S. S., & He, H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03983-6_9
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