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 49,401–49,500 of 56,203
Robot system for accurate nail recognition, localization and retrieval
Wang, H., Hu, Z., & Zhao, X. (2025) · Intelligent Service Robotics · doi:10.1007/s11370-025-00645-7
Snake-Shaped Fiber-Optic Cantilever Accelerometer for Pipeline Intrusion Events Monitoring
Li, J., Liu, Y., Sun, T. P., et al. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3665535
“Video image monitoring can achieve a target recognition rate of over 90% through deep learning algorithms such as YOLO [7].”
Nayko, F., & Lichti, D. D. (2026) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2026.2684079
Searching of near exact duplicate images in cloud database
Maneesha, & Chana, I. (2016) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/inventive.2016.7823239
Image Matching Algorithm Based on GMS
Li, H., Liu, W., Han, C., & Kong, X. (2019) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc/dsci/smartcns.2019.00073 · Cited by 1
Ear Recognition Based on the SIFT Descriptor with Global Context and the
Zeng, H., Mu, Z., Li, Y., et al. (2009)
Neurosurgical Flexible Probe Microscopy with Enhanced Architectural and Cytological Detail
Osman, H., Elsahy, D., Slivova, V., et al. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.05.039
Deep Convolutional Feature-Based Fluorescence-to-Color Image Registration
Liu, X., Quang, T., Deng, W., et al. (2021) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea52024.2021.9478607
Development of Collision Alert System Using a Single Wide Angle Fovea Sensor
Shimizu, S., & Otsuka, K. (2026) · IEEJ Journal of Industry Applications · doi:10.1541/ieejjia.20250633
Multiple partial solutions for the point-to-point correspondence problem in three views
Carrasco, M. (2012) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2012.6356179
Mehta, R., Chum, O., & Matas, J. (2015) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2015.7333840
DÖKÜ, F. E., Kayikci, D., & Kılıçkaya, N. (2026) · Open Archaeology · doi:10.1515/opar-2025-0076
“Concept have used this study: − Scale-Invariant Feature Transform (SIFT): SIFT is a well-established method for detecting and describing local features in images (Lowe 2004).”
Object recognition in a context-aware application
Vo, T., Tran, D., & Ma, W. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6707099
A lowbit rate vocabulary coding scheme for mobile landmark search
Ji, R., Duan, L.-Y., Chen, J., et al. (2011)
Biologically-inspired place recognition with neural networks
Chen, Z. (2016) · Queensland University of Technology · doi:10.5204/thesis.eprints.98550
Li, X. Y., & Chen, D. Y. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.181-182.37
Automatic Suit Lapel Types Detection and Classification Using Swin Transformer-Improved YOLOv5n
Pan, S., Zhong, N., Xu, J., et al. (2025) · Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence (DEAI) · doi:10.1145/3745238.3745271
Canonical correlation analysis of local feature set for view-based object recognition
Han, X., Chen, Y., & Ruan, X. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116496
Xavier, N. A. Z., Lapa, J. D. A., Honório, D. D. D. C., et al. (2023) · Latin American Robotics Symposium, Brazilian Symposium on Robotics and Workshop on Robotics in Education (LARS/SBR/WRE) · doi:10.1109/lars/sbr/wre59448.2023.10333064
Image Retrieval-Based Localization Under Seasonal Changes
Zhu, H. (2022) · IEEE International Conference on Computer Communication and Artificial Intelligence (CCAI) · doi:10.1109/ccai55564.2022.9807825
Developing visual functions for smart camera network
Tang, C.-Y., Wu, Y.-L., Chan, K.-H., et al. (2016) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2016.7872946
Image Mosaics Solution for Video Conference
Yang, P., Zheng, M., Zhang, Z., et al. (2010) · doi:10.1109/icmult.2010.5630427
Data-centric artificial intelligence in health care
Chang, E. Y. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99136-0.00005-2 · Cited by 2
Digital holographic synthetic aperture method based on the improved SIFT
Shu, S., Lu, Z., Zhang, T., et al. (2025) · Journal of Modern Optics · doi:10.1080/09500340.2025.2515117
“Constrained by the field-of-view of the digital holographic microimaging system and the camera’s sensitive size, images can be fused using the SIFT algorithm [39–41].”
Proximity Searching and the Quest for the Holy Grail
Mount, D. M. (2012)
Comparison and fusion of vision and range measurements for robot pose estimation
Zingaretti, P., & Frontoni, E. (2007) · 2006 14th Mediterranean Conference on Control and Automation · doi:10.1109/med.2007.4433854
A Novel MSPA-OS Method for Robust and Fast Optical-to-SAR Image Registration
Ye, S., Liu, J., Tan, S., et al. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3627793
Júnior, M. L. G. D. Q. P., Melo, J. L. D. S., de Miranda, M. P., et al. (2021) · doi:10.5753/sbiagro.2021.18383
Moving Object Tracking Method Based on Topology Structure
Sun, N., & Wang, Q. (2013) · International Conference on Image and Graphics · doi:10.1109/icig.2013.78
A Structure Feature Algorithm for Multi-modal Forearm RegistrationPreprint
Li, J., Ding, Y. Y., Zhang, W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05485
Seamlessly Natural: Image Stitching with Natural Appearance Preservation
Tchana, G. L. N., Fotso, D. B. M., Hendricks, A., et al. (2026) · Technologies · doi:10.3390/technologies14030186
Quality enhancement for feature matching on car black box videos
Simon, C., Lee, M. H., & Park, I. K. (2014) · doi:10.1109/apsipa.2014.7041750
Zhang, D., Huang, S., Zhang, B., et al. (2026) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-026-04735-7
“Considering the relatively weak texture and abundant repetitive structures on the Szechuan pepper surface, the Scale-Invariant Feature Transform (SIFT) algorithm was employed for feature point detection and description [32 ].”
Active control points-based 6DoF pose tracking for industrial metal objects
Zhao, X., Chen, Y., Shen, C., et al. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07403-x
“For instance, Skrypnyk et al. [19] employed SIFT [20] for tracking.”
Militello, C., Barbera, A., Mirotta, M., et al. (2026) · Array · doi:10.1016/j.array.2026.100994
Application of Detection and Correlation Filter in Long-Term Vehicle Tracking
Chen, Y., Peng, J., Pei, S., et al. (2021) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi52967.2021.9540206
FGR: Frequency Aware and Geometric Structure-Guided Multi-modality Image Registration Framework
Ye, Q., & Wang, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6957-1_4
Position Transformer for Image Retrieval
Tian, H., & Wu, L. (2022) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml57342.2022.10009832
Zamankhani, S., Honig, H. L., Blum, M., Troebs, E., Schaaf, P., & Sattler, K.-U. (2026) · Journal of Computational Electronics · doi:10.1007/s10825-026-02611-7
“Automated alignment using feature-based descriptors (e.g., ORB) and model fitting (e.g., RANSAC) has become standard for creating reproducible difference maps, allowing observed structural evolution to be translated into measurable, uncertainty-aware descriptors [20–22].”
SIFT Based on MediaPipe to Identify Athletic Posture
Wu, J., Wang, Y.-E., Hsueh, P.-H., et al. (2025) · doi:10.1109/anzcc65042.2025.10873344
“For this study, we applied an edge threshold of 10, aligning with the recommendations in Lowe’s original work [5], to enhance stability and consistency in key point selection.”
InfluentialYoshida, S., Okada, H., Ogawa, T., et al. (2013) · ITE Transactions on Media Technology and Applications · doi:10.3169/mta.1.237
Density corrected sparse recovery when R.I.P. condition is broken
Lin, M., Lan, Z., & Hauptmann, A. G. (2015) · International Conference on Artificial Intelligence
TESIS DOCTORAL Diagnóstico automático de tuberculosis: una decisión ante incertidumbre
Mozos, R. S., Rodríguez, A. A., & Pérez‐Cruz, F. (2009)
Parallel similarity search based on the dimensions value cardinalities of image descriptor vectors
Rafailidis, D., & Manolopoulos, Y. (2015) · ACM Symposium on Applied Computing · doi:10.1145/2695664.2695857
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face RecognitionPreprint
Huang, S., Yang, D., Zhang, H., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.7446
Yuan, L., Su, T., Wang, R. F., et al. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3593386
Mixed 2D-3D Object Recognition for Autonomous Inspection in Industrial Environments
Melacci, S., Nunziati, G., Corradini, B. T., Magistris, G. D., Fiorucci, M., & Schillaci, G. (2025) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1109/aimlsystems67835.2025.11331264
“SIFT (Scale-Invariant Feature Transform) [7] leverages the Difference of Gaussian (DoG) detector and produces descriptors robust to scale and rotation.”
Image Matching Algorithm Based on Contourlet Transform and VA Fast Search
Fu, J., Wang, P., He, Y. P., et al. (2013) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/eeic-13.2013.105
An efficient E-commerce based online shopping for visually impaired people
Jabez, J., Gowri, S., Jinila, Y. B., et al. (2023) · AIP conference proceedings · doi:10.1063/5.0142855
Naurunna, L. A. H., Premaratne, S. C., & Ginige, T. N. D. S. (2023) · WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL · doi:10.37394/23203.2023.18.60
Patch-Based image fusion for computational photography
Blandon, C. F. O. (2018) · doi:10.70675/8aab09f8z1fccz4c95z836czdb95c2f7b1f8
A Fast Neural-Dynamical Approach to Scale-Invariant Object Detection
Terzić, K., Lobato, D., Saleiro, M., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12637-1_64
Understanding architectural characteristics of multimedia retrieval workloads
Dai, C., Lv, C., Li, J., et al. (2013) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/2465529.2465541
Depth Estimation from a Single Holoscopic 3D Image and Image Up-sampling with Deep-learning
Aondoakaa, A. S. (2020) · Brunel University Research Archive (BURA) (Brunel University London)
Prystavka, P. (2015) · Actual problems of automation and information technology · doi:10.15421/431507
Dong, Z. H., Lin, B., & Xie, F. (2023) · IEEE Access · doi:10.1109/access.2023.3264502 · Cited by 1
Multi-features description for an efficient image retrieval
Hbali, S., Sadgal, M., & Fazziki, A. E. (2015) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2015.7407908
Ke, D., Li, J., Liao, J., Chen, J., Wang, X., Meng, F., & Zhang, D. (2025) · Measurement · doi:10.1016/j.measurement.2025.119973
VS-FPM: large-format, label-free virtual histopathology microscopyPreprint
Bendkowski, C., Levine, A. P., Rodriguez‐Justo, M., et al. (2025) · medRxiv · doi:10.1101/2025.05.20.25327933
Optimal detection of the feature matching map in presence of noise and outliers
Galstyan, T., Minasyan, A., & Dalalyan, A. S. (2022) · Electronic Journal of Statistics · doi:10.1214/22-ejs2076
A deep learning-based image stitching method for fiber-optic image bundles imaging
Gao, Y., Zheng, Z., Huang, K., et al. (2023) · International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC) · doi:10.1109/raiic59453.2023.10280833
A Point Cloud Connectivity Reduction Algorithm Based on Distance Thresholds
Wang, J., Li, Y., Liu, W., et al. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326687
Face Recognition Using Firefly Model
Barati, M. N., & Khademi, M. (2015) · International journal of advance research and innovative ideas in education
Universiteit Leiden Opleiding Informatica
Zaaijer, S., & Bohrweg, N. (2013)
Recognition and navigation for UAVs using computer vision
May, M., Proctor, S., Markham, K., et al. (2009) · Research Explorer (The University of Manchester)
Learning Robust and Discriminative Manifold Representations for Pattern Recognition
Kumar, S. (2016) · RIT Scholar Works (Rochester Institute of Technology)
Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes
Li, X., Li, L., Wu, Y., Rao, T., Zhang, K., Hui, X., & Pan, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37369-4_34
Identify mismatches for stereo matching using sequential RVR
Liu, A., Xu, L., Jiang, L., et al. (2012) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip.2012.6391456
Nested Graph Representation for Visual SLAM based on Local and Global Feature Processing
Elgayar, S., Salem, M. A. ‐., & Roushdy, M. I. (2013) · International Journal of Computer Applications · doi:10.5120/13604-1350
Analysis of Extracellular Vesicle Data on Fluorescence and Atomic Force Microscopy Images
Janout, H., Schurr, J., Haghofer, A., et al. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-38854-5_2
Branke, J., Zieher, T., Pfeiffer, J., et al. (2025) · Geomorphology · doi:10.1016/j.geomorph.2025.109996
Rubio, J., de Andrés, A., Paulete, C., Gallego, Á., & Escobar, D. (2025) · Aerospace · doi:10.3390/aerospace12100942
A Novel Approach to Retrieval of Similar Patterns in Biological Images
Sluzek, A. S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41939-3_63
Improving Dynamic Visual SLAM in Robotic Environments via Angle-Based Optical Flow Analysis
DİKİCİ, S., & Arı, F. (2026) · Electronics · doi:10.3390/electronics15010223
Interactive Omnidirectional Indoor Tour
Bazin, J., Saurer, O., Fraundorfer, F., et al. (2013) · doi:10.1002/9781118583593.ch20
Cross-domain Image Translation and Feature Extraction for Large FoV Aerial Crop NIR Image Creation
Rathore, S., Shukla, A., Upadhyay, A., et al. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-5212-6_1
Local Feature Detection Based on Image Block Discrete Scale Space
Leng, J., Li, T.-H., & Xu, W. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-25128-4_69
A Palmprint ROI Segmentation Algorithm for Eliminating Wrist Interference
Zhang, D., Li, X., & Huang, X. (2019) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei48845.2019.8966028
Towards an Intelligent Fuzzy-fusion Model for Identity Document Classification
Khandan, N., Beheshti, A., Farhood, H., et al. (2021) · doi:10.1145/3487664.3487738
Using syntactic methods and LSTM to the recognition of objects visual patterns
Astolfi, G., Weber, V. A. D. M., Oliveira, A. D. S., et al. (2019) · doi:10.5753/wvc.2019.7632
Omnidirectional Object Recognition Based Mobile Robot Localization
Kim, S., & Kweon, I. S. (2013) · Lecture notes in electrical engineering · doi:10.1007/978-94-007-6738-6_120
Scaled-Distance-Transforms and Monotonicity of Autocorrelations
Bouchot, J.-L., & Morain-Nicolier, F. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2325407
ESCal: Efficient and Scalable Calibration of Camera Networks Using a Top View
Ziegler, T., Müller, M., & Khelil, A. (2024) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta63115.2024.00031
Li, Z., & Zhou, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228299
Real-Time Pedestrian-Vehicle Conflict Detection Algorithm Using Video Data
Liu, S., Zhu, T., Zhang, Y., et al. (2014) · doi:10.1061/9780784413623.052
Tree stem diameter estimation from terrestrial point clouds
Forsman, M. (2018) · Epsilon Open Archive (Sveriges lantbruksuniversitet biblioteket (Swedish University of Agricultural Sciences))
An approach to loop-closing based on RGB-D images
Zhao, F., Sun, M., Li, X., & Zhang, X. (2013) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2013.6739539
Ghallabi, F. (2020) · theses.fr (ABES) · doi:10.70675/15d2e2cfz238dz4e81z9e50z7a3a1e64d898 · Cited by 3
Boosting Correspondence Learning with Structure-Aware Estimator
Yan, T., An, W., Wang, P., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37531-5_10
Melnyk, A., Kozarov, L., & Wachsmann‐Hogiu, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4191112
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
