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 44,901–45,000 of 56,200
VETE: improving visual embeddings through text descriptions for eCommerce search engines
Martínez, G., Saavedra, J. M., & Murrugarra-Llerena, N. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14595-8 · Cited by 1
Image Clustering under Domain Shift
Gopalan, R. (2017) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2017.16 · Cited by 1
Affine estimation via region expansion
Farhan, E., & Hagege, R. R. (2014) · IEEE Workshop on Statistical Signal Processing (SSP) · doi:10.1109/ssp.2014.6884580 · Cited by 1
A Diving Posture Recognition Method Based on Multiple Features Fusion
Wang, J., Xiao, G., & Qiu, K. (2011) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-25658-5_14 · Cited by 1
Viewing stomata in action: Autonomous in planta imaging of individual stomatal movementPreprint
van den Berg, T., Sanders, R. G. P., Kaiser, E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.13.584774 · Cited by 1
Violence detection in video game metadata using ConvLSTM
Correia, H. A., & Brito, J. H. (2021) · IEEE International Conference on Serious Games and Applications for Health (SeGAH) · doi:10.1109/segah52098.2021.9551853 · Cited by 1
Minimizing the Number of Keypoint Matching Queries for Object Retrieval
Niedermayer, J., & Kröger, P. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.48 · Cited by 1
Implementataion of Global Localization and Kidnap Recovery for Mobile Robot on Feature Map
Lee, J., Lee, K., Ahn, S., et al. (2007) · The Journal of Korea Robotics Society · Cited by 1
A Sub-Second Method for SAR Image Registration Based on Hierarchical Episodic Control
Zhou, R. F., Wang, G., Xu, H., et al. (2023) · Remote Sensing · doi:10.3390/rs15204941 · Cited by 1
Enhancing Marine Debris Acoustic Monitoring by Optical Flow-Based Motion Vector Analysis
Zhou, X., & Mizuno, K. (2025) · IEEE Underwater Technology (UT) · doi:10.1109/ut61067.2025.10947410 · Cited by 1
“Specifically, several commonly used feature detection operators, including SIFT [12], FAST [13], ORB [14], and AKAZE [15], [16], were applied to extract local feature points around the targets from optical and sonar image sequences.”
Create visual word pairs dynamically based on sparse codes of SIFT features for image categorization
Wu, L., Huang, Y., Sun, W., & Ke, J. (2012) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2012.6234525 · Cited by 1
Multi-view Image Generation from Stereoscopic Image Features and the Occlusion Region Extraction
Lee, W.-R., Ko, M.-S., Um, G., et al. (2012) · Journal of Broadcast Engineering · doi:10.5909/jbe.2012.17.5.838 · Cited by 1
Precise Aerial Image Matching based on Deep Homography EstimationPreprint
Oh, M., Lee, Y.-J., & Lee, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08768 · Cited by 2
Prototype-Based Research on Immersive Virtual Reality and on Self-Replicating Robots
Dornberger, R., Korkut, S., Lutz, J., Berga, J., & Jäger, J. (2018) · Studies in systems, decision and control · doi:10.1007/978-3-319-74322-6_17 · Cited by 1
Nawawi, S. W., Ahmed, A. A. A. M., Biswas, H. A., et al. (2022) · ELEKTRIKA- Journal of Electrical Engineering · doi:10.11113/elektrika.v21n2.384 · Cited by 1
Get More With Less: Near Real-Time Image Clustering on Mobile PhonesPreprint
Ortiz, J., Huang, C.-C., & Chakraborty, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.02972 · Cited by 1
Leaning Local Features for Visual Localization Using Images Generated by Diffusion-Based Model
Katayose, G., Shimizu, I., & Sattler, T. (2024) · International Conference on Computer Graphics and Image Processing (CGIP) · doi:10.1109/cgip62525.2024.00034 · Cited by 1
An Improved Image Retrieval by Using Texture Color Descriptor with Novel Local Textural Patterns
Johari, P. K., & Kumar, R. K. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110932 · Cited by 1
Varshney, K. R. (2005) · Cited by 1
Active Colorization for Cartoon Line Drawings
Shuyu, C., Jiaqi, Z., Lin, G., et al. (2020) · doi:10.26686/wgtn.12585551 · Cited by 1
An Analysis of Dependency of Prior Probability for Codebook-Based Image Representation
Shinomiya, Y., & Hoshino, Y. (2016) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scis-isis.2016.0034 · Cited by 1
Salient Region Filtering for Background Subtraction
Rodhetbhai, W., & Lewis, P. H. (2007) · Springer eBooks · doi:10.1007/978-3-540-76414-4_13 · Cited by 1
OSPC: Online Sequential Photometric Calibration
Haidar, J., Khalil, D., & Asmar, D. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.03.005 · Cited by 1
Lisbon Landmark Lenslet Light Field Dataset
Teixeira, J. A., Brites, C., Pereira, F. M. B., et al. (2017) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3095713.3095723 · Cited by 1
UCF @ TRECVID 2009: High-level Feature Extraction
Hochreiter, J. E., Barreiros, S., Imran, N., et al. (2009) · Cited by 1
Semantic Correspondence in the Wild
Pemasiri, A., Thanh, K. N., Sridharan, S., et al. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00126 · Cited by 1
Disparity-augmented trajectories for human activity recognition
Habashi, P., Boufama, B., & Ahmad, I. S. (2021) · Evolutionary Intelligence · doi:10.1007/s12065-020-00553-y · Cited by 1
An Improved ORB Image Matching Algorithm Based on Compressed Sensing
Wang, Y., & Ge, S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-14680-1_39 · Cited by 1
LightSGM: Local feature matching with lightweight seeded
Feng, S., Qian, H., Wang, H., & Wang, W. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102095 · Cited by 1
“Traditionally, matching points are identified through local feature extraction and nearest neighbor matching, with subsequent refinement steps like mutual nearest neighbor checks or ratio tests (Lowe, 2004) to remove outliers.”
A system of abnormal behaviour detection in aerial surveillance
Walha, A., Wali, A., & Alimi, A. M. (2013) · International Conference on Information Assurance and Security (IAS) · doi:10.1109/isias.2013.6947741 · Cited by 1
Grumpe, A., Schröer, C., Kauffmann, S., et al. (2016) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xli-b4-565-2016 · Cited by 1
The Photogrammetric Reconstruction Task Solved by Bundle Adjustment Directly with Pixel Bundles
Wrobel, B. P. (2017) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-017-0037-9 · Cited by 1
Road Segment Re-Identification in Dashcam Videos
Tsukamoto, Y., Amano, T., Hiromori, A., et al. (2021) · International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob52687.2021.9606254 · Cited by 1
Xu, Y., Liu, N., Yin, T., et al. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3108045 · Cited by 1
Towards multi-modality invariance: A study in visual representation
Xie, L., & Tian, Q. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-814601-9.00029-8 · Cited by 1
Indoor Visual Odometry Algorithm Based on Adaptive Feature Fusion
Mu, Q., Wang, Y., Guo, S., et al. (2022) · International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI) · doi:10.1109/ahpcai57455.2022.10087841 · Cited by 1
Real-Time Circuit Breaker State Detection and Identification Method Based on Faster R-CNN
Ma, Y., Guo, R., Liu, M., et al. (2018) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2018.8812404 · Cited by 1
SIFT-Based Non-blind Watermarking Robust to Non-linear Geometrical Distortions
Yamasaki, T., & Aizawa, K. (2013) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e96.d.1368 · Cited by 1
VGG16-based Feature Fusion For Image Kyepoint Description
Norouzi, J., Liaghat, A., Helfroush, M. S., & Danyali, H. (2023) · doi:10.1109/icee59167.2023.10334668 · Cited by 1
Towards Real-Time Advancement of Underwater Visual Quality with GANPreprint
Chen, X., Yu, J., Kong, S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00736 · Cited by 1
Planar Object Tracking in the Wild: A BenchmarkPreprint
Liang, P., Wu, Y., Lu, H., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07938 · Cited by 1
Stable Camera Motion Estimation Using Convex ProgrammingPreprint
Özyeşil, O., Singer, A., & Basri, R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.5047 · Cited by 1
Śledzenie obszarów zainteresowania w sekwencjach obrazów bronchoskopowych za pomocą metody SIFT
Pawlik, P., Bubliński, Z., & Duplaga, M. (2009) · Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie · Cited by 1
Adaptive Zoom Distance Measuring System of Camera Based on the Ranging of Binocular Vision
Zhang, Z., Qian, W. N., Pan, L., et al. (2012) · Modern Applied Science · doi:10.5539/mas.v6n5p43 · Cited by 1
UThe Vision Research Lab of UCSB at TRECVID 2007.
Gelasca, E. D., Joshi, S., Kleban, J. T., Mangiat, S., Manjunath, B. S., Moxley, E., Sarkar, A., & Xu, J. (2007) · TRECVID · Cited by 1
Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST
Tönnes, C., & Zöllner, F. G. (2023) · Applied Sciences · doi:10.3390/app13169179 · Cited by 1
Image Matching Using Enhancement Offsets With Adaptive Parameter Selection Via Histogram Analysis
Psaila, J., Hong-Phuoc, T., & Guan, L. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506798 · Cited by 1
Byun, S.-W., & Lee, S.-P. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.08.028 · Cited by 1
Evaluating Data Collection Methods for Vision-Based Learning in Humanoid Robot Soccer
Hong, E. R., Ahn, J. S., Lee, S., Alvarez, A. F., Wang, S., & Hong, D. W. (2025) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur65550.2025.11078062 · Cited by 1
“Traditionally, the vision task has been achieved using an amalgamation of classical & deep learning based approaching, including color segmentation, pattern detection [2] [3], feature extraction [9] [10] [11], and matching [12].”
SIFT features of fusion region information entropy in Bag-of-Words
Li, W., Liu, R. Z. Z., & Huang, Y. (2012) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2012.6234205 · Cited by 1
Unmanned Aerial Vehicle Based Structure from Motion Biomass Inventory Estimates
Bedell, E. (2016) · doi:10.15760/etd.3326 · Cited by 1
Robust Part-Based Correlation Tracking
Liu, X., & Zhou, Y. F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46672-9_71 · Cited by 1
An Incremental Structured Part Model for Image Classification
Zhang, H., Bai, X. P., Cheng, J., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34166-3_53 · Cited by 1
Distributed 3D Object Recognition System Using Smartphones
Ibrahim, M., Elgendy, O. A., & Farouk, M. (2015) · doi:10.15849/icit.2015.0015 · Cited by 1
Random Forest-Assisted GP for Feature Learning
Bi, Y., Xue, B., & Zhang, M. (2021) · Adaptation, learning, and optimization · doi:10.1007/978-3-030-65927-1_9 · Cited by 1
Multimodal Object Categorization Based on Hierarchical Dirichlet Process by a Robot
Nakamura, T., Araki, T., Nagai, T., et al. (2013) · Transactions of the Society of Instrument and Control Engineers · doi:10.9746/sicetr.49.469 · Cited by 1
Narayanankutty, H., & Srinivasan, C. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-16660-1_2 · Cited by 1
Automatic Handgun Detection in X-ray Images using Bag of Words Model with Selective SearchPreprint
Piñol, D. C., & Reyes, E. J. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.01322 · Cited by 1
An Effective Image Copy-Move Forgery Detection Using Entropy Information
Jiang, L., & Lu, Z. (2024) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc61627.2024.10837115 · Cited by 1
Indian Sign Language Recognition Using Scale-Invariant Feature Transform by Depth Sensor
Gangrade, J., Gangrade, S., Mishra, A., et al. (2023) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla58983.2023.10346613 · Cited by 1
Ultra-low power signal processor for biomedical applications
Zeinolabedin, S. M. A. (2017) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/70122 · Cited by 1
Gaussian Splatting for Automated Video-to-3D Building Energy ModelingPreprint
Chowdhury, S., Adesanya, M. A., Mohammed, N. A. C., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5149707 · Cited by 1
Generalized visual concept detection
Saracoğlu, A., Tekin, M., Esen, E., et al. (2010) · IEEE Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2010.5650360 · Cited by 1
Magnor, M. A., Grau, O., Sorkine‐Hornung, O., & Theobalt, C. (2015) · A K Peters/CRC Press eBooks · doi:10.1201/b18154-30 · Cited by 1
An Android Based Medication Reminder System: A Concept Analysis Approach
Hashemi, R. R., Sears, L., & Bahrami, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22688-5_25 · Cited by 1
A Scale and Rotational Invariant Key-point Detector based on Sparse CodingPreprint
Hong-Phuoc, T., & Guan, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06264 · Cited by 1
Gaussian pyramid extraction with a CMOS vision sensor
Suarez, M., Brea, V. M., Fernández‐Berni, J., et al. (2014) · International Workshop on Cellular Nanoscale Networks and their Applications (CNNA) · doi:10.1109/cnna.2014.6888606 · Cited by 1
Laser-to-Vehicle Extrinsic Calibration in Low-Observability Scenarios for Subsea Mapping
Hitchcox, T., & Forbes, J. R. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3367270 · Cited by 1
“To obtain a set of inlier matches between the different scans, 3D SIFT keypoints [18] r qwa ∈ R 3 are detected and matched using the TEASER++ coarse alignment algorithm [19] on the basis of FPFH feature correspondences [20].”
MethodUAV Cross-Modal Image Registration: Large-Scale Dataset and Transformer-Based Approach
Xiao, Y. H., Liu, F., Zhu, Y., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-1417-9_16 · Cited by 1
A large-region high-resolution satellite dataset of Beijing-Tianjin-Hebei Urban Agglomeration
Long, T., He, G., Jiao, W., et al. (2020) · China Scientific Data · doi:10.11922/csdata.2020.0015.zh · Cited by 1
Brain Inspired Keypoint Matching for 3D Scene Reconstruction
Zaman, A., Yangyu, F., Ayub, M. S., et al. (2022) · International Conference on Virtual Reality · doi:10.1109/icvr55215.2022.9847807 · Cited by 1
Predicting FGR at Early Pregnancy via a HDC-based Encoding Method
Zhou, T., Huang, X., Zhang, Y., Tang, P., & Chen, M. (2025) · Wireless and Optical Communications Conference · doi:10.1109/wocc63563.2025.11082243 · Cited by 1
Novel Methods for Forensic Multimedia Data Analysis: Part II
Perner, P. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.92548 · Cited by 1
Image Stitching of the Computed Radiology images Using a Pixel-Based Approach
Sedehzadeh, M., & Farokhi, F. (2013) · International journal of smart electrical engineering · Cited by 1
Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide ImagesPreprint
Deng, R., Yang, H., Jha, A., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06038 · Cited by 1
Enabling Continuous Object Recognition in Mobile Augmented Reality
Su, X. J., Jiang, A., Cao, J., et al. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490100.3516459 · Cited by 1
A2-GNN: Angle-Annular GNN for Visual Descriptor-Free Camera Relocalization
Zhang, Y., Wang, S., & Kannala, J. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00038 · Cited by 3
“Our model is trained on the MegaDepth [37] using the SIFT [39] detector and evaluated on the indoor 7Scenes dataset [60] with the SIFT [39] outdoor Cambridge dataset [32] using the SuperPoint [14] detector.”
Deep-Learning-Based Precision Visual Tracking
Peng, X., Xu, Z., Ji, X., et al. (2023) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001423520080 · Cited by 1
Tanahashi, M., Lin, M.-C., & Lin, C. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70085 · Cited by 1
An Improved Method for Interest Point Detection in Human Activity Video
Hu, Z., Zhang, H., Yang, Y., & Yang, C. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1237/2/022089 · Cited by 1
Performance Analysis of Feature Extraction Methods towards Underwater vSLAM
Wu, D., Wang, Y., Hou, D., Hao, L., Zhang, L., & Wang, D. (2022) · International Conference on Control and Automation · doi:10.1109/icca54724.2022.9831964 · Cited by 1
Face Match for Family Reunification
Borovikov, E., Vajda, S., & Gill, M. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5204-8.ch028 · Cited by 1
Sparse robust multiview feature selection via adaptive-weighting strategy
Wang, Z., Zhong, J., Chen, Y., & Zhong, P. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01453-y · Cited by 1
A real-time middle wavelength infrared multi-target detection and tracking algorithm based on LoG
Song, D., Bao-jun, Z., & Linbo, T. (2011) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi.2011.6037962 · Cited by 1
An accurate, automatic method for markerless alignment of electron tomographic images
Chu, Q., Zhang, F., Zhang, K., et al. (2010) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2010.5706597 · Cited by 1
Depth map estimation using focus and aperture bracketing from a modified Canon 600D camera
Domínguez, À. P. (2017) · Cited by 1
Voting-based Object Recognition with 3-Dimensional Confidence Map from Multiple Viewpoints
Qiu, Y., Satoh, Y., Suzuki, R., et al. (2017) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.83.1125 · Cited by 1
Eigenvector Orientation Corrected LeNet for Digit Recognition
Swetha, V. C., Mishra, D., & Gorthi, R. K. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-32-9088-4_27 · Cited by 1
GEVO: Memory-Efficient Monocular Visual Odometry Using Gaussians
Gao, D., Li, P. Z. X., Sze, V., et al. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3534683 · Cited by 1
SIFT Image Stitching Technology Based on Music Score Scanning Recognition System
Guanyu, Z., & Song, Q. (2016) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2016.2116 · Cited by 1
Efficient Structure from Motion for Large-Size Videos from an Open Outdoor UAV Dataset
Xiang, R., Chen, J., & Ji, S. (2024) · Sensors · doi:10.3390/s24103039 · Cited by 1
“We use four different features: SIFT [43], learning-based SuperPoint [45], D2-Net [47], and R2D2 [46] for extracting feature points in the original scale.”
MethodRobust method in photogrammetric reconstruction of geometric primitives in solid modeling
Yang, X. S. (2017) · doi:10.7282/t3wm1hkk · Cited by 1
WaveDiff: Wavelet-guided latent diffusion with Fourier detail enhancement for underwater images
Yan, X., Qu, R., Zhu, X., et al. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104749 · Cited by 1
The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means BetterPreprint
Parker, C., Daiter, M., Omar, K., Levi, G., & Hassner, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.03986 · Cited by 1
Mining user interests from social media by fusing textual and visual features
Chao, F.-Y., Jia, X., & Lin, C. (2016) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2016.7820713 · Cited by 1
Mitra, D., Boutchko, R., Ray, J., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081691 · Cited by 1
Feature-based video coding: Designing an RD efficient and search friendly framework
da Silva, R. C., Pereira, F. M. B., & da Silva, E. A. B. (2016) · Picture Coding Symposium · doi:10.1109/pcs.2016.7906353 · Cited by 1
Design Flow of Single Camera Motion Estimation Using GPU Accelerators
Yang, G., & Saniie, J. (2019) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2019.8834204 · Cited by 1
Inverted indexing in image fragment retrieval using huge keypoint-based vocabularies
Sluzek, A. S. (2013) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2013.6576576 · 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