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 44,501–44,600 of 56,200
Relative similarity preserving bitwise weights generated by an adaptive mechanism
Wang, Z., Zhang, L.-B., Sun, F., et al. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6997-y · Cited by 1
Deep µStitch: an unsupervised sliding window-based deep global microscopy image stitching framework
Wang, S., & Yu, W. (2025) · Applied Optics · doi:10.1364/ao.564775 · Cited by 1
Neural Network Based Electronics Segmentation
Ma, S., Fan, X., Wang, L., Cheng, J. S., & Xu, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27541-9_43 · Cited by 1
Co-VisiON: Co-Visibility ReasONing on Sparse Image Sets of Indoor Scenes
Chen, C., Dang, N., Zhang, J., Sun, W., Zheng, P., He, X., Ye, Y., Zhang, J., Srinivas, T., & Feng, C. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00504 · Cited by 2
Scale and Ane Invariant Local Detectors and Descriptors
Dork, G., & Rh, I. (2005) · Cited by 1
Efficient Interactive Search for Geo-tagged Multimedia DataPreprint
Long, J., Zhu, L., Zhang, C., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00571 · Cited by 1
Automatic Panorama Recognition and Stitching Based on Graph Structure
Zhao, Y., & Xia, Y. (2013) · doi:10.2991/cse.2013.58 · Cited by 1
Multi-scale Spatial Topic Models for scene recognition
Li, H. (2016) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2016.7852762 · Cited by 1
Multi-modal Retrieval via Deep Textual-Visual Correlation Learning
Song, J., Wang, Y., Wu, F. Q., Lü, W., Tang, S., & Zhuang, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23989-7_19 · Cited by 1
CAMILLI, F., & Prados, E. (2006) · Springer eBooks · doi:10.1007/0-387-31071-1_2 · Cited by 1
A Scaled Monocular 3D Reconstruction Based on Structure from Motion and Multi-View Stereo
Zhan, Z., Yang, F., Jiang, J., Du, J., Li, F., Sun, S., & Yan, W. Q. (2024) · Electronics · doi:10.3390/electronics13193943 · Cited by 1
Dynamic Places’ Definition for Sequence-Based Visual Place Recognition
Tsintotas, K. A., Bampis, L., & Gasteratos, A. C. (2022) · Springer tracts in advanced robotics · doi:10.1007/978-3-031-09396-8_4 · Cited by 1
Development of Three-Dimensional Feature-Based Vision Algorithm for Ship-Deck Landing
Britcher, V., Chopra, I., & Datta, A. (2025) · Journal of Aircraft · doi:10.2514/1.c038161 · Cited by 1
A Review of Methods to Quantify the Genomic Similarity of Topological Associating Domains
Li, Y., Wu, A., Liu, G., & Liu, L. (2019) · Journal of Computational Biology · doi:10.1089/cmb.2019.0129 · Cited by 1
Real-Time Bottle Detection Using Histogram of Oriented Gradients
Jangid, M., Srivastava, S., & Verma, V. K. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-8240-5_13 · Cited by 1
Deep Learning for Satellite Image Reconstruction
Saxena, J., Jain, A., & Krishna, P. R. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-16-5747-4_48 · Cited by 1
Calibrated Convolution with Gaussian of Difference
Yang, H., Li, C., Liang, Y., et al. (2022) · Applied Sciences · doi:10.3390/app12136570 · Cited by 1
Video copy recognition by Oriented PCA and statistical analysis
Yang, X., & Yuan, M. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414590 · Cited by 1
Research on Cotton Flow Velocity Measurement Based on SIFT Feature Matching
Feng, Z., Zhang, S., Dai, Y., et al. (2021) · International Academic Exchange Conference on Science and Technology Innovation (IAECST) · doi:10.1109/iaecst54258.2021.9695791 · Cited by 1
Özüağ, E., Güllü, M. K., Urhan, O., et al. (2011) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2011.6064622 · Cited by 1
Enhancing Inertial Navigation with Structure from Motion Trajectory Estimates
Tharp, J. S., & Woolley, B. G. (2016) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2016-2097 · Cited by 1
Zhou, G., Liu, S., Wang, X., Gao, E., Lu, Y., Yu, L., & Wang, W. (2026) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2026.105215 · Cited by 1
TOSD: A Hierarchical Object-Centric Descriptor Integrating Shape, Color, and Topology.
Choi, J.-H., Pyo, J.-W., An, Y.-C., & Kuc, T. (2025) · PubMed Central · doi:10.3390/s25154614 · Cited by 1
High accuracy fluorescence-guided cryo-focused ion beam millingPreprint
Perez, D., Betzler, S., Cleeve, P., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.11.724418 · Cited by 1
“These images were registered to the FIB image used for registration by SIFT registration in Fiji (Lowe, 2004; Schindelin et al., 2012) calculated for a portion of the image which should not experience deformation, such as a grid bar.”
Building localization from forward-looking infrared images for UAV guidance
Qin, Y., Cao, Z., Li, H., et al. (2013) · Gyroscopy and Navigation · doi:10.1134/s2075108713040093 · Cited by 1
Geotagged iage rcognition by cmbining tree dfferent knds of golocation fatures
Yaegashi, K., & Yanai, . (2010) · Asian Conference on Computer Vision · Cited by 1
AR Long-Term Tracking Combining Multi-Attention and Template Updating
Mengru, G., & Chen, Q. (2023) · Applied Sciences · doi:10.3390/app13085015 · Cited by 1
A review of feature indexing methods for fast approximate nearest neighbor search
Pham, T.-A., Le, V.-H., & Le, D.-N. (2018) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics.2018.8606853 · Cited by 1
Safavigerdini, K., Yaghooti, B., Sinopoli, B., et al. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00145 · Cited by 1
“Fundamental techniques in feature extraction [7, 11] and dimensionality reduction [6, 21] provide the mathematical foundation for these diverse tasks.”
Experimental analysis of SIFT and SURF features for multi-object image retrieval
Sudhamani, M. V., & Kavitha, H. (2017) · International Journal of Computational Vision and Robotics · doi:10.1504/ijcvr.2017.10004039 · Cited by 1
Facial tracking and animation for digital social system
Huang, D., Yao, Y., Tang, W. J., et al. (2018) · ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry (VRCAI) · doi:10.1145/3284398.3284413 · Cited by 1
Zhang, X., & Hirai, S. (2011) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2011.6181344 · Cited by 1
SoFIT: Self-Orienting Camera Network for Floor Mapping and Indoor Tracking
Liu, Y., Nie, J., Xia, S., et al. (2022) · International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss54816.2022.00029 · Cited by 1
Improving the generalization capability of face spoofing detection
Li, H. (2018) · doi:10.32657/10220/46392 · Cited by 1
Feature Point Analysis for Image Spam E-Mail Detection
Liu, T., & Lu, Y. (2009) · Chinese Conference on Pattern Recognition · doi:10.1109/ccpr.2009.5344082 · Cited by 1
Mutual Information Refinement for Flash-no-Flash Image Alignment
Varjo, S. J. O., Hannuksela, J., Sílven, O., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23687-7_37 · Cited by 1
K-medoids LSH: a new locality sensitive hashing in general metric space.
Silva, E., & Junior, E. A. D. V. (2013) · SBBD (Short Papers) · Cited by 1
Gaussian Processes for Object Detection in High Resolution Remote Sensing Images
Liang, Y., Monteiro, S. T., & Saber, E. S. (2016) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2016.0180 · Cited by 1
Pooled time series representation for mitosis event recognition
Su, Y., Wang, S., Nie, W., et al. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0572-7 · Cited by 1
A method of airborne remote sensing image quality assessment based on image matching
Tian, J., Duan, F., Qu, X., et al. (2012) · International Conference on Geoinformatics · doi:10.1109/geoinformatics.2012.6270326 · Cited by 1
Affine Invariant Feature Extraction Using Symmetry
Anjulan, A., & Canagarajah, N. C. (2005) · Lecture notes in computer science · doi:10.1007/11558484_42 · Cited by 1
Handcrafted and Deep Trackers: A Survey
Laaroussi, K., Ouerdi, N., Arrassen, I., & Kasmi, M. A. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-66850-0_9 · Cited by 1
A Tree-Structured Feature Matching Algorithm
Sun, X., Ma, X., Chen, L., et al. (2015) · Communications in computer and information science · doi:10.1007/978-3-662-48570-5_19 · Cited by 1
Unmanned Airship Based Multiple Spectrum Image Mosaic with SIFT Feature Matching
Su, J., & Ai, M. (2011) · doi:10.1109/m2rsm.2011.5697397 · Cited by 1
Aggregation of Deep Local Features using VLAD and Classification using R Forest
Vinay, A., Garg, H., Anand, A., et al. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.334 · Cited by 1
Inter-vehicle distance detection based on keypoint matching for stereo images
Shima, Y. (2017) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2017.8302064 · Cited by 1
Patterns of spon1b:GFP expression during early zebrafish brain development
Dueñas, N. A., Forero-Shelton, M., Zhdanova, I. V., & Akle, V. (2020) · BMC Research Notes · doi:10.1186/s13104-019-4876-x · Cited by 1
Seo, J.-J., Baddar, W. J., Kim, D. H., et al. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350866 · Cited by 1
Survey of Important Issues in Multi Unmanned Aerial Vehicles Imaging Systeme Indian Commercial Banks
Deng, C., Liu, G., Qu, F., He, X., & Zhang, S. (2018) · International Journal of Software & Hardware Research in Engineering · doi:10.26821/ijshre.6.10.2018.61005 · Cited by 1
Kumar, S., & Soundrapandiyan, R. (2022) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.1470 · Cited by 1
Multimodal Fine-Grained Grocery Product Recognition Using Image and Ocr TextPreprint
Pettersson, T., Riveiro, M., & Löfström, T. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4505952 · Cited by 1
Visual SLAM Loop Closure Detection Algorithm Based on LightGlue
Zhang, L., Xu, M., Tian, G., Ma, H., Wu, W., & Ma, L. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865507 · Cited by 1
“Local features include ORB [4], SIFT [5], SURF [6], etc.”
Vascular Roadmap Generation by Registration and Blending of Multiple Enhanced X-ray Angiograms
Kawabe, M., Ohnishi, T., Nakano, K., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3062834 · Cited by 1
Reflective Particle Tag for Arms Control and Safeguards Authentication.
Merkle, P. B., Gonzáles, A., Faust, A., et al. (2009) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 1
Target following camera system based on real-time recognition and tracking
Kumar, R. V. (2014) · Cited by 1
Ultrasound extended-field-of-view imaging based on motion estimation using quaternion wavelet
Liu, Y., Jin, J., Wang, Q., et al. (2012) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2012.6365366 · Cited by 1
Towards Large-Scale Visual Mapping and Localization
Pollefeys, M., Frahm, J., Fraundorfer, F., et al. (2011) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-19457-3_32 · Cited by 1
A scene parsing method based on super-pixel and mid-level feature
Dong, S. (2013) · IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT) · doi:10.1109/icbnmt.2013.6823952 · Cited by 1
Coloring image search with coupled multi-index
Zheng, L., Wang, S., & Tian, Q. (2015) · IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) · doi:10.1109/chinasip.2015.7230378 · Cited by 1
The Many Facets of Progressive Retrieval for CBIR
Bouteldja, N., Gouet-Brunet, V., & Scholl, M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89796-5_63 · Cited by 1
Searching for plausible gas sources using SIFT features
Zeng, M., Bai, Z., Meng, Q., Han, T., & Jia, H. (2012) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2012.6469985 · Cited by 1
E2SIFT: Neuromorphic SIFT via Direct Feature Pyramid Recovery from Events
Henry, C., Maharjan, P., Zhu Li, B., & York, G. W. P. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647465 · Cited by 1
Nakashika, T., Suga, A., Takiguchi, T., et al. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946632 · Cited by 1
SAR Image Registration Based on SAR-SIFT and Template Matching
Liu, S., Deng, X., Liu, C., et al. (2025) · Remote Sensing · doi:10.3390/rs17132216 · Cited by 1
The design of electric power equipment information search system
Yu, P., Yuan, H., & Zhao, Z. (2008) · International Conference on Computer-Aided Industrial Design and Conceptual Design (CAIDCD) · doi:10.1109/caidcd.2008.4730586 · Cited by 1
Detection of Facial Features on Color Face Images
Wang, H.-W., Wu, Y., Lu, Y.-L., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28487-8_9 · Cited by 1
Šabanovič, E. (2019) · doi:10.20334/2019-051-m · Cited by 1
Retrieval of Binary Features in Image Databases: A Study
Niedermayer, J., & Kröger, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11988-5_14 · Cited by 1
High-Precision Autonomous Parking Localization System based on Multi-Sensor Fusion
Chen, G., Wang, Z., Gao, Z., et al. (2024) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2024-01-2843 · Cited by 1
Comparison of deep learning and hand crafted features for mining simulation data
Georgiou, T., Schmitt, S., Bäck, T., Pu, N., Chen, W., & Lew, M. S. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413165 · Cited by 1
Multi-type feature fusion visual navigation for asteroid landing
Xiu, W., Zhu, S., & Liu, Y. (2024) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2024.10.021 · Cited by 1
Yamashita, Y., & Wakahara, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.11866 · Cited by 1
Real-time object detection by a multi-feature fully convolutional network
Guo, Y., Guo, X., Jiang, Z., Men, A., & Zhou, Y. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296365 · Cited by 1
High-performance image retrieval based on bitrate allocation
Dong, H., Zhu, C., Liu, J., et al. (2019) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2524334 · Cited by 1
Chen, M., Zhang, Z., Liu, K., et al. (2025) · The Photogrammetric Record · doi:10.1111/phor.70008 · Cited by 1
Cell Image Stitching Algorithm Based on Retinex And Improved ORB
Liu, N., & Li, Z. (2021) · International Symposium on Artificial Intelligence for Medicine Sciences (ISAIMS) · doi:10.1145/3500931.3501001 · Cited by 1
Precise localization of key-points to identify local regions for robust data hiding
Nataraj, L., Sarkar, A., & Manjunath, B. S. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5652531 · Cited by 1
IEKF based object pose estimation for Augmented Reality
Song, J., Hu, S., & Yang, Y. (2018) · International Conference on Virtual Reality · doi:10.1145/3198910.3198914 · Cited by 1
The Integrated Design of an Interactive Service Robot for Shanghai World Expo 2010
Xiong, R., Du, X., Wang, W. F., et al. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.308-310.2084 · Cited by 1
Improved 3D modeling using multistage feature extraction and matching
Mohamed, M. A., Ibrahim, A. F., & Othman, E. (2014) · International Conference on Computer Engineering & Systems (ICCES) · doi:10.1109/icces.2014.7030992 · Cited by 1
Visual Odometry Analysis on the ISRO’s Chandrayaan-3 Pragyan Rover Images
Sree, M. S., Ghosh, R., Maniktala, S., et al. (2025) · doi:10.1109/space65882.2025.11170544 · Cited by 1
“A ratio test is used to ensure reliable matching [4].”
A study on indoor automatic change detection for a mobile-camera
Tanjung, G., & Lu, T. (2007) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 1
A Graph Learning Based Framework for Billion-Scale Offline User Identification
Wang, D.-X., Weng, Z.-J., Wu, Z.-W., Zhang, Z.-Q., Cui, P., Zhao, H., & Zhou, J. (2022) · Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539191 · Cited by 1
Research on Large Space Building Fire Positioning Technology Using Video Surveillance Images
Lü, Y., Wang, H., Bi, Z., et al. (2013) · International Conference on Computer Science and Electronics Engineering (ICCSEE) · doi:10.2991/iccsee.2013.306 · Cited by 1
Using Range Information to Reduce Local Descriptor Computations
Stefánsson, S. F. (2011) · Cited by 1
Multi-scale Attributed Graph Kernel for Image Categorization
Hu, D., Xu, Q., Tang, J., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03338-5_51 · Cited by 1
Hsu, C.-Y., Cheng, Y.-C., & Lin, S.-F. (2012) · Optical Engineering · doi:10.1117/1.oe.51.2.027006 · Cited by 1
Free Viewpoint Video Synthesis based on Natural Features Using Uncalibrated Moving Cameras
Jarusirisawad, S., Saitô, H., & Non-members (2007) · Cited by 1
Image Clustering and Video Summarization for Efficient 3D Modelling and Reconstruction
Voulodimos, A. S., Protopapadakis, E. E., Doulamis, N. D., et al. (2020) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-5225-5294-9.ch009 · Cited by 1
Enhancement system of nighttime infrared video image and visible video image
Wang, Y., & Piao, Y. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2256456 · Cited by 1
THREE NEW METHODS FOR COLOR AND TEXTURE BASED IMAGE MATCHING IN CONTENT-BASED IMAGE RETRIEVAL
Scotia, N. (2010) · Cited by 1
KAZE Feature Point with Modified-SIFT Descriptor
Meixi, C., Yule, Y., & Zhao, Y. J. (2013) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/icmt-13.2013.153 · Cited by 1
Leveraging from group classification for video concept detection
Niaz, U., & Mérialdo, B. (2013) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2013.6576577 · Cited by 1
A New Non-smart Water Meter Digital Region Localization and Digital Character Segmentation Method
Lei, F., Xiong, Z., & Wang, X. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1922-2_39 · Cited by 1
Appel, R., & Aarabi, P. (2009) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2009.5090281 · Cited by 1
On-board three-dimensional object tracking: Software and hardware solutions
Mandava, A. K. (2020) · Digital Scholarship - UNLV (University of Nevada Reno) · doi:10.34917/1394589 · Cited by 1
Integrated Analytics and Visualization for Multi-modality Transportation Data
Silva, C. T., Freire, J., Miranda, F., et al. (2019) · Cited by 1
Handwritten Word Image Matching Based on Heat Kernel Signature
Zhang, X., & Tan, C. L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40246-3_6 · Cited by 1
Two-Stage Registration for Optical and SAR Images With Combined Features and Graph Neural Networks
Wu, W., Ning, C., Guo, J., et al. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3451524 · Cited by 1
Video annotation of TV content using audiovisual information
Mateus, J., Malheiro, F., Cavaco, S., et al. (2012) · doi:10.1109/icmcs.2012.6320188 · 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