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 40,501–40,600 of 56,201
Srivastava, M. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010911000003124 · Cited by 1
DGN: Descriptor Generation Network for Feature Matching in Monocular Endoscopy 3D Reconstruction
Zhang, K., Fan, W., Chen, Y., & Luó, X. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096371 · Cited by 1
Improvement on Image Rotation for Relative Self-Localization Estimation
Xiong, X., & Choi, B.-J. (2013) · Cited by 1
An improvement in SIFT Algorithm for Wide-Baseline Image Matching
Hasheminasab, S. M., Ebadi, H., Ahmadabadian, A. H., et al. (2015) · Journal of Geospatial Information Technology · doi:10.29252/jgit.3.3.53 · Cited by 1
Cancelable Iris Template Generation Using Weber Local Descriptor and Median Filter Projection
Kavati, I., Akula, V., Babu, E. S., et al. (2023) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon58879.2023.10322360 · Cited by 1
POOR TEXTURAL IMAGE MATCHING BASED ON GRAPH THEORY
Chen, S., Yuan, X., Yuan, W., 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/isprs-archives-xli-b3-741-2016 · Cited by 1
Time Delay Mitigation in Aerial Telerobotic Operations Using Heterogeneous Stereo-Vision Systems
Sakib, N., Gahan, K. C., & Woolsey, C. A. (2023) · Journal of Aerospace Information Systems · doi:10.2514/1.i011204 · Cited by 1
Nonaka, A., Arimura, H., Nakamura, K., et al. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2042875 · Cited by 1
Prototype-Based Methods for Human Movement Modeling
Lin, Z., Jiang, Z., & Davis, L. S. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_378 · Cited by 1
A Fast SAR-Optical Registration Algorithm for Land-Water Interface Area
Zhang, K., Yu, A., Tong, W., et al. (2024) · doi:10.1109/icsip61881.2024.10671416 · Cited by 1
“First, feature points are extracted through a feature detector, such as Harris[5], etc.; Subsequently, the feature points are encoded into feature vectors using descriptors, e.g. SIFT[6].”
MethodHigh Performance Automatic ROI Extraction from Hand Images with Convolutional Neural Networks
Sünnetcİ, B. K., Bingöl, Ö., Gedikli, E., & Ekіncі, M. (2024) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap64064.2024.10710885 · Cited by 2
“Feature Extraction: The Scale Invariant Feature Transform (SIFT) algorithm [36] identifies distinctive points in the image, called key-points.”
BackgroundNFGS Enabled Face Re-identification for Efficient Surveillance in Low Quality Video
Saha, P., & Das, A. (2019) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip47207.2019.8985713 · Cited by 1
Comparative study of SURF, KAZE and MSER for visual odometry in presence of noise
Dhatrak, S., & Gharpure, D. (2021) · AIP conference proceedings · doi:10.1063/5.0043916 · Cited by 1
Automatic registration of remote sensing images based on SURF and NSNNI
Zhao, J. C., Sun, J., Lei, L., et al. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.832553 · Cited by 1
MATCHING FILTER-BASED VSLAM OPTIMIZATION IN INDOOR ENVIRONMENTS
Wei, S., & Wang, S. (2023) · 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/isprs-archives-xlviii-1-w2-2023-679-2023 · Cited by 1
Improving Image Matching Accuracy with Keypoint Features and Feature Fusion
Lu, T. (2021) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic54370.2021.9647145 · Cited by 1
LF2SLAM: Learning-based Features For visual SLAM
Legittimo, M., Crocetti, F., Fravolini, M. L., Mollica, G., & Costante, G. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801935 · Cited by 1
Picture-Driven User Interface Development for Applications on Multi-platforms
Nguyen, V.-T., Tran, M., & Duong, A.-D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07233-3_33 · Cited by 1
Hard-Lite SLAM: A Hybrid Detector Based Real-Time SLAM System
Cai, C., Jiao, J., Xu, W., et al. (2022) · International Conference on Innovation in Artificial Intelligence (ICIAI) · doi:10.1145/3529466.3529473 · Cited by 1
Ибрагимова, Р. Р., Гильманов, И. И., Лопухова, Е. А., et al. (2022) · Вестник Российского государственного медицинского университета · doi:10.24075/vrgmu.2022.062 · Cited by 1
Efficient Score-Based Indexing Technique for Fast Palmprint Retrieval
Kavati, I., Prasad, M. V. N. K., & Bhagvati, C. (2017) · SpringerBriefs in computer science · doi:10.1007/978-3-319-57660-2_3 · Cited by 1
Visual-inertial SLAM with line segment merging and efficient feature tracking method
Li, W., & Qu, G. (2025) · Machine Vision and Applications · doi:10.1007/s00138-025-01759-9 · Cited by 1
Cumulative Color Naming for Accurate and Robust Image Retrieval
Shi, X., Guo, Z., Zhang, D., et al. (2016) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv.2016.35 · Cited by 1
Shuhua, 周. Z., Sixiang, 许. X., Chenchen, 董. D., et al. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop221272 · Cited by 1
Visual event detection using orientation histograms with feature point trajectory information
Pong, H.-K., Xue, P., & Tian, Q. C. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202504 · Cited by 1
Doizaki, R., Iiba, S., Okatani, T., et al. (2015) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.30.124 · Cited by 1
A new trademark detection method via trademark confidence score of MSERs
Yang, Z., Liu, J., Zhang, Y., et al. (2019) · International Conference on Digital Image Processing · doi:10.1117/12.2539671 · Cited by 1
NII, Japan at the first THUMOS Workshop 2013
Phan, S. (2013) · Cited by 1
SKD: Keypoint Detection for Point Clouds Using Saliency Estimation
Tinchev, G., Peñate-Sánchez, A., & Fallon, M. F. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3065224 · Cited by 1
Saliency Superpixel Matching for Underwater Terrain-Aided Localization
Zhang, F., Sun, Y., Zhang, L., et al. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3592985 · Cited by 1
“To verify the effectiveness of the proposed SSM method, we compare SSM with feature point-based matching meth-ods, including SIFT [20], MFD [34], and LoFTR [28], and area-based matching methods, including NCC, BBS [16], and QATM [17], where LoFTR and QATM use depth features.”
From coin to data: the impact of object detection on digital numismatics
Cabral, R., Iorio, M. D., & Harris, A. (2025) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqaf046 · Cited by 1
Image clusters based 3D virtual tour schema
Zhang, D., Zheng, R., & Yang, G. (2014) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2014.6933650 · Cited by 1
Exploiting Deep Contrast Feature for Image Retrieval
Zhou, L., Liu, G., Li, Z., et al. (2025) · Cognitive Computation · doi:10.1007/s12559-024-10375-0 · Cited by 1
3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS
Soltanpour, S. (2018) · Scholarship at UWindsor (University of Windsor) · Cited by 1
Novel Co-SIFT Detector for Scanned Images Differentiation
Štancelová, P., Černeková, Z., & Ferko, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-22025-8_7 · Cited by 1
Image Registration using Fireworks Algorithm and Chaotic Sequences
Bejinariu, S.-I., Rotariu, C., Costin, H. N., et al. (2019) · International Symposium on Advanced Topics in Electrical Engineering · doi:10.1109/atee.2019.8725020 · Cited by 1
Is Sparsity Really Relevant for Image Classification
Rigamonti, R., Brown, M., & Lepetit, V. (2010) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1
Proposal Flow: Semantic Correspondences from Object ProposalsPreprint
Ham, B., Cho, M., Schmid, C., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07144 · Cited by 1
Tracking chessboard corners using projective transformation for augmented reality
Malek, S., Zenati-Henda, N., Benbelkacem, S., et al. (2011) · doi:10.1109/ccca.2011.6031196 · Cited by 1
Coupled multiview autoencoders with locality sensitivity for three-dimensional human pose estimation
Yu, J., Sun, J., Luo, S., & Duan, B. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.5.053026 · Cited by 1
Image-based effective feature generation for protein structural class and ligand binding prediction
Sadique, N., Ahmed, A. A. N., Islam, M. T., Pervage, M. N., & Shatabda, S. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.253 · Cited by 1
An optimal solution for RANSAC using constraint of saliency region
Zheng, J. Y., Xian, S., Zhang, W., et al. (2012) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2012.6294805 · Cited by 1
The Multi-view Geometry of Parallel Cylinders
Tegler, E., Engman, J., Gillsjö, D., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-31438-4_32 · Cited by 1
Enhanced Mapping of Multi-robot Using Distortion Reducing Filter Based SIFT
Choi, K., Kim, Y., An, J., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13495-1_80 · Cited by 1
Detection of Critical Camera Configurations for Structure from Motion Using Random Forest
Michelini, M., & Mayer, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41404-7_36 · Cited by 1
Research on Instrument Position Detection Method Based on YOLOV4
Zhang, Y., & Cao, N. (2021) · International Conference on Wireless Communications and Smart Grid (ICWCSG) · doi:10.1109/icwcsg53609.2021.00118 · Cited by 1
Making an image worth a thousand visual words
Pedrosa, G. V., & Traina, A. J. M. (2015) · Scientific Electronic Library Online (São Paulo Research Foundation, Latin American and Caribbean Center on Health Sciences Information, Conselho Nacional de Desenvolvimento Científico e Tecnológico) · Cited by 1
Shallow Multi-Scale Network For Stylized Super-Resolution
Durand, T., Rabin, J., & Tschumperlé, D. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506261 · Cited by 1
Origin of the Chromospheric Umbral Waves in Sunspots
Zhang, X., Yan, X., Xue, Z., et al. (2024) · Solar Physics · doi:10.1007/s11207-024-02323-w · Cited by 1
“Because of the unstable seeing conditions during the observation, we selected the best-quality TiO image in each time interval and used the Scale-Invariant Feature Transform algorithm (SIFT: Lower, 2004; Ji et al., 2019) to align the TiO images and the HMI continuum images with sub-pixel accuracy.”
MethodFFUIE: Fourfold-Fusion of Color and Clarity for Underwater Image Enhancement
Mortuza, M. G., Mim, S. A., & Zhang, J. (2026) · Optics & Laser Technology · doi:10.1016/j.optlastec.2026.114689 · Cited by 1
Content-based image retrieval using salient points
Zhao, S., Wang, S., & Gao, H. (2010) · doi:10.1109/icicisys.2010.5658630 · Cited by 1
Grinin, L. E., Леонидович, Г. А., & Леонидович, Г. И. (2023) · Философия и общество · doi:10.30884/jfio/2023.04.01 · Cited by 2
Global localization from a single feature correspondence
Fraundorfer, F., & Bischof, H. (2006) · Cited by 1
Real-time 3D reconstruction using point-dependent pose graph optimization framework
Liu, L., Tang, T., Chen, J., et al. (2022) · Machine Vision and Applications · doi:10.1007/s00138-021-01268-5 · Cited by 1
Bags of Trajectory Words for video indexing
Strat, S. T., Benoît, A., & Lambert, P. (2014) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2014.6849820 · Cited by 1
Embedded Memory Architecture for Low-Power Application Processor
Yoo, H. J., & Kim, D. (2009) · Integrated circuits and systems · doi:10.1007/978-0-387-88497-4_2 · Cited by 1
Age-variation face recognition based on bayes inference
Su, Y., Wang, M., & Bao, H. (2015) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2015.7382097 · Cited by 1
Dongdong, 史. S., 邹, 骏., Limin, 刘. L., et al. (2025) · Laser & Optoelectronics Progress · doi:10.3788/lop250482 · Cited by 1
Decelle, J., Kayal, E., Bigeard, E., Gallet, B., Bougoure, J. J., Clode, P. L., Schieber, N. L., Templin, R., Hehenberger, E., Prensier, G., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.04.467241 · Cited by 1
An approach for combining multiple descriptors for image classification
Tran, D. T., Jansen, B. M. P., Deklerck, R., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2181017 · Cited by 1
Image Matching Algorithm Based on Improved Harris
Lei, F., Ren, W., & Lv, L. (2015) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn.2015.81 · Cited by 1
A Fisher Kernel Approach for Multiple Instance Based Object Retrieval in Video Surveillance
Mironică, I., Mitrea, C. A., Ionescu, B., et al. (2015) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2015.04006 · Cited by 1
Pléiades à Pléiades Neo : Une analyse de l'amélioration de la restitution 3D
Youssefi, D., Lallement, D., Hümmer, C., et al. (2024) · Revue Française de Photogrammétrie et de Télédétection · doi:10.52638/rfpt.2024.672 · Cited by 1
A comparison of feature extraction methods in image stitching
Zheng, J. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/15/20230828 · Cited by 1
Enhanced Pseudo-Labels for End-to-End Weakly Supervised Semantic Segmentation with Foundation Models
Zhou, X., & Tang, Z. (2025) · Applied Sciences · doi:10.3390/app151810013 · Cited by 1
PERFORMANCE ANALYSIS OF SELECTED FEATURE DESCRIPTORS USED FOR AUTOMATIC IMAGE REGISTRATION
Ajayi, O. G. (2020) · 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/isprs-archives-xliii-b3-2020-559-2020 · Cited by 1
Optimized SIFT Feature Matching for Image Retrieval
SCHULZE, C. H., & Liwicki, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12093-5_5 · Cited by 1
Enforcing the General Planar Motion Model: Bundle Adjustment for Planar Scenes
Örnhag, M. V., & Wadenbäck, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-40014-9_6 · Cited by 1
Key Frame Extraction from Videos Based on SIFT and Structural Similarity
De, P. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-2100-3_29 · Cited by 1
Liu, Y., Huang, K., Qi, X., Hao, S., Li, J., Huang, Z., Yang, M., Zhao, N., Li, Y., Mao, R., et al. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2025.3580227 · Cited by 1
“The frontend computes the initial pose state of camera, primarily through feature point extraction and matching [14], [15], [16].”
Multi-object tracking in video sequences
Pinheiro, M. A. (2010) · Open Repository of the University of Porto (University of Porto) · doi:10.34626/zgya-xa92 · Cited by 1
Wang, Z., Zhan, Z.-X., Zhou, G., & Xiao-quan, W. A. N. G. (2022) · 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/isprs-archives-xliii-b2-2022-649-2022 · Cited by 1
Optical image and SAR image registration based on position constraint
Wu, Y., Hei, G., Teng, D., et al. (2024) · International Conference on Geology, Mapping and Remote Sensing (ICGMRS) · doi:10.1117/12.3020081 · Cited by 1
“The feature extraction method based on Scale Invariant Feature Transform (SIFT) [1] algorithm has become the research focus of optical image and SAR image registration at this stage.”
MethodTrajectory matching from unsynchronized videos
Hu, H., & Zhou, J. (2010) · 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.2010.5539811 · Cited by 1
Research on the Parameters of Feature Extraction and Matching Algorithms for Breech Face Comparisons
Zhu, J., Hong, R., Robin, A. U. Z., et al. (2022) · International Conference on Robotics, Control and Automation (ICRCA) · doi:10.1109/icrca55033.2022.9828901 · Cited by 1
Sparse and redundant signal representations for x-ray computed tomographyPreprint
Karimi, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03379 · Cited by 1
Bei, W., Fan, X., Jian, H., Du, X., Yan, D., Xu, J., & Ge, Q. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2421956 · Cited by 2
“Concerning the SfM part, typical methods like SIFT achieve subpixel precision of keypoint localization but tend to be distracted by high-resolution details like repetitive patterns or similar visual structures.”
InfluentialTop-Down Tracking and Estimating 3D Pose of a Die
Torres, F., & Kropatsch, W. G. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34166-3_54 · Cited by 1
A Scheme for Shot Detection and Video Retreival using Spatio Temporal Features
(2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.b1139.0982s1019 · Cited by 1
Audio-visual face detection for tracking in a meeting room environment
Barnard, M., Wang, W., Kittler, J., et al. (2013) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 1
Generalizable multimodal retinal image registration via label-free vessel segmentation
Doshi, U., Davis, E., Al‐Sheikh, M., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108841 · Cited by 1
Merkmalsverfolgung für die Panoramaendoskopie
Nowack, S., Wittenberg, T. C., Paulus, D. W. R., et al. (2013) · Informatik aktuell · doi:10.1007/978-3-642-36480-8_37 · Cited by 1
VLO: Vision-Laser Odometry for Autonomous Flight of Micro Aerial Vehicle
Tang, D., Fang, Q., Shen, L., et al. (2018) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2018.8581123 · Cited by 1
Methodologies for visual correspondence
Tombari, F. (2009) · AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna) · doi:10.6092/unibo/amsdottorato/1482 · Cited by 1
Fang, X., Zhang, S., Zhang, H., et al. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3653189 · Cited by 1
“The number of keypoints (N) extracted per image via the SIFT algorithm [48] was varied from 1 k to 8 k .”
InfluentialAdvanced methods for robust object detection
Cai, Z., & Vasconcelos, N. M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822109-9.00011-4 · Cited by 1
Evaluation of Biometric Systems: An SVM-Based Quality index
El-Abed, M., Giot, R., Charrier, C., et al. (2010) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 1
Robust Stereo Matching for Document Images Using Parameter Selection of Text-Line Extraction
Afzal, M. Z., Bukhari, S. S., Kramer, M., et al. (2012) · UWA Profiles and Research Repository (University of Western Australia) · Cited by 1
Unsupervised graph-based image clustering for pretext distribution learning in IC assurance
Tee, Y.-Y., Hong, X., Cheng, D., Lin, T., Shi, Y., & Gwee, B. (2023) · Microelectronics Reliability · doi:10.1016/j.microrel.2023.115160 · Cited by 2
LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
Nakshbandi, M.-M., Sharawy, Z., Cojocaru, D., & Grigorescu, S. M. (2025) · International Aegean Conference on Electrical Machines and Power Electronics & International Conference on Optimization of Electrical and Electronic Equipment (ACEMP-OPTIM) · doi:10.1109/optim-acemp62776.2025.11075240 · Cited by 1
“The dataset provides a standardized test for evaluating feature-based algorithms (SIFT [15], ORB [16], BRIEF [17], DAISY [18]) under challenging viewpoint variations.”
Person detection and identification surrounding gas facilities based on Improved-Yolov4-tiny
Wang, Z., Zhuang, Y., Li, J., et al. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2205/1/012010 · Cited by 1
Using PhotoCube as an extensible demonstration platform for advanced image analysis techniques
Tómasson, G., Olafsson, G. K., Sigurporsson, H., et al. (2012) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2012.6269792 · Cited by 1
Multiple feature instance detection with density based clustering
Makhura, O. J., & Woods, J. (2018) · International Conference on Consumer Electronics · doi:10.1109/icce.2018.8326328 · Cited by 1
Wang, S., Xue, Y., Chu, L., et al. (2013) · doi:10.1145/2461466.2461524 · Cited by 1
Toward Effortless TV-to-Online (T2O) Experience: A Novel Metric Learning Approach
Luo, Y., Wen, Y., Tao, D., et al. (2016) · Globecom · doi:10.1109/glocom.2016.7842335 · Cited by 1
Detecting Structured Image Region Using Local Features and Clustering Analysis
Lin, H., Hsu, C.-Y., & Chiang, Y.-Y. (2013) · Machine Vision and Applications · Cited by 1
Detecting and tracking people and their body parts in infrared
Jüngling, K., & Arens, M. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.883663 · Cited by 1
A Sparse Pooling Adversarial Learning Framework for Anomaly Event Detection
ZHANG, M., Hu, H., & Li, Z. (2025) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2025.02006 · Cited by 1
Deep Segmentation Network Without Mask Image Supervision for 2D Image Registration
Yoneda, S., Irie, G., Shibata, T., Nishiyama, M., & Iwai, Y. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-06381-7_16 · 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
