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 3,760
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 2,360
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 6,101–6,200 of 56,204
A sliding-window visual-IMU odometer based on tri-focal tensor geometry
Hu, J., & Chen, M.-Y. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907434 · Cited by 52
QGORE: Quadratic-Time Guaranteed Outlier Removal for Point Cloud Registration
Li, J.-Y., Shi, P., Hu, Q.-W., & Zhang, Y.-J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3262780 · Cited by 58
Matrix factorization+ for movie recommendation
Zhao, L., Lu, Z., Plan, S. J., et al. (2016) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 54
A new framework for feature descriptor based on SIFT
Li, C., & Ma, L. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.12.004 · Cited by 54
Hybrid-feature-guided lung nodule type classification on CT images
Yuan, J., Liu, X., Hou, F., Qin, H., & Hao, A. (2017) · Computers & Graphics · doi:10.1016/j.cag.2017.07.020 · Cited by 54
Learning Spatiotemporal T-Junctions for Occlusion Detection
Apostoloff, N., & Fitzgibbon, A. W. (2005) · 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.2005.206 · Cited by 54
A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms
Oza, P. R., Sharma, P., Patel, S., & Bruno, A. (2021) · Journal of Imaging · doi:10.3390/jimaging7090190 · Cited by 54
Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
Garg, S., Harwood, B., Anand, G., & Milford, M. J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3005627 · Cited by 56
A Comparison of Dense Region Detectors for Image Search and Fine-Grained Classification
İşcen, A., Tolias, G., Gosselin, P.-H., & Jeǵou, H. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2423557 · Cited by 54
Multi-camera visual SLAM for off-road navigation
Yang, Y. M., Tang, D., Wang, D., Song, W., Wang, J., & Fu, M. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103505 · Cited by 55
Virtual Mirror Rendering With Stationary RGB-D Cameras and Stored 3-D Background
Shen, J., Su, P.-C., Cheung, S.-C. S., et al. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2268941 · Cited by 54
4D facial expression recognition
Fang, T., Zhao, X., Shah, S. K., & Kakadiaris, I. A. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130440 · Cited by 51
A Near-Term Quantum Computing Approach for Hard Computational Problems in Space ExplorationPreprint
Smelyanskiy, V., Rieffel, E. G., Knysh, S., Williams, C. P., Johnson, M. W., Thom, M. C., Macready, W. G., & Pudenz, K. L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1204.2821 · Cited by 74
Novel Method of Semantic Segmentation Applicable to Augmented Reality
Taeyoung, K., & Lee, S.-H. (2020) · Sensors · doi:10.3390/s20061737 · Cited by 53
City brain: practice of large‐scale artificial intelligence in the real world
Zhang, J., Hua, X., Huang, J., Shen, X., Chen, J., Zhou, Q., Fu, Z., & Zhao, Y. (2019) · IET Smart Cities · doi:10.1049/iet-smc.2019.0034 · Cited by 63
Heterogeneous face image matching using multi-scale features
Liu, S., Yi, D., Lei, Z., & Li, S. Z. (2012) · International Conference on Biometrics · doi:10.1109/icb.2012.6199762 · Cited by 51
SK-Net: Deep Learning on Point Cloud via End-to-End Discovery of Spatial Keypoints
Wu, W., Zhang, Y., Wang, D., et al. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6113 · Cited by 54
Sparse Online Learning of Image Similarity
Gao, X., Hoi, S. C. H., Zhang, Y., Zhou, J., Wan, J., Chen, Z., Li, J., & Zhu, J. (2017) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3065950 · Cited by 51
Sutour, C., Deledalle, C., & Aujol, J. (2015) · SIAM Journal on Imaging Sciences · doi:10.1137/15m1012682 · Cited by 54
A Novel Projective-Consistent Plane Based Image Stitching Method
Zheng, J. G., Wang, Y., Wang, H., et al. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2905692 · Cited by 54
When textual and visual information join forces for multimedia retrieval
Safadi, B., Sahuguet, M., & Huet, B. (2014) · doi:10.1145/2578726.2578760 · Cited by 54
Wiliem, A., Wong, Y., Sanderson, C., et al. (2013) · Proceedings · doi:10.1109/wacv.2013.6475005 · Cited by 54
Encoding Pathlet and SIFT Features With Bagged VLAD for Historical Writer Identification
Lai, S., Zhu, Y., & Jin, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2991880 · Cited by 54
Ancient Chinese architecture 3D preservation by merging ground and aerial point clouds
Gao, X., Shen, S., Zhou, Y., Cui, H., Zhu, L., & Hu, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.023 · Cited by 53
Fast Keypoint Features From Laser Scanner for Robot Localization and Mapping
Kallasi, F., Rizzini, D. L., & Caselli, S. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2517210 · Cited by 54
Saqib, N., Haque, K. F., Yanambaka, V. P., & Abdelgawad, A. (2022) · Algorithms · doi:10.3390/a15040129 · Cited by 66
Simultaneous super-resolution and 3D video using graph-cuts
Tung, T., Nobuhara, S., & Matsuyama, T. (2008) · 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.2008.4587703 · Cited by 53
Qian, H., Li, Y., Yang, J., et al. (2022) · Cement and Concrete Composites · doi:10.1016/j.cemconcomp.2022.104496 · Cited by 54
Review of robot-based automated measurement of vibration for civil engineering structures
Poorghasem, S., & Bao, Y.-N. (2023) · Measurement · doi:10.1016/j.measurement.2022.112382 · Cited by 53
Object Tracking with Dynamic Feature Graph
Tang, F., & Tao, H. (2006) · IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) · doi:10.1109/vspets.2005.1570894 · Cited by 54
Revisiting animal photo‐identification using deep metric learning and network analysis
Miele, V., Dussert, G., Spataro, B., Chamaillé‐Jammes, S., Allainé, D., & Bonenfant, C. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13577 · Cited by 56
Yang, J., Huang, Z., Quan, S., Qi, Z., & Zhang, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3058552 · Cited by 54
Relative Parts: Distinctive Parts for Learning Relative Attributes
Sandeep, R. N., Verma, Y., & Jawahar, C. V. (2014) · 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.2014.462 · Cited by 54
Phantom based evaluation of CT to CBCT image registration for proton therapy dose recalculation
Landry, G., Dedes, G., Zöllner, C., et al. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/2/595 · Cited by 54
Parallel search of k-nearest neighbors with synchronous operations
Sismanis, N., Pitsianis, N. P., & Sun, X. (2012) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec.2012.6408667 · Cited by 54
A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn
Cao, J., Zhang, J., & Jin, X. (2021) · IEEE Access · doi:10.1109/access.2021.3109606 · Cited by 60
Simultaneous localization and mapping: A feature-based probabilistic approach
Skrzypczyński, P. (2009) · International Journal of Applied Mathematics and Computer Science · doi:10.2478/v10006-009-0045-z · Cited by 53
Car make and model recognition using 3D curve alignment
Hsiao, E., Sinha, S. N., Ramnath, K., et al. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836126 · Cited by 54
Performance evaluation of feature detection and matching in stereo visual odometry
Jiang, Y., Yun-xi, X., & Liu, Y. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.06.055 · Cited by 52
Guetari, R., Ayari, H., & Sakly, H. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01894-7 · Cited by 63
Predicting gene regulatory interactions based on spatial gene expression data and deep learning
Yang, Y., Fang, Q., & Shen, H. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007324 · Cited by 53
Object detection and identification for blind people in video scene
Jabnoun, H., Benzarti, F., & Amiri, H. (2015) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2015.7489256 · Cited by 55
Integrating SIFT and CNN Feature Matching for Partial-Duplicate Image Detection
Zhou, Z., Wu, Q. M. J., Wan, S., Sun, W., & Sun, X. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2909936 · Cited by 53
Ren, M., Vu, H. Q., Li, G., & Law, R. (2020) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2020.1765226 · Cited by 55
Sound event recognition in unstructured environments using spectrogram image processing
Dennis, J. W. (2014) · doi:10.32657/10356/59272 · Cited by 52
Face recognition based on the multi-scale local image structures
Geng, C., & Jiang, X. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.011 · Cited by 53
Exploring Local and Overall Ordinal Information for Robust Feature Description
Wang, Z., Fan, B., Wang, G., & Wu, F. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2513396 · Cited by 54
Illumination Normalization for SIFT Based Finger Vein Authentication
Kim, H.-G., Lee, E., Yoon, G.-J., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33191-6_3 · Cited by 53
Parametric local multimodal hashing for cross-view similarity search
Zhai, D., Chang, H., Zhen, Y., Liu, X., Chen, X., & Gao, W. (2013) · International Joint Conference on Artificial Intelligence · Cited by 52
Hybrid Genetic Algorithm for Medical Image Feature Extraction and Selection
Nagarajan, G., Minu, R. I., Muthukumar, B., et al. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.192 · Cited by 53
Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach
Bruneau, P., Gelgon, M., & Picarougne, F. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.08.006 · Cited by 53
Euro Banknote Recognition System for Blind People
Dunai, L. D., Pérez, M. C., Peris-Fajarnés, G., & Lengua, I. L. (2017) · Italian National Conference on Sensors · doi:10.3390/s17010184 · Cited by 48
Co-transduction for Shape Retrieval
Bai, X., Wang, B., Wang, X., Liu, W., & Tu, Z. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15558-1_24 · Cited by 52
VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results
Wen, L., Zhu, P., Du, D., Bian, X., Ling, H., Hu, Q., Liu, C., Cheng, H., Liu, X., Ma, W., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_28 · Cited by 50
A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines
Zhu, J., Guo, Y., Yue, F., Yuan, H., Yang, A., Wang, X., & Rong, M. (2020) · IEEE Access · doi:10.1109/access.2020.2995608 · Cited by 52
Annotating images and image objects using a hierarchical dirichlet process model
Yakhnenko, O., & Honavar, V. (2008) · doi:10.1145/1509212.1509213 · Cited by 53
Sloop: A pattern retrieval engine for individual animal identification
Duyck, J., Finn, C., Hutcheon, A., Vera, P., Salas, J., & Ravela, S. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.07.017 · Cited by 53
Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model
Fu, C., Duan, R., Kırcalı, D., & Kayacan, E. (2016) · Sensors · doi:10.3390/s16091406 · Cited by 53
Knowledge-Augmented Deep Learning and its Applications: A Survey
Cui, Z., Gao, T., Talamadupula, K., & Ji, Q. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3338619 · Cited by 66
Zeybek, M. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/abf25d · Cited by 53
Rapid seismic risk assessment of bridges using UAV aerial photogrammetry
Wang, X., Demartino, C., Narazaki, Y., Monti, G., & Spencer, B. F. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.115589 · Cited by 54
Attention-Based User Interface Design for a Tele-Operated Crane
Chen, Y., Chi, H.-L., Kang, S.-C., & Hsieh, S. (2015) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000489 · Cited by 53
Alcantarilla, P. F., Bergasa, L. M., & Davison, A. J. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.11.001 · Cited by 54
Hong, Q., Jiang, H., Xiao, P., et al. (2024) · IEEE Transactions on Computers · doi:10.1109/tc.2024.3504817 · Cited by 53
Real-time image-based rigid registration of three-dimensional ultrasound
Schneider, R. J., Perrin, D. P., Vasilyev, N. V., et al. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.10.004 · Cited by 53
Uwizeye, C., Brisbin, M. M., Gallet, B., Chevalier, F., LeKieffre, C., Schieber, N. L., Falconet, D., Wangpraseurt, D., Schertel, L., Stryhanyuk, H., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025252118 · Cited by 52
Compact Video Description for Copy Detection with Precise Temporal Alignment
Douze, M., Jégou, H., Schmid, C., & Pérez, P. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15549-9_38 · Cited by 53
Soft Exemplar Highlighting for Cross-View Image-Based Geo-Localization
Guo, Y., Choi, M., Li, K., et al. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3152046 · Cited by 53
Visual Bilingual Lexicon Induction with Transferred ConvNet Features
Kiela, D., Vulić, I., & Clark, S. C. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1015 · Cited by 46
Feature-Based Laser Scan Matching and Its Application for Indoor Mapping
Li, J., Zhong, R., Hu, Q., et al. (2016) · Sensors · doi:10.3390/s16081265 · Cited by 53
Photogrammetry using visible, infrared, hyperspectral and thermal imaging of crime scenes
Edelman, G. J., & Aalders, M. C. G. (2018) · Forensic Science International · doi:10.1016/j.forsciint.2018.09.025 · Cited by 53
Gago, J., Fernie, A. R., Nikoloski, Z., Tohge, T., Martorell, S., Escalona, J. M., Ribas‐Carbó, M., Flexas, J., & Medrano, H. (2017) · Plant Methods · doi:10.1186/s13007-017-0241-z · Cited by 53
BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Autonomous Ground Vehicles
Luo, L., Cao, S.-Y., Xiaorui, L., Xu, J., Ai, R., Zhu, Y., & Chen, X. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3585385 · Cited by 59
Object classification in 3D baggage security computed tomography imagery using visual codebooks
Flitton, G. T., Mouton, A., & Breckon, T. P. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.02.006 · Cited by 49
Attribute based object identification
Sun, Y., Bo, L., & Fox, D. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630858 · Cited by 53
Driver Monitoring Using Sparse Representation With Part-Based Temporal Face Descriptors
Chiou, C.-Y., Wang, W.-C., Lu, S.-C., Huang, C.-R., Chung, P., & Lai, Y.-Y. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2892155 · Cited by 53
Global Feature Tracking and Similarity Estimation in Time‐Dependent Scalar Fields
Saikia, H., & Weinkauf, T. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13163 · Cited by 53
Comparison of local image descriptors for full 6 degree-of-freedom pose estimation
Vikstén, F., Forssén, P.-E., Johansson, B., & Moe, A. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152360 · Cited by 53
Personalized Video Recommendation Using Rich Contents from Videos
Du, X., Yin, H., Chen, L., Wang, Y., Yang, Y., & Zhou, X. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2885520 · Cited by 53
A New Learning Approach to Malware Classification Using Discriminative Feature Extraction
Liu, Y., Lai, Y., Wang, Z., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2892500 · Cited by 53
Soft Tissue Tracking for Minimally Invasive Surgery: Learning Local Deformation Online
Mountney, P., & Yang, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85990-1_44 · Cited by 56
A comparison of feature descriptors for visual SLAM
Hartmann, J., Klussendorff, J. H., & Maehle, E. (2013) · European Conference on Mobile Robots · doi:10.1109/ecmr.2013.6698820 · Cited by 53
Comparison of photo‐matching algorithms commonly used for photographic capture–recapture studies
Matthé, M., Sannolo, M., Winiarski, K. J., et al. (2017) · Ecology and Evolution · doi:10.1002/ece3.3140 · Cited by 53
A Framework for Handling Spatiotemporal Variations in Video Copy Detection
Chiu, C., Chen, C., & Chien, L. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.918447 · Cited by 53
Implications of Pooling Strategies in Convolutional Neural Networks: A Deep Insight
Sharma, S., & Mehra, R. (2019) · Foundations of Computing and Decision Sciences · doi:10.2478/fcds-2019-0016 · Cited by 52
Deterministically maximizing feasible subsystem for robust model fitting with unit norm constraint
Zheng, Y., Sugimoto, S., & Okutomi, M. (2011) · doi:10.1109/cvpr.2011.5995640 · Cited by 53
SIFT features for face recognition
Geng, C., & Jiang, X. (2009) · IEEE International Conference on Computer Science and Information Technology · doi:10.1109/iccsit.2009.5234877 · Cited by 53
Zhu, Q., Wang, Z., Hu, H., Xie, L., Ge, X., & Zhang, Y. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.05.024 · Cited by 52
Occlusion Reasoning for Object Detectionunder Arbitrary Viewpoint
Hsiao, E., & Hebert, M. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2303085 · Cited by 53
Texture and Shape Biased Two-Stream Networks for Clothing Classification and Attribute Recognition
Zhang, Y., Zhang, P., Yuan, C., & Wang, Z. (2020) · 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/cvpr42600.2020.01355 · Cited by 53
Line as a Visual Sentence: Context-Aware Line Descriptor for Visual Localization
Yoon, S., & Kim, A. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3111760 · Cited by 55
Efficient Clothing Retrieval with Semantic-Preserving Visual Phrases
Fu, J., Wang, J., Li, Z., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37444-9_33 · Cited by 53
An ear biometric system based on artificial bees and the scale invariant feature transform
Ghoualmi, L., Draa, A., & Chıkhı, S. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.03.004 · Cited by 53
Real-Time Time-to-Collision from Variation of Intrinsic Scale
Nègre, A., Braillon, C., Crowley, J. L., & Laugier, C. (2008) · Springer tracts in advanced robotics · doi:10.1007/978-3-540-77457-0_8 · Cited by 54
Discrete Curvature Representations for Noise Robust Image Corner Detection
Zhang, W., Sun, C., Breckon, T. P., & AlShammari, N. K. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2910655 · Cited by 55
Attribution in Scale and Space
Xu, S., Venugopalan, S., & Sundararajan, M. (2020) · 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/cvpr42600.2020.00970 · Cited by 58
Edge SLAM: Edge Points Based Monocular Visual SLAM
Maity, S., Saha, A., & Bhowmick, B. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.284 · Cited by 56
Sequential Spectral Learning to Hash with Multiple Representations
Kim, S., Kang, Y., & Choi, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33715-4_39 · Cited by 53
Socialized Mobile Photography: Learning to Photograph With Social Context via Mobile Devices
Yin, W., Mei, T., Chen, C. W., et al. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2283468 · Cited by 53
Runoff and nutrient loss from a water-repellent soil
Müller, K., Mason, K., Strozzi, A. G., et al. (2018) · Geoderma · doi:10.1016/j.geoderma.2018.02.019 · Cited by 53
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
