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 10,801–10,900 of 56,200
Chen, T.-W., Su, Y.-C., Huang, K.-Y., et al. (2011) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2011.2170203 · Cited by 28
Discriminative Ferns Ensemble for Hand Pose Recognition
Krupka, E., Vinnikov, A., Klein, B., Bar-Hillel, A., Freedman, D., & Stachniak, S. (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.2014.469 · Cited by 29
Automatic comic page segmentation based on polygon detection
Li, L., Wang, Y., Tang, Z., et al. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1241-7 · Cited by 28
Endomicroscopic video retrieval using mosaicing and visualwords
André, B., Vercauteren, T. K., Buchner, A. M., et al. (2010) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2010.5490265 · Cited by 28
Gabor Layers Enhance Network Robustness
Pérez, J. C., Alfarra, M., Jeanneret, G., Bibi, A., Thabet, A., Ghanem, B., & Arbeláez, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_26 · Cited by 30
c-Axis orientation determination of α-titanium using Computational Polarized Light Microscopy
Jin, K.-W., & Graef, M. D. (2020) · Materials Characterization · doi:10.1016/j.matchar.2020.110503 · Cited by 28
A Sequential CNN Approach for Foreign Object Detection in Hyperspectral Images
Al-Sarayreh, M., dos Reis, M. M., Yan, W. Q., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29888-3_22 · Cited by 28
A Global Adjustment Method for Photogrammetric Processing of Chang’E-2 Stereo Images
Ren, X., Liu, J., Li, C., et al. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2908813 · Cited by 28
Component-based Attention for Large-scale Trademark Retrieval
Tursun, O., Denman, S., Sivapalan, S., Sridharan, S., Fookes, C., & Mau, S. (2022) · QUT ePrints (Queensland University of Technology) · Cited by 28
A seasonally invariant deep transform for visual terrain-relative navigation
Fragoso, A. T., Lee, C. T., McCoy, A. S., & Chung, S. (2021) · Science Robotics · doi:10.1126/scirobotics.abf3320 · Cited by 28
High-performance visual odometry with two-stage local binocular BA and GPU
Lü, W., Xiang, Z., & Liu, J. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629614 · Cited by 28
Intelligent Multimedia Group of Tsinghua University at TRECVID 2006
Cao, J., Lan, Y., Li, J., et al. (2006) · TRECVID · Cited by 28
Threat Objects Detection in X-ray Images Using an Active Vision Approach
Riffo, V., Flores, S., & Mery, D. (2017) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-017-0419-3 · Cited by 28
Ferrarini, B., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3148908 · Cited by 27
Infrared-visible image registration for augmented reality-based thermographic building diagnostics
Liu, F., & Seipel, S. (2015) · Visualization in Engineering · doi:10.1186/s40327-015-0028-0 · Cited by 28
GenerativeMTD: A deep synthetic data generation framework for small datasets
Sivakumar, J., Ramamurthy, K., Radhakrishnan, M., & Won, D. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110956 · Cited by 28
Guiding attention in controlled real-world environments
Booth, T., Sridharan, S., McNamara, A., Grimm, C. M., & Bailey, R. (2013) · ACM Symposium on Applied Perception · doi:10.1145/2492494.2492508 · Cited by 28
Vision-Aided Inertial Navigation for Small Unmanned Aerial Vehicles in GPS-Denied Environments
Wang, T., Wang, C., Liang, J., et al. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/56660 · Cited by 28
Trademark Image Retrieval Using a Combination of Deep Convolutional Neural Networks
Perez, C. A., Estévez, P. A., Galdames, F. J., et al. (2018) · doi:10.1109/ijcnn.2018.8489045 · Cited by 28
A hybrid feature extraction method for SAR image registration
Norouzi, M., Akbarizadeh, G., & Eftekhar, F. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1312-y · Cited by 28
Learning Discriminative Pattern for Real-Time Car Brand Recognition
Hu, C., Bai, X., Li, Q., Wang, X., Xue, G., & Mei, L. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2441051 · Cited by 28
Fusing Multi-Stream Deep Networks for Video ClassificationPreprint
Wu, Z., Jiang, Y., Wang, X., Ye, H., Xue, X., & Wang, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.06086 · Cited by 35
Personalized Sketch-Based Image Retrieval by Convolutional Neural Network and Deep Transfer Learning
Qi, Q., Huo, Q., Wang, J., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2894351 · Cited by 28
Stereo Cameras Self-Calibration Based on SIFT
Liu, R., Zhang, H., Liu, M., et al. (2009) · International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) · doi:10.1109/icmtma.2009.338 · Cited by 28
Weakly supervised sparse coding with geometric consistency pooling
Cao, L., Ji, R., Gao, Y., Yang, Y., & Tian, Q. (2012) · 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.2012.6248102 · Cited by 28
Writer Identification for Historical Arabic Documents
Fecker, D., Asit, A., Märgner, V., et al. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.526 · Cited by 28
Yang, M., Qiu, G., Huang, J., et al. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.851 · Cited by 28
Leveraging deep visual features for content-based movie recommender systems
Filho, R. J. R., Wehrmann, J., & Barros, R. C. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965908 · Cited by 30
Real-time vehicle detection using Haar-SURF mixed features and gentle AdaBoost classifier
Sun, S., Zhize, X., Wang, X., et al. (2015) · doi:10.1109/ccdc.2015.7162227 · Cited by 28
Object recognition for blind people based on features extraction
Jabnoun, H., Benzarti, F., & Amiri, H. (2014) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas.2014.7043293 · Cited by 28
FAST: Near Real-Time Searchable Data Analytics for the Cloud
Hua, Y., Jiang, H., & Feng, D. (2014) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2014.67 · Cited by 28
Assisted Video Object Labeling By Joint Tracking of Regions and Keypoints
Fauqueur, J., Brostow, G., & Cipolla, R. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409124 · Cited by 30
Perceptual uniform descriptor and ranking on manifold for image retrieval
Liu, S., Wu, J., Feng, L., et al. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.010 · Cited by 28
Image Feature Correspondence Selection: A Comparative Study and a New Contribution
Zhao, C., Cao, Z., Yang, J., Xian, K., & Li, X. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2962678 · Cited by 27
Learning Equivariant Functions with Matrix Valued Kernels
Reisert, M., & Burkhardt, H. (2007) · doi:10.5555/1248659.1248674 · Cited by 32
Li, H., Liu, G., & Ngan, K. N. (2011) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2168814 · Cited by 27
Adaptive Consistency Propagation Method for Graph Clustering
Li, X., Chen, M., & Wang, Q. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2936195 · Cited by 28
Zhang, L., Wang, L., Wang, J., et al. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105069 · Cited by 28
Image Stitching and Rectification for Hand-Held Cameras
Zhuang, B., & Tran, Q.-H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_15 · Cited by 30
Revisiting Self-Similarity: Structural Embedding for Image Retrieval
Lee, S., Lee, S., Seong, H., et al. (2023) · 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/cvpr52729.2023.02242 · Cited by 28
Image and Data Processing for InSight Lander Operations and Science
Abarca, H., Deen, R., Hollins, G., Zamani, P., Maki, J. N., Tinio, A., Pariser, O., Ayoub, F., Toole, N. T., Algermissen, S. S., et al. (2019) · Space Science Reviews · doi:10.1007/s11214-019-0587-9 · Cited by 28
Leveraging Equivariant Features for Absolute Pose Regression
Musallam, M. A., Gaudilliere, V., Del Castillo, M. O., Al Ismaeil, K., & Aouada, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00675 · Cited by 27
Confidence-Based 6D Object Pose Estimation
Huang, W.-L., Hung, C.-Y., & Lin, I. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3092149 · Cited by 28
Feature-Based Color Correction of Multiview Video for Coding and Rendering Enhancement
Fezza, S. A., Larabi, M., & Faraoun, K. M. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2309776 · Cited by 28
Video Summarization using Deep Semantic Features
Otani, M., Nakashima, Y., Rahtu, E., et al. (2016) · Institutional Repositories DataBase (IRDB) · doi:10.48550/arxiv.1609.08758 · Cited by 28
Interactive Process Automation based on lightweight object detection in manufacturing processes
Mangat, A. S., Mangler, J., & Rinderle‐Ma, S. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103482 · Cited by 31
Distinctive Similarity Measure for stereo matching under point ambiguity
Yoon, K., & Kweon, I. S. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2008.02.003 · Cited by 28
A smartphone‐based augmented reality system for university students for learning digital electronics
Avilés‐Cruz, C., & Villegas-Cortéz, J. (2019) · Computer Applications in Engineering Education · doi:10.1002/cae.22102 · Cited by 28
Scale-Wise Convolution for Image Restoration
Fan, Y., Yu, J., Liu, D., & Huang, T. S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6706 · Cited by 36
Zheng, X., Qiu, Y., Zhang, G., Leí, T., & Jiang, P. (2024) · Remote Sensing · doi:10.3390/rs16234374 · Cited by 36
Local structure preserving discriminative projections for RGB-D sensor-based scene classification
Tao, D., Cheng, J. S., Xu, L. H., & Jiang, Y. (2015) · Information Sciences · doi:10.1016/j.ins.2015.03.031 · Cited by 27
Deepdiary: Lifelogging image captioning and summarization
Fan, C., Zhang, Z., & Crandall, D. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.05.008 · Cited by 30
Center and Scale Prediction: A Box-free Approach for Pedestrian and Face DetectionPreprint
Liu, W., Hasan, I., & Liao, S. (2019) · arXiv (Cornell University) · Cited by 28
Medical Image Retrieval Using SIFT Feature
Zhi, L., Zhang, S., Zhao, D., et al. (2009) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2009.5304112 · Cited by 28
Image Feature Point Matching Based on Improved SIFT Algorithm
Zhong, B., & Li, Y. (2019) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc47709.2019.8981329 · Cited by 30
Joint Face Alignment with Non-parametric Shape Models
Smith, B. M., & Zhang, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33712-3_4 · Cited by 28
Extraction of accurate tie points for automated pose estimation of close-range blocks
Barazzetti, L., Fabio Remondino, A., & Scaioni, M. (2010) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · Cited by 28
Saliency-based keypoint selection for fast object detection and matching
Buoncompagni, S., Maio, D., Maltoni, D., & Papi, S. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.04.019 · Cited by 27
Guo, H., Wu, Q., & Wang, Y. (2025) · Remote Sensing · doi:10.3390/rs17111920 · Cited by 29
Multi-scale multi-hierarchy attention convolutional neural network for fetal brain extraction
Sun, L., Shao, W., Zhu, Q., Wang, M., Li, G., & Zhang, D. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109029 · Cited by 28
Reflection Symmetry Detection via Appearance of Structure Descriptor
Atadjanov, I. R., & Lee, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_1 · Cited by 27
Learning group-based dictionaries for discriminative image representation
Hao, L., Mei, K., Zheng, N., Dong, P., Zhou, N., & Jianping Fan, B. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.07.016 · Cited by 26
Multiple close‐range image matching based on a self‐adaptive triangle constraint
Zhu, Q., Zhang, Y., Wu, B., & Zhang, Y. (2010) · The Photogrammetric Record · doi:10.1111/j.1477-9730.2010.00603.x · Cited by 28
Overview of image-based 3D reconstruction technology
Niu, Y., Liu, L., Huang, F., Huang, S., & Chen, S. (2024) · Journal of the European Optical Society Rapid Publications · doi:10.1051/jeos/2024018 · Cited by 33
“In 2004, Lowe fi nally improved the SIFT descriptor on the basis of the 1999 paper and formally proposed the SIFT operator [57], which was widely used in the fi eld of computer vision.”
MethodInterferences in Match Kernels
Murray, N., Jeǵou, H., Perronnin, F., & Zisserman, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2615621 · Cited by 28
Joint audio-visual bi-modal codewords for video event detection
Ye, G., Jhuo, I., Liu, D., Jiang, Y., Lee, D. T., & Chang, S. (2012) · International Conference on Multimedia Retrieval · doi:10.1145/2324796.2324843 · Cited by 28
Progress of Human Action Recognition Research in the Last Ten Years: A Comprehensive Survey
Singh, P. K., Kundu, S., Adhikary, T., Sarkar, R., & Bhattacharjee, D. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09681-9 · Cited by 32
DBF: Dynamic Belief Fusion for Combining Multiple Object Detectors
Lee, H., & Kwon, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2952847 · Cited by 29
Object scale selection of hierarchical image segmentation with deep seeds
Al‐Huda, Z., Peng, B., Yang, Y. T., et al. (2020) · IET Image Processing · doi:10.1049/ipr2.12020 · Cited by 28
A fast recognition framework based on extreme learning machine using hybrid object information
Minhas, R., Mohammed, A. A., & Wu, Q. M. J. (2010) · Neurocomputing · doi:10.1016/j.neucom.2009.11.049 · Cited by 28
Comparing Interactive Retrieval Approaches at the Lifelog Search Challenge 2021
Tran, L.-D., Nguyen, M.-D., Dang‐Nguyen, D., Heller, S., Spieß, F., Lokoč, J., Peška, L., Nguyen, T.-N., Khan, O. S., Duane, A., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3248284 · Cited by 32
How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines
Chelani, K., Kahl, F., & Sattler, T. (2021) · 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/cvpr46437.2021.01541 · Cited by 28
Machine learning framework for image classification
Loussaief, S., & Abdelkrim, A. (2017) · International Conference on Information and Digital Technologies (IDT) · doi:10.1109/dt.2017.8012075 · Cited by 28
MobileSP: An FPGA-Based Real-Time Keypoint Extraction Hardware Accelerator for Mobile VSLAM
Liu, Y., Li, J., Huang, K., Li, X., Qi, X., Chang, L., Long, Y., & Zhou, J. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3190300 · Cited by 29
Hybrid vision-based navigation for mobile robots in mixed indoor/outdoor environments
Cristóforis, P. D., Nitsche, M., Krajník, T., et al. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.010 · Cited by 28
Debnath, R., & Bhowmik, M. K. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103165 · Cited by 29
AUTOMATIC RELATIVE ORIENTATION OF IMAGES 1
Läbe, T., & Förstner, W. (2008) · Cited by 28
A Symbol Spotting Approach Based on the Vector Model and a Visual Vocabulary
Nguyen, T.-O., Tabbone, S., & Boucher, A. (2009) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2009.207 · Cited by 28
Structured Max-Margin Learning for Inter-Related Classifier Training and Multilabel Image Annotation
Jianping Fan, B., Shen, Y., Yang, C., & Zhou, N. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2073476 · Cited by 26
Wang, D., Liu, H., & Cheng, X. (2018) · Sensors · doi:10.3390/s18072243 · Cited by 27
Learnable Pooling Regions for Image ClassificationPreprint
Malinowski, M., & Fritz, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3516 · Cited by 28
A study of the scale-invariant feature transform on a parallel pipeline
Vinukonda, P. (2011) · doi:10.31390/gradschool_theses.2721 · Cited by 28
Shape Augmented Regression for 3D Face Alignment
Gou, C., Wu, Y., Wang, F., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_42 · Cited by 28
Building Local Safety Maps for a Wheelchair Robot using Vision and Lasers
Murarka, A., Modayil, J., & Kuipers, B. J. (2006) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2006.20 · Cited by 28
Xu, J., Zhang, C., Liu, Z., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2941918 · Cited by 28
Unsupervised auxiliary visual words discovery for large-scale image object retrieval
Kuo, Y.-H., Lin, H.-T., Cheng, W.-H., et al. (2011) · doi:10.1109/cvpr.2011.5995639 · Cited by 28
Polarimetric Multi-View Inverse Rendering
Zhao, J., Monno, Y., & Okutomi, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3232211 · Cited by 28
Building modeling from noisy photogrammetric point clouds
Xiong, B., Elberink, S. O., & Vosselman, G. (2014) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-3-197-2014 · Cited by 28
Fan, C. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2841 · Cited by 29
Alfarisi, O., Zhang, H., Raza, A., et al. (2019) · SPE Reservoir Characterisation and Simulation Conference and Exhibition · doi:10.2118/196657-ms · Cited by 28
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
Ionescu, R. T., & Butnaru, A. M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1033 · Cited by 28
Korman, S., & Litman, R. (2018) · 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.2018.00700 · Cited by 29
Sparsely Encoded Local Descriptor for face recognition
Cui, Z., Shan, S., Chen, X., et al. (2011) · doi:10.1109/fg.2011.5771389 · Cited by 28
Tracking‐DOSeqSLAM: A dynamic sequence‐based visual place recognition paradigm
Tsintotas, K. A., Bampis, L., Gasteratos, A. C., & FIET (2021) · IET Computer Vision · doi:10.1049/cvi2.12041 · Cited by 28
Kim, W. H., Racine, A. M., Adluru, N., Hwang, S. J., Blennow, K., Zetterberg, H., Carlsson, C. M., Asthana, S., Koscik, R. L., Johnson, S. C., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.10.024 · Cited by 28
Image retrieval based on image-to-class similarity
Chen, J., Wang, Y., Luo, L., et al. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.01.017 · Cited by 28
Real-time keypoints matching: application to visual servoing
Trân, T. T. H., & Marchand, É. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.364059 · Cited by 27
Multiple Kernel Point Set Registration
Nguyen, T. M., & Wu, Q. M. J. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2511063 · Cited by 28
Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters
Azzopardi, G., & Petkov, N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23672-3_55 · Cited by 28
Optimal Visual Servoing for differentially flat underactuated systems
Sheckells, M., Garimella, G., & Kobilarov, M. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759815 · Cited by 28
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
