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 43,601–43,700 of 56,200
Improved Multi-Source Color Transfer Algorithm by Saliency Filters
Wei, P., & Li, H. (2018) · International Conference on Intelligent Computation Technology and Automation · doi:10.1109/icicta.2018.00033 · Cited by 1
A Deep Learning Pipeline for Adaptive Bayesian-Optimized Keyframe Extraction in Video Summarization
Zarif, S., Morad, E., Amin, K. M., et al. (2026) · IEEE Access · doi:10.1109/access.2026.3660775 · Cited by 1
Video Content Extraction: Scene Segmentation, Linking And Attention Detection
Zhai, Y. (2006) · Journal of International Crisis and Risk Communication Research · Cited by 1
Saturation High Light Removal of Pressure Plate Image Based on Feature Matching
Li, P., Chen, Y., Li, H., et al. (2021) · Asia Conference on Power and Electrical Engineering (ACPEE) · doi:10.1109/acpee51499.2021.9436869 · Cited by 1
Mondal, S., Pal, A. K., & Islam, S. H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4791159 · Cited by 1
The Research of Intelligent Vision System for Smart Substation
Li, H. (2013) · doi:10.1109/isdea.2012.362 · Cited by 1
Revisiting multi-scale feature representation and fusion for UAV-based road distress detection
Wang, P., Liu, J., Chen, H., et al. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133331 · Cited by 1
Kalman-based continuous pose estimation network for spacecraft
Zhou, R., Qi, N., Jia, S., Du, D., Mo, L., Yang, D., & Liu, Y. (2024) · Aerospace Frontiers Conference (AFC) · doi:10.1117/12.3032451 · Cited by 1
An estimation of the fundamental matrix using hybrid statistics
Okutani, R., & Kuroki, Y. (2013) · Visual Communications and Image Processing · doi:10.1109/vcip.2013.6706341 · Cited by 1
Detection of Genuine and Posed Facial Expressions of Emotion: A ReviewPreprint
Jia, S., Wang, S., Hu, C., Webster, P. J., & Li, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.11353 · Cited by 1
Reconstructing a 3D Room from a Kinect Carrying UAV
Mangina, E., Gannon, C., & O’Keeffe, E. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9181-1_7 · Cited by 1
Jung, C., Kim, N., Lee, K., et al. (2024) · 대한원격탐사학회지 · doi:10.7780/kjrs.2024.40.6.3.5 · Cited by 1
Yang, S. Y., Zhu, S., Zhen-ju, L., et al. (2016) · Journal of Earth Science · doi:10.1007/s12583-015-0605-0 · Cited by 1
Li, J., Zhang, B., & Zhang, D. D. (2022) · Information Fusion · doi:10.1007/978-981-16-8976-5_1 · Cited by 1
SLTFILL: Spatial and Light Transformer for Multi-Reference Image Inpainting
Fan, Y., Zhou, Y., Yang, Z., & Tong, Z. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897176 · Cited by 1
Spatiotemporal Video Synchronisation by Visual Matching.
Bailer, W., Thaler, M., & Thallinger, G. (2015) · Cited by 1
Face Recognition Using the Novel Fuzzy-GIST Mechanism
Vinay, A., a, B. G., Shekhar, V. S., et al. (2017) · Lecture notes in networks and systems · doi:10.1007/978-981-10-5146-3_36 · Cited by 1
Ear feature region detection based on a combined image segmentation algorithm-KRM
Jiang, J., Zhang, H., Zhang, Q., et al. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2036893 · Cited by 1
PBDE: an effective post-processing method based on box density for object detection
Li, Z., Jia, B., He, Y., & Xie, L. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02540-x · Cited by 1
2D3D-DescNet: Jointly Learning 2D and 3D Local Feature Descriptors for Cross-Dimensional Matching
Chen, S., Su, Y., Lai, B., et al. (2024) · Remote Sensing · doi:10.3390/rs16132493 · Cited by 1
“On the one hand, the description strategies of 2D handcrafted feature descriptors are usually classified into gradient statistics (SIFT [5], SURF [6]), local binary pattern statistics (LBP [26], RLBP [27]), local intensity comparison (BRIEF [28], ORB [29]), and…”
Hardware/Software Co-design of Embedded Real-Time KD-Tree Based Feature Matching Systems
Shoaib, S., Hafiz, R., & Shafique, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14364-4_90 · Cited by 1
Real-time affine invariant patch matching using DCT descriptor and affine space quantization
Chen, X., Ye, F., Zhu, F., et al. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116291 · Cited by 1
Investigation of ant cuticle dataset using image texture analysis
Gardner, N., Hellenbrand, J. P., Phan, A., Zhu, H., Long, Z., Wang, M., Penick, C. A., & Hung, C. (2022) · Applied Computing and Intelligence · doi:10.3934/aci.2022008 · Cited by 1
Vagdargi, P., Uneri, A., Jones, C. K., et al. (2022) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.2613231 · Cited by 1
Souza, M. R. E. (2018) · doi:10.47749/t/unicamp.2018.996066 · Cited by 1
Identifying and learning visual attributes for object recognition
Wan, K.-W., & Roy, S. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5653980 · Cited by 1
Chimpanzee identification using global and local features
Loos, A. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638074 · Cited by 1
Scalable Indoor Novel-View Synthesis Using Drone-Captured 360 Imagery with 3D Gaussian Splatting
Chen, Y., Zhang, C., Wang, J., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91989-3_4 · Cited by 1
Automated Detection of Radiopaque Markers in Chest and Abdomen Radiography
Nose, H., & Shiraishi, J. (2018) · Japanese Journal of Radiological Technology · doi:10.6009/jjrt.2018_jsrt_74.4.344 · Cited by 1
Kim, S., & Shim, M.-S. (2015) · The Scientific World JOURNAL · doi:10.1155/2015/465290 · Cited by 1
Quantifying Deformation in Aegean Sealing Practices
Bogacz, B., Finlayson, S., Panagiotopoulos, D., & Mara, H. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-41590-7_25 · Cited by 2
TAU-Indoors Dataset for Visual and LiDAR Place Recognition
Dag, A., Alijani, F., Peltomäki, J., Suomela, L., Rahtu, E., Edelman, H., & Kämäräinen, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-31438-4_22 · Cited by 1
Morris, T., Morellas, V., Papanikolopoulos, N., et al. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.07.351 · Cited by 1
Bayesian–Geometric Fusion: A Probabilistic Framework for Robust Line Feature Matching
Zhang, C., Ge, Y., & Gu, S. (2025) · Electronics · doi:10.3390/electronics14193783 · Cited by 1
A Real-time Trademark Detection Method
Leng, Y., & Fan, Q. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1693/1/012106 · Cited by 1
Machine Learning Applications in Computer Vision
Harandi, M. T., Taheri, J., & Lovell, B. C. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3994-2.ch045 · Cited by 1
Invariant Methods in Computer Vision
Lohit, S., Turaga, P. K., & Veeraraghavan, A. (2020) · Springer eBooks · doi:10.1007/978-3-030-03243-2_826-1 · Cited by 1
Normalized videosnapping: A non-linear video synchronization approach
Tripathi, A., Changmai, B., Habib, S., et al. (2017) · International Conference on Contemporary Computing · doi:10.1109/ic3.2017.8284325 · Cited by 1
Object-based Color Image Retrieval Using Concentric Circular Invariant Features
XIE, Y., Setia, L., & Burkhardt, H. (2007) · Cited by 1
Co-segmentation of inter-subject brain magnetic resonance images
Jang, J., Kim, H. W., & Kim, Y. S. (2014) · International Conference on Ubiquitous Robots and Ambient Intelligence · doi:10.1109/urai.2014.7057400 · Cited by 1
Video‐Based Parking Management
Sidla, O., & Lipetski, Y. (2017) · Wiley eBooks · doi:10.1002/9781118971666.ch8 · Cited by 1
Han, J. Y., Lee, Y.-B., & Lee, S. (2013) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2013.531 · Cited by 1
Simulation of Full Tailings Flocculation Behavior in the Feedwell of the DCT
Ruan, Z. (2025) · Springer eBooks · doi:10.1007/978-981-96-1100-3_4 · Cited by 1
Multi-level fine-grained center calibration network for unsupervised person re-identification
Che, H., Zhao, J., & Li, Y. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01729-1 · Cited by 1
Natural Scene and Object Perception Based on Statistical Image Features: Psychophysics and EEG
Orima, T., Kurosawa, F., Sekimoto, T., & Motoyoshi, I. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0859-25.2025 · Cited by 2
Robust Image Matching Algorithm Using SIFT on Multiple Layered Strategies
Chen, Y., Shang, L., & Hu, E. J. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/452604 · Cited by 1
Algorithms for Recognition in Marker-Based and Spatial Augmented Reality
Artikova, M., & Sayfiev, E. (2025) · doi:10.1109/apeie66761.2025.11289469 · Cited by 1
“However, their dependence on predefined patterns limits scalability in dynamic environments [13].”
Text-Assisted Multimodal Adaptive Registration and Fusion Classification Network
He, Y., Xi, B., Li, G., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3649754 · Cited by 1
A Novel Explainable Deep Learning Model with Class Specific Features
Kuttichira, D. P., Azam, B., Verma, B., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25825-1_5 · Cited by 1
Jiao, R., Wang, Q., Lai, T., et al. (2021) · Remote Sensing · doi:10.3390/rs13224637 · Cited by 1
Evaluation of vocabulary trees for localization in robot applications
Hwang, S., Park, C., Choi, Y., Yoo, D., & Kweon, I. S. (2013) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2013.6704138 · Cited by 1
Moradi, K., Haque, E., Kaur, J., et al. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv61041.2025.00105 · Cited by 1
“Feature extraction is performed on masked image frames using the Scale-Invariant Feature Transform (SIFT) [15] to identify suitable interest points in the image that are appear invariant to scale and orientation.”
On the Influence of Texture Strength and Camera Signal on Micro- Texture Surface Classification
Schuiki, J., Kauba, C., Hofbauer, H., & Uhl, A. (2024) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf62628.2024.10593842 · Cited by 1
“Two distinct feature extraction techniques are employed in this study: (i) Dense SIFT [19] (SIFT descriptors applied on a fixed-spaced grid), which are subsequently encoded using improved Fisher vector encoding [20] in a similar way as originally proposed in [21], and (ii) a local binary pattern…”
MethodObjects from Animacy: Joint Discovery in Shape and Haar Feature Space
Nandi, S., Guha, P., & Venkatesh, K. S. (2008) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1109/icvgip.2008.78 · Cited by 1
Deep Learning-Based Improved Object Recognition in Warehouses
Fouzia, S., Bell, M., & Klette, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75786-5_29 · Cited by 1
Lien, C. (2011) · BiblioBoard Library Catalog (Open Research Library) · doi:10.5772/intechopen.84007 · Cited by 1
Konno, A., MAEDA, Y., TERAGUCHI, T., et al. (2017) · Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) · doi:10.2208/jscejcei.73.i_279 · Cited by 1
Gupta, S., Dinesh, D. A., & Thenkanidiyoor, V. (2018) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3152494.3167985 · Cited by 1
Facial Recognition-Based Emotion Detection for Enhanced Voter Experience
Gourav, S. N., Arora, K., Khan, S., et al. (2023) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon59197.2023.10434600 · Cited by 1
Video error concealment using Speeded Up Robust Features and affine transformation
Ranjan, A., Midya, A., Chakraborty, J., et al. (2014) · IEEE Students’ Technology Symposium · doi:10.1109/techsym.2014.6807917 · Cited by 1
Peng, S., & Li, Z. (2024) · doi:10.1117/12.3005340 · Cited by 1
Murata, M., Nagano, H., Hiramatsu, K., et al. (2016) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016edp7066 · Cited by 1
Automatic spatial accuracy estimation for correlation-based image registration
DelMarco, S. P., Webb, H. F., & Tom, V. T. (2019) · Mobile Multimedia/Image Processing, Security, and Applications · doi:10.1117/12.2518183 · Cited by 1
Liu, H., & Xiao, G. (2020) · Journal of Electronic Imaging · doi:10.1117/1.jei.29.1.013015 · Cited by 1
LDAM: line descriptors augmented by attention mechanism
Cao, X., Huang, Y., Huang, Y., Li, Y., & Cai, S. (2022) · International Conference on Digital Image Processing · doi:10.1117/12.2644245 · Cited by 1
Das, D., Kobayashi, Y., & Kuno, Y. (2011) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e94.d.1467 · Cited by 1
Alshammari, K. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160394 · Cited by 1
Accelerating Image Feature Comparisons using CUDA on Commodity Hardware
Apon, A., Warn, S., Emeneker, W., et al. (2010) · TigerPrints (Clemson University) · Cited by 1
Fast tracking of feature points in full-view image
Qin, X., & Li, S. (2011) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2011.5986232 · Cited by 1
Multi-modality fusion : registering photographs, videos, and LIDAR range scans
Morago, B. (2016) · doi:10.32469/10355/56548 · Cited by 1
STED-Net: Self-taught encoder-decoder network for unsupervised feature representation
Ду, С., & Ikenaga, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09734-4 · Cited by 1
Copy-move forgery detection using improved SIFT
Zhou, Y., & Xie, Y. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2244287 · Cited by 1
Rajendran, M., Stephan, P., Stephan, T., et al. (2022) · Sustainability · doi:10.3390/su14159063 · Cited by 1
Optimal KAZE and AKAZE Features for Facial Similarity Matching
Vinay, A., Vasu, K. A., Lodha, P. Y., et al. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-37940-6_14 · Cited by 1
Ek, C. H., & Kragić, D. (2016) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-29363-9_7 · Cited by 1
Humanoid robot action using multi-mode fusion based on frame expert system
Juang, L. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19058-2 · Cited by 1
Coarse-grained reconfigurable stream processor for distributed smart cameras
Chan, W.-K., Tseng, Y.-H., Lin, Y., et al. (2014) · SiPS ... design and implementation - IEEE Workshop on Signal Processing Systems · doi:10.1109/sips.2014.6986097 · Cited by 1
chen, X., Zhang, S., Dong, F., et al. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae467d · Cited by 1
Observation Planning for Object Search by a Mobile Robot with Uncertain Recognition
Boussard, M., & Miura, J. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-33932-5_10 · Cited by 1
Improving video concept detection through label space partitioning
Niaz, U., & Mérialdo, B. (2014) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890258 · Cited by 1
Low-dimension local descriptor for dense stereo matching and scene reconstruction
Zhang, C., Xue, B., & Zhou, F. (2017) · Optical Engineering · doi:10.1117/1.oe.56.8.083105 · Cited by 1
Object-based stereo matching using adjustable-cross for depth estimation
Wang, L.-H., Tsai, K.-L., & Wu, C.-B. (2015) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-tw.2015.7216851 · Cited by 1
New Compact 3-Dimensional Shape Descriptor for a Depth Camera in Indoor Environments
Choi, H., & Kim, E. (2017) · Sensors · doi:10.3390/s17040876 · Cited by 1
A SIFT-Kalman information fusion tracking algorithm based on PCA
Zhou, K., Fan, R., & Li, W. (2010) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2010.5554734 · Cited by 1
Fast Global Motion Estimation on Android-based Software-defined Satellite
Lin, Y., Hu, J., Wu, F., et al. (2019) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis46139.2019.8940195 · Cited by 1
Scherer, R. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-12195-2_3 · Cited by 1
RANSAC Based Data Association for Underwater Visual SLAM
Burguera, A., González-Cid, Y., & Oliver, G. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-03413-3_1 · Cited by 1
Liu, C., Liu, J., Zhang, Y., & Huang, L. (2021) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00021 · Cited by 1
Non-Redundant Gradient Semantic Local Binary Patterns for pedestrian detection
Xu, J., Jiang, N., & Goto, S. (2014) · Cited by 1
A New Location Method for LBS Based on Image
Xinchao, X., Aigong, X., Lijuan, S. U., et al. (2011) · Information Technology Journal · doi:10.3923/itj.2011.2175.2181 · Cited by 1
A Fast Video Copy Detection Approach by Dynamic Programming
Zhou, Y., Chi, M., & Jin, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15702-8_51 · Cited by 1
Rotation-equivariant correspondence matching based on a dual-activation mixer
Su, S., Dang, R., Fan, R., et al. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127053 · Cited by 1
Basu, S., Li, C., & Cohen, F. S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12284-6 · Cited by 1
RST Invariant Image Watermarking Based on Invariant Contents
Li, X., Guo, B., & Chen, L. (2010) · International Conference on Genetic and Evolutionary Computing · doi:10.1109/icgec.2010.172 · Cited by 1
An Augmented Approach for Recognising Sri Lankan Currency Notes
Kogilawanee, B., & Ramanan, A. (2018) · IEEE International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs.2018.8913332 · Cited by 1
3D shape retrieval based on Multi-scale Integral Orientations | NIST
Wagan, A. I., Bres, S., Emptoz, H., et al. (2010) · Cited by 1
Tracking visual objects using pyramidal rotation invariant features
Paheding, S., Essa, A. E., Krieger, E. W., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2228914 · Cited by 1
Positioning for High-Speed Maneuverable Platform Based on Wrapped InSAR Interferogram
Jiang, Y., Sun, B. S., & Li, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3223162 · Cited by 1
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
