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 2,046
Research works printed but not linked: 3
- Harris, C. and Stephens, M. 1988. Acombined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.
- Mikolajczyk, K. 2002. Detection of local features invariant to affine transformations, Ph.D. thesis, Institut National Polytechnique de Grenoble, France.
- Torr, P. 1995. Motion segmentation and outlier detection, Ph.D. Thesis, Dept. of Engineering Science, University of Oxford, UK.
Cited by
Sorted by citations
Items 17,101–17,200 of 56,201
A Novel Object Detection Method in City Aerial Image Based on Deformable Convolutional Networks
Zhou, L.-Q., Sun, P., Li, D., & Piao, J.-C. (2022) · IEEE Access · doi:10.1109/access.2022.3156953 · Cited by 15
Markerless Augmented Reality Using Image Mosaics
Azzari, P., Stefano, L. D., Tombari, F., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69905-7_47 · Cited by 15
Zhou, Y., Song, Y. T., Cui, S., Zhu, H., Sun, J., & Qin, W. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3064311 · Cited by 16
Land cover classification using geo-referenced photos
Leung, D., & Newsam, S. D. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2261-2 · Cited by 15
Affine-Transformation Parameters Regression for Face Alignment
Li, X., Xu, Y., Lv, Q., et al. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2499778 · Cited by 15
Enhanced Spatial Pyramid Matching Using Log-Polar-Based Image Subdivision and Representation
Zhang, E., & Mayo, M. (2010) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2010.46 · Cited by 15
Relevance Ranking for Vertical Search Engines
(2014) · Elsevier eBooks · doi:10.1016/c2012-0-02862-1 · Cited by 17
Loop Closure Detection Via Maximization of Mutual Information
ZHANG, G., Yan, X., & Ye, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2937967 · Cited by 15
Bi, R., Gan, S., Yuan, X., et al. (2021) · Sensors · doi:10.3390/s21238109 · Cited by 15
Real-time robust individual X point localization for stereoscopic tracking
Wang, J., Ji, X., Zhang, X., et al. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.07.002 · Cited by 15
Weber Binarized Statistical Image Features (WBSIF) based video copy detection
Boukhari, A., & Serir, A. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.10.015 · Cited by 15
SPARTAN/SEXTANT/COMPASS: Advancing Space Rover Vision via Reconfigurable Platforms
Lentaris, G., Stamoulias, I., Diamantopoulos, D., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16214-0_44 · Cited by 15
Background subtraction driven seeds selection for moving objects segmentation and matting
Zhong, B., Chen, Y., Chen, Y., et al. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.10.001 · Cited by 15
Uncooperative Spacecraft Pose Estimation Using Monocular Monochromatic Images
Shi, J.-F., & Ulrich, S. (2021) · Journal of Spacecraft and Rockets · doi:10.2514/1.a34775 · Cited by 15
Quantitative and Qualitative Evaluation of Performance and Robustness of Image Stitching Algorithms
Dissanayake, V., Herath, S., Rasnayaka, S., et al. (2015) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2015.7371297 · Cited by 15
A Coarse-to-Fine Feature Match Network Using Transformers for Remote Sensing Image Registration
Liang, C., Dong, Y., Zhao, C., & Sun, Z. (2023) · Remote Sensing · doi:10.3390/rs15133243 · Cited by 15
Oblique aerial image matching based on iterative simulation and homography evaluation
Song, W.-H., Jung, H.-G., Gwak, I.-Y., & Lee, S. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.10.027 · Cited by 16
Visual guard rail detection for advanced highway assistance systems
Scharwächter, T., Schuler, M., & Franke, U. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856573 · Cited by 15
Parallel K Nearest Neighbor Matching for 3D Reconstruction
Cao, M., Li, L. J., Xie, W., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2912647 · Cited by 15
Mining Semantically Consistent Patterns for Cross-View Data
Zhang, L., Zhao, Y., Zhu, Z., et al. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2313866 · Cited by 15
An Efficient Automated Detection of Schizophrenia Using k-NN and Bag of Words Features
Tyagi, A., Singh, V. P., & Gore, M. M. (2023) · SN Computer Science · doi:10.1007/s42979-023-01947-2 · Cited by 15
Detection of drivers visual attention using smartphone
Xiao, D., & Feng, C. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603247 · Cited by 15
Monocular Rear-View Obstacle Detection Using Residual Flow
Molineros, J., Cheng, S. Y., Owechko, Y., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33868-7_50 · Cited by 15
Offshore Ship Detection in Foggy Weather Based on Improved YOLOv8
Liang, S., Liu, X., Yang, Z., et al. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12091641 · Cited by 15
Quality factors affecting iris segmentation and matching
Alonso‐Fernandez, F., & Bigün, J. (2013) · International Conference on Biometrics · doi:10.1109/icb.2013.6613016 · Cited by 15
Li, Y., Zhang, Z., Wang, X., et al. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108149 · Cited by 15
Arashloo, S. R., Kittler, J., & Christmas, W. J. (2010) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2010.5634502 · Cited by 15
Thubsaeng, W., Kawewong, A., & Patanukhom, K. (2014) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse.2014.6841838 · Cited by 15
ACDAS: Authenticated controlled data access and sharing scheme for cloud storage
Tiwari, D., Chaturvedi, G. K., & Gangadharan, G. R. (2019) · International Journal of Communication Systems · doi:10.1002/dac.4072 · Cited by 15
SliceNets — A Scalable Approach for Object Detection in 3D CT Scans
Yang, A., Pan, F., Saragadam, V., Dao, D. L., Hui, Z., Chang, J.-H. R., & Sankaranarayanan, A. C. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00038 · Cited by 14
Exploring Urban Spatial Quality Through Street View Imagery and Human Perception Analysis
Li, Y., Lu, J., Meng, Y., et al. (2025) · Buildings · doi:10.3390/buildings15173116 · Cited by 15
Sheeba, P. T., & Murugan, S. (2019) · Evolutionary Intelligence · doi:10.1007/s12065-019-00245-2 · Cited by 15
Natural Feature Tracking Augmented Reality for On-Site Assembly Assistance Systems
Radkowski, R., & Oliver, J. H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39420-1_30 · Cited by 15
A Robust Analytical Solution to Isometric Shape-from-Template with Focal Length Calibration
Bartoli, A., Pizarro, D., & Collins, T. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.123 · Cited by 15
RGB-D object classification using covariance descriptors
Fehr, D., Beksi, W. J., Zermas, D., et al. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907663 · Cited by 15
A non-rigid appearance model for shape description and recognition
Almazán, J., Fornés, A., & Valveny, E. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.01.010 · Cited by 15
Medical Image Segmentation Using Descriptive Image Features
Yang, M., Yuan, Y., Li, X., et al. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.94 · Cited by 15
Dynamic Two-Stage Image Retrieval from Large Multimodal Databases
Arampatzis, A., Zagoris, K., & Chatzichristofis, S. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20161-5_33 · Cited by 15
Learning the compositional structure of man-made objects for 3D shape retrieval
Wessel, R., & Klein, R. (2010) · doi:10.5555/2381147.2381157 · Cited by 15
On the Definiteness of Earth Mover’s Distance and Its Relation to Set Intersection
Gardner, A., Duncan, C. A., Kanno, J., & Šelmić, R. R. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2761798 · Cited by 20
Li, N., Tu, W., Ai, H., Deng, H., Tao, J., Hu, T., & Sun, X. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22913 · Cited by 16
Compressed Sensing for Robust Texture Classification
Liu, L., Fieguth, P., & Kuang, G. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19315-6_30 · Cited by 15
Cao, S., Yu, Y., Guan, H., Peng, D., & Yan, W. (2019) · Remote Sensing · doi:10.3390/rs11141708 · Cited by 15
Dorini, L. B., & Goldenstein, S. K. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.07.009 · Cited by 15
Pose robust low-resolution face recognition via coupled kernel-based enhanced discriminant analysis
Wang, X., Hu, H., & Gu, J. (2016) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2016.7451108 · Cited by 15
Video Scene Retrieval Based on Local Region Features
Anjulan, A., & Canagarajah, N. C. (2006) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2006.313044 · Cited by 15
RCVS: A Unified Registration and Fusion Framework for Video Streams
Xie, H., Sang, M., Zhang, Y., et al. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3443673 · Cited by 15
Dual Model Medical Invoices Recognition
Yi, F., Zhao, Y.-F., Sheng, G., Xie, K., Wen, C. B., Tang, X., & Xuan, Q. (2019) · Sensors · doi:10.3390/s19204370 · Cited by 15
Complex Event Detection Using Joint Max Margin and Semantic Features
Abbasnejad, I., Sridharan, S., Denman, S., Fookes, C., & Lucey, S. (2016) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2016.7797023 · Cited by 16
On combining side information and unlabeled data for heterogeneous multi-task metric learning
Luo, Y., Wen, Y., & Tao, D. (2016) · UTS ePRESS (University of Technology Sydney) · Cited by 15
Changing the Image Memorability: From Basic Photo Editing to GANs
Sidorov, O. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00107 · Cited by 15
Optimized Periocular Template Selection for Human Recognition
Bakshi, S., Sa, P. K., & Majhi, B. (2013) · BioMed Research International · doi:10.1155/2013/481431 · Cited by 15
A novel approach for extraction of ripple mark parameters based on SfM
Li, L., Wang, R., Lin, J., et al. (2019) · Sedimentary Geology · doi:10.1016/j.sedgeo.2019.105523 · Cited by 15
Robust Pose Estimation with 3D Textured Models
Gall, J., Rosenhahn, B., & Seidel, H. (2006) · Lecture notes in computer science · doi:10.1007/11949534_9 · Cited by 15
Visual Trajectory-Tracking Model-Based Control for Mobile Robots
Zdešar, A., Škrjanc, I., & Klančar, G. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/56757 · Cited by 15
Zhang, Y., Wang, J., Chen, J., Shi, D., & Chen, X. (2024) · Remote Sensing · doi:10.3390/rs16183368 · Cited by 15
“To measure the performance of our algorithm, we compare the image registration algorithm presented in this paper, which is based on ORB features, with other classical feature extractors [27,29–31].”
Major Convolutional Neural Networks in Image Classification: A Survey
Kumar, N., Kaur, N., & Gupta, D. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-3020-3_23 · Cited by 17
Vision-based Vehicle Detection and Distance Estimation
Qiao, D., & Zulkernine, F. H. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308364 · Cited by 15
Cast indexing for videos by NCuts and page ranking
Gao, Y., Wang, T., Li, J., et al. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282345 · Cited by 15
Mining consistent correspondences using co-occurrence statistics
Xiao, G., Wang, S., Wang, H., et al. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108062 · Cited by 15
A 42fps full-HD ORB feature extraction accelerator with reduced memory overhead
Sun, R., Liu, P., Wang, J., et al. (2017) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2017.8280137 · Cited by 15
A robust kinship verification scheme using face age transformation
Kim, H., Kim, H., Shim, J., & Hwang, E. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103662 · Cited by 15
Boosting k-NN for Categorization of Natural Scenes
Nock, R., Piro, P., Nielsen, F., et al. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-012-0539-2 · Cited by 15
Cao, Y., Cao, Y., Dong, Y., et al. (2021) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2021.106634 · Cited by 15
Plant 3D reconstruction based on LiDAR and multi-view sequence images
Wu, J., Xue, X., Zhang, S., et al. (2018) · International Journal of Precision Agricultural Aviation · doi:10.33440/j.ijpaa.20180101.0007 · Cited by 15
Automatically Detected Feature Positions for LBP Based Face Recognition
Lenc, L., & Král, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44654-6_24 · Cited by 15
Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition
Zhou, B., & Cheng, Y. (2016) · Shock and Vibration · doi:10.1155/2016/1948029 · Cited by 15
Domain independent static video summarization using sparse autoencoders and K-means clustering
Mohan, J., & Nair, P. (. ). M. S. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169906 · Cited by 15
6-DOF probe tracking via skin mapping for freehand 3D ultrasound
Sun, S.-Y., Gilbertson, M. W., & Anthony, B. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556591 · Cited by 15
An indexing scheme and descriptor for 3D object retrieval based on local shape querying
van Blokland, B. I., & Theoharis, T. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.09.001 · Cited by 14
RST-SHELO: sketch-based image retrieval using sketch tokens and square root normalization
Saavedra, J. M. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3076-5 · Cited by 15
Canonical Forms for Non-Rigid 3D Shape Retrieval
Pickup, D., Sun, X., Rosin, P. L., et al. (2015) · ORCA Online Research @Cardiff (Cardiff University) · doi:10.2312/3dor.20151063 · Cited by 15
Ramík, D. M., Madani, K., & Sabourin, C. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.05.014 · Cited by 15
A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes
Khac, C. N., Choi, Y., Park, J. H., et al. (2021) · Sensors · doi:10.3390/s21062133 · Cited by 15
RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote Sensing Image Registration
Zhao, M., An, B., Wu, Y., et al. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2606899 · Cited by 15
Hybrid component-based face recognition
Gumede, A., Viriri, S., & Gwetu, M. V. (2017) · doi:10.1109/ictas.2017.7920665 · Cited by 15
3-Dimensional Reconstruction From Histopathological Sections: A Systematic Review
Kurz, A., Müller, H., Kather, J. N., Schneider, L., Bucher, T.-C., & Brinker, T. J. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102049 · Cited by 16
Saha, S., Xiao, D., Frost, S. M., et al. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0911-z · Cited by 15
Scale and Rotation Invariant Detection of Singular Patterns in Vector Flow Fields
Liu, W., & Ribeiro, E. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14980-1_51 · Cited by 15
Scale-space color blob and ridge detection
Khanina, N. A., Semeikina, E. V., & Yurin, D. V. (2012) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661812010221 · Cited by 15
Extended Keyframe Detection with Stable Tracking for Multiple 3D Object Tracking
Park, Y.-M., Lepetit, V., & Woo, W. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.262 · Cited by 15
Ma, Y., Zhang, Y., Wang, Z., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108046 · Cited by 15
Learning by expansion: Exploiting social media for image classification with few training examples
Wang, S., Liao, W.-S., Hsieh, L.-C., Chen, Y., & Hsu, W. H. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.05.043 · Cited by 15
Adaptive convolutional neural network for aluminum surface defect detection
Wang, Y., Wei, Y.-S., Wu, Z., He, Z.-H., Wang, K., Ding, Z.-S., & Zou, L. (2023) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112262 · Cited by 15
Hoegner, L., & Stilla, U. (2016) · International Conference on Quantitative InfraRed Thermography (QIRT) · doi:10.21611/qirt.2016.042 · Cited by 15
Semantic 3D reconstruction-oriented image dataset for building component segmentation
Wong, M. O., Ying, H., Yin, M., et al. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105558 · Cited by 15
DSLR-Quality Photos on Mobile Devices with Deep Convolutional NetworksPreprint
Ignatov, A., Kobyshev, N., Vanhoey, K., et al. (2017) · arXiv (Cornell University) · doi:10.3929/ethz-b-000203254 · Cited by 15
Improving Object Classification using Pose Information
Penedones, H., Collobert, R., Fleuret, F., & Grangier, D. (2012) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 14
Robust coverless video steganography based on inter-frame keypoint matching
Li, N., Qin, J., Xiang, X., et al. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103653 · Cited by 15
Implicit Neural Image Stitching With Enhanced and Blended Feature Reconstruction
Kim, M., Lee, J., Lee, B., Im, S., & Jin, K. H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00404 · Cited by 15
Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly
Mirzasoleiman, B., Jegelka, S., & Krause, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11529 · Cited by 15
Jiao, C., Ling, D. C., Bian, S. X., et al. (2023) · Cancers · doi:10.3390/cancers15143544 · Cited by 15
Algorithm for Sequence Image Automatic Mosaic Based on SIFT Feature
Li, L., & Geng, N. (2010) · WASE International Conference on Information Engineering · doi:10.1109/icie.2010.55 · Cited by 15
A study on topological descriptors for the analysis of 3D surface texture
Zeppelzauer, M., Zieliński, B., Juda, M., & Seidl, M. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.10.012 · Cited by 15
Supervised training database by using SVD-based method for building recognition
Trinh, H.-H., Kim, D.-N., & Jo, K.-H. (2008) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2008.4694231 · Cited by 15
Wide context learning network for stereo matching
Nguyen, T. P., & Jeon, J. W. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.07.008 · Cited by 15
Qian, C., Zhuang, Y., & Xu, Z. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.12.025 · Cited by 14
SLAM using Visual Scan-Matching with Distinguishable 3D Points
Bertolli, F., Jensfelt, P., & Christensen, H. I. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.281865 · Cited by 15
Wang, C., Sun, D., Yang, J., Li, Z., & Gao, Q. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3324451 · Cited by 15
Keypoint Detection Using Higher Order Laplacian of Gaussian
Cho, Y., Kim, D.-J., Saeed, S., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2965169 · Cited by 15
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
