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 41,101–41,200 of 56,200
Feature Consistent Point Cloud Registration in Building Information Modeling
Jiang, H., Liu, Y., Nader, G., et al. (2023) · doi:10.1145/3589572.3589578 · Cited by 1
Combining Spatial and Semantic Information to Represent Image
Zhao, Y. (2013) · Jisuanji kexue yu tansuo · Cited by 1
Glasgow University at TRECVID 2008
Punitha, P., Urruty, T., Feng, Y., et al. (2007) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · Cited by 1
A New Watermarking Scheme Based on SIFT Feature Points with Reversible Rotation
Huang, X. (2019) · International Conference on Digital Data Processing (DDP) · doi:10.1109/ddp.2019.00021 · Cited by 1
Depth-from-trajectories for uncalibrated multiview video
Ardis, P. A., Singhal, A., & Brown, C. M. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.806816 · Cited by 1
Face recognition via sparse representation of SIFT feature on hexagonal-sampling image
Zhang, D., Zhang, X., Li, L., et al. (2018) · International Conference on Graphic and Image Processing (ICGIP) · doi:10.1117/12.2304894 · Cited by 1
An Adaptive Approach for Keypoints Description Using Fractional Derivative
Si, S., Hu, F., Wang, Z., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23989-7_62 · Cited by 1
KSR‐BOF: a new and exemplified method (as KSRs method) for image classification
Maleki, M. H., Hodtani, G. A., & Hashemi, S. H. O. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2019.0613 · Cited by 1
Wang, F., Ma, M., & Zhang, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4501377 · Cited by 1
Evaluating Local Feature Detectors in Salient Region Detection
Kinnunen, T., Laine‐Hernandez, M., & Oittinen, P. T. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38886-6_9 · Cited by 1
Estimation of Initial Position Using Line Segment Matching in Maps
Wei, C., Wang, R., Wu, T. T., & Fu, H. (2016) · International Journal of Advanced Robotic Systems · doi:10.5772/64067 · Cited by 1
Combining color histogram and gradient orientation histogram for vision based global localization
Liu, H., Yu, X., & Yu, H. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5346684 · Cited by 1
Scale-Based Local Feature Selection for Scene Text Recognition
Zhang, B., Feng, J., & Tang, X. (2014) · INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE · doi:10.14569/ijarai.2014.030403 · Cited by 1
Improved 3D Scene Modeling for Image Registration in Change Detection
van Riel, S., van de Wouw, D., & de With, P. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.7.iriacv-465 · Cited by 1
A Study on Evaluation of Safe Driving Using an In-Vehicle Data Camera
Ishizaki, R., & Morimoto, M. (2013) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2013.571 · Cited by 1
Speed-up Method of Rotation Template Matching Based on Optimization of Manifold's Shape
Okugawa, K., & Hashimoto, M. (2017) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.83.1166 · Cited by 1
House structure feature enhancement using Multi-view high resolution SAR images
Yang, G., Zhang, X., Juanjuan, Y., et al. (2017) · International Conference on Frontiers of Sensors Technologies (ICFST) · doi:10.1109/icfst.2017.8210517 · Cited by 1
An evaluation of large-scale methods for image instance and class discoveryPreprint
Douze, M., Jeǵou, H., & Johnson, J. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.02898 · Cited by 1
Complete Scene Reconstruction by Merging Images and Laser ScansPreprint
Gao, X., Shen, S., Zhu, L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09568 · Cited by 1
An Efficient Block Phase Correlation Approach for CMFD System
Soni, B., Das, P. K., & Thounaojam, D. M. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-7871-2_5 · Cited by 1
Li, F., Li, J., Han, W., Feng, R., & Wang, L. (2021) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.87.8.577 · Cited by 1
Hermansyah, A., Nugroho, A. R., Kurniawan, A. R., et al. (2019) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia.2019.8937152 · Cited by 1
Reconocimiento de rostros en tiempo real sobre dispositivos móviles de bajo costo
López, A. C., & Torres, F. P. (2018) · Lámpsakos · doi:10.21501/21454086.2938 · Cited by 1
Two-Stage Multi-Camera Constrain Mapping Pipeline for Large-Scale 3D Reconstruction
Han, F., Jia, S., Jiao, Y., et al. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240636 · Cited by 1
Deep Feature Fusion for Face Analytics
Sankaran, N., Mohan, D. D., Tulyakov, S., Setlur, S., & Govindaraju, V. (2021) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-74697-1_9 · Cited by 1
Matching spherical panoramas and planar photographs
Carmichael, G. (2009) · doi:10.22215/etd/2009-09136 · Cited by 1
A Novel Cross-Device Target Handover Method based on Affine Transformation and Correlation Filter
Xu, J., Chen, Z., He, H., et al. (2023) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc58219.2023.10326513 · Cited by 1
Zhou, L., Peng, T., Zhiqiang, H. A. N., Li, L., Zhu, Q. Q., & Ye, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3664764 · Cited by 1
Accurate cyber-physical system simulation for distributed visual search applications
Martino, D., Shen, Y. C., Paracchini, M. B. M., et al. (2017) · IEEE International Forum on Research and Technologies for Society and Industry (RTSI) · doi:10.1109/rtsi.2017.8065917 · Cited by 1
An automatic method for matching salient structures in optical remote sensing images
Wang, Z., Feng, X., Wu, Y., et al. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1976873 · Cited by 1
Vision-Based Pose Optimization Using Learned Metrics
Li, S., Xian, Y., Zhang, T., Li, B., Zhang, D., & Guo, W. (2020) · IEEE Access · doi:10.1109/access.2020.3021824 · Cited by 1
Contour-Based Binary Image Orientation Detection by Orientation Context and Roulette Distance
Zhou, J. H., & Matsumaru, T. (2016) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.e99.a.621 · Cited by 1
Tyagi, A., & Bansal, S. R. (2023) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-031-31164-2_17 · Cited by 1
PCIR: An Open-World Remote Sensing Image Representation Learning Method From a Causal Perspective
Zhao, L. D., Li, M., Shao, R., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3647787 · Cited by 1
Pose-Invariant 2D Face Verification by Combining MICA and 2D Features
Joseph, A. F. A., & Raman, R. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93691-3_2 · Cited by 1
Study on the Improvement of Flexibility for an Industrial Robot Based on Machine Vision
Wang, J., Zhang, X., Zhang, W., et al. (2014) · Journal of Robotics Networking and Artificial Life · doi:10.2991/jrnal.2014.1.1.9 · Cited by 1
Evolutionary Deep Learning Using GP with Convolution Operators
Bi, Y., Xue, B., & Zhang, M. (2021) · Adaptation, learning, and optimization · doi:10.1007/978-3-030-65927-1_5 · Cited by 1
Outliers removal based on geometric coherence constraints in remote sensing image matching
Jia, L., Shi, L., Shi, D., et al. (2025) · International Journal of Remote Sensing · doi:10.1080/01431161.2025.2471594 · Cited by 1
“Currently, point feature-based image matching methods are widely used, such as scale-invariant feature transform (SIFT) (Lowe 2004), optical-SAR SIFT (OS-SIFT) (Xiang, Wang, and You 2018), radiation-variation insensitive feature transform (RIFT) (J. Li, Hu, and Ai 2020), prominent structure-guided feature (PSGF) (Lv e…”
Semi-supervised image classification based on sparse coding spatial pyramid matching
Chen, H., Liu, G., & Zhang, L. (2013) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2499788.2499860 · Cited by 1
Daniel, N., Wasserman, T., Adler, Z., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.17.492244 · Cited by 1
Gao, H., Xie, W., Kang, H., et al. (2019) · Optoelectronics Letters · doi:10.1007/s11801-019-8208-0 · Cited by 1
An improved SIFT descriptor based on in-out region division
Xia, C., & Wei, P. (2017) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2017.8124514 · Cited by 1
Digital watermarking algorithm based on image feature point
Liu, J., Wang, Y., & He, W. (2010) · Journal of Computer Applications · doi:10.3724/sp.j.1087.2010.00702 · Cited by 1
An Incremental Clustering based codebook construction in video copy detection
Ren, H., Ramampiaro, H., Zhang, Y., et al. (2012) · IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) · doi:10.1109/ssiai.2012.6202489 · Cited by 1
Consistent video projection on curved displays
Lyu, Y., Lu, S.-P., Bolsée, Q., et al. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.03.006 · Cited by 1
Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysis
Chauvin, L., Kumar, K., Desrosiers, C., et al. (2018) · Mathematics and visualization · doi:10.1007/978-3-319-73839-0_7 · Cited by 1
Implementation of Robust SIFT-C Technique for Image Classification
Ghazali, K. H., Mokri, S. S., Mustafa, M. M., et al. (2007) · Student Conference on Research and Development · doi:10.1109/scored.2007.4451374 · Cited by 1
Efficient and robust multi-template tracking using multi-start interactive hybrid search
Firouzi, H., & Najjaran, H. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.12.009 · Cited by 1
Discriminative supplementary representation learning for novel-category classification
Liu, Q., Li, Z., & Tang, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.100 · Cited by 1
RECOGNITION OF AIRCRAFT IN REMOTE SENSING IMAGES USING CONVOLUTIONAL NEURAL NETWORK
Sayani, J. K. S. (2022) · Journal of Airline Operations and Aviation Management · doi:10.56801/jaoam.v1i1.8 · Cited by 1
Development of the complex modelling system for intelligent control algorithms testing
Kostylev, D., Tolstikhin, A., & Ulryanov, S. (2019) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro.2019.8757003 · Cited by 1
Optimized SIFT: An efficient algorithm for matching medical products images
Abdollahi, A., Targhi, A. T., & Dehshibi, M. M. (2015) · International Conference on Digital Information Management · doi:10.1109/icdim.2015.7381862 · Cited by 1
Connecting Visual Experiences using Max-flow Network with Application to Visual LocalizationPreprint
Hafez, A. H. A., Agarwal, N., & Jawahar, C. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.00208 · Cited by 3
Fast Human Detection Using Dynamic Contour and Histograms of Oriented Gradients
Qu, Z. Y., Jin, Y., & Feng, J. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.347-350.3600 · Cited by 1
Qiu, J., Li, H., Cao, H., et al. (2023) · doi:10.36227/techrxiv.22266820.v2 · Cited by 1
Amor, M. B., Kammoun, F., & Masmodi, N. (2016) · International Conference on Sciences and Techniques of Automatic Control and Computer Engineering · doi:10.1109/sta.2016.7951986 · Cited by 1
A Novel Image Perceptual Hashing Algorithm via Feature Points and Fuzzy Distance Matching
Hu, Y., Niu, X., Liu, S., et al. (2009) · doi:10.1109/csie.2009.372 · Cited by 1
Jinheng, 靖. J., Xiangbin, 凌. L., Yuhao, 熊. X., et al. (2025) · Infrared and Laser Engineering · doi:10.3788/irla20240478 · Cited by 1
Algorithm for Object Image Automatic Tracking
Баумана, М. И. Н. Э., Колючкин, В. Я., Бойков, В. А., et al. (2017) · Herald of the Bauman Moscow State Technical University Series Instrument Engineering · doi:10.18698/0236-3933-2017-5-4-13 · Cited by 1
Comparing two methods of object detection in autonomous vehicle
Rosas-Miranda, D. I., Ponomaryov, V. I., & Palacios-Enriquez, A. (2014) · International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) · doi:10.1109/iceee.2014.6978279 · Cited by 1
Efficient Content-based Image Retrieval for Multi-Image Queries
Kim, T., Tu, N. A., & Lee, Y.-K. (2016) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1145/2857546.2857641 · Cited by 1
SkyGuard: Semi-supervised Drone Technology for Real-Time Traffic Rule Enforcement
Gautam, V., Thakare, S., Prasad, S., et al. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93691-3_10 · Cited by 1
AeroSCR: Large-Scale Scene Coordinate Regression for Aerial Visual Localization
Lin, W., Guo, C., Zhang, P., et al. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3679046 · Cited by 1
“Then, local features [8], [13], [14], [35] are extracted from both the query and candidate images, and FM is performed.”
Use of Photogrammetry in a Business Simulation Game
Kalisperakis, I., Kopsiaftis, G., Rallis, I., et al. (2022) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3529190.3534767 · Cited by 1
Implicit Map Augmentation for Relocalization
Hou, Y., Shen, T., Yang, T.-Y., DeTone, D., Kim, H. J., Sweeney, C., & Newcombe, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25066-8_36 · Cited by 1
A GRAPH BASED APPROACH FOR FINDING PEOPLE IN NEWS
Özkan, D. (2007) · Bilkent University Institutional Repository (Bilkent University) · Cited by 1
Operator-interactive abondoned and removed object detection
Çapa, N., & Hocaoǧlu, A. K. (2016) · doi:10.1109/siu.2016.7495947 · Cited by 1
Atsumi, M. (2013) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2013.52014 · Cited by 1
A parametric elastic registration method for airborne multispectral line scan imager
Zhou, C., Meng, F., Li, C., et al. (2017) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2017.8359904 · Cited by 1
Multi-camera Moving Object Detection Based on Improved ORB Feature Matching
Cai, Z., Niu, Y., Zhao, J., et al. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-6613-2_522 · Cited by 1
A Generic, Multimodal Geospatial Data Alignment System for Aerial Navigation
Martin-Lac, V., Petit-Frère, J., & Caillec, J. L. (2023) · Remote Sensing · doi:10.3390/rs15184510 · Cited by 1
3D image geo-registration using vision-based modeling
Ni, K. S., Sun, Z. Z., & Bliss, N. T. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946796 · Cited by 1
Evaluation of Lightweight Local Descriptors for Level Ground Navigation with Monocular SLAM
Chen, W.-Y., Wan, Y., Ou, S.-Q., & Xue, Z.-D. (2018) · Chinese Conference on Pattern Recognition and Computer Vision · doi:10.1007/978-3-030-03341-5_29
Partial Least Squares for Face Hashing
dos Santos, C. E., & Schwartz, W. R. (2016) · doi:10.5753/ctd.2016.9132 · Cited by 1
Wang, S., Yao, J., Petrick, N., et al. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.811219 · Cited by 1
Super-resolution reconstruction for sequential license plate images
Yao, Y., Xing, Z., & Yue, G. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2281578 · Cited by 1
SliceLRF: A Local Reference Frame Sliced along the Height on the 3D Surface
Zhong, B., & Li, D. (2023) · Sensors · doi:10.3390/s23073483 · Cited by 1
A Practical Method for 3D Reconstruction with Uncalibrated Image Sequences
Liu, D., Wang, X., Liu, S., et al. (2010) · Chinese Conference on Pattern Recognition · doi:10.1109/ccpr.2010.5659207 · Cited by 1
A Robust Approach for Road Users Classification Using Motion Cues
Talib, H. (2015) · doi:10.22215/etd/2015-10876 · Cited by 1
Self-Reflective Risk-Aware Artificial Cognitive Modeling for Robot Response to Human BehaviorsPreprint
Han, F., Reardon, C., Parker, L. E., & Zhang, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.04934 · Cited by 1
On Feature-Based SAR Image Registration: Appropriate Feature and Retrieval Algorithm
Li, D., Zhang, Y., & Shi, X. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.81665 · Cited by 1
A new framework to enhance Evolution-COnstructed object recognition method
Zayyan, M. H., Alrahmawy, M. F., & Elmougy, S. (2017) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2017.10.003 · Cited by 1
Finding learned obstacles to avoid collisions in autonomous robotic navigation
Sánchez-García, Á. J., Ríos-Figueroa, H. V., Marı́n-Hernández, A., et al. (2017) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2017.8402489 · Cited by 1
Re-ranking approach to mobile landmark recognition
Banlupholsakul, K., Ieamsaard, J., & Muneesawang, P. (2014) · International Computer Science and Engineering Conference · doi:10.1109/icsec.2014.6978203 · Cited by 1
A Facial Features Matching Method with Age Robustness Based on SIFT Algorithm
Lin, K., Wu, Q., Kong, L., et al. (2017) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce.2017.14 · Cited by 1
Liu, X., Yu, H., Liang, C., et al. (2025) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.109307 · Cited by 1
LDCFormer: A Lightweight Approach to Spectral Channel Image Recognition
Qi, X., Zhi, M., Yin, Y., et al. (2024) · doi:10.1109/ijcnn60899.2024.10651133 · Cited by 1
Kalliatakis, G., Kounalakis, T., Papadourakis, G., et al. (2012) · doi:10.2316/p.2012.779-027 · Cited by 1
Large parallax dental image stitching based on regional clustering
Li, H.-A., & Yang, L. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109126 · Cited by 1
A multilayered approach to location recognition
Wimpey, B. J., Drucker, E., Martin, M. P., et al. (2009) · Proceedings of IEEE Southeastcon · doi:10.1109/secon.2009.5174077 · Cited by 1
Analysis of Placental Oxygenation in BOLD MRI for Fetal Growth Restriction Estimation
Nishida, K., Morita, K., Enomoto, N., et al. (2024) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scisisis61014.2024.10759965 · Cited by 1
Can keypoint descriptors be used in outdoor image classification?
Păvăloi, I., Ignat, A., & Lazăr, C. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.174 · Cited by 1
Chen, X., Candan, K. S., Sapino, M. L., et al. (2015) · doi:10.1145/2806416.2806577 · Cited by 1
iSBEM: An Open-Source Workflow for Automated ROI Targeting in Volume Electron MicroscopyPreprint
Ronchi, P., Ross, G., Burrell, A., de Folter, J., Klenz, Y., Darif, N., Young, F., Lawson, M., Albers, J., Pietz, T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.05.730298 · Cited by 1
“Single-tile ROIs were aligned in Z using the Fiji plugin “linear stack alignment with SIFT” using translation as transformation.”
A NOVEL ITERATIVE MATCHING SCHEME BASED ON HOMOGRAPHY METHOD FOR X-RAY IMAGE
Song, L., Zou, H., JI, Z., Xie, X., & LI, W. (2020) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519420500384 · Cited by 1
Robust Vehicle Localization for Spherical Camera Models: Solution, Framework, and Verification
Lu, Y., Jiao, Y., Zhou, D., et al. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3558257 · Cited by 1
“However, PnP methods typically rely on the extraction and matching of feature points, such as SIFT [27] and ORB [28].”
A Method of Image Matching Used in Image-Based Modeling System
Yi, C., Wang, X., & Xu, X. (2009) · doi:10.1109/aici.2009.32 · Cited by 1
Fathy, M., Hussein, A. S., & Tolba, M. F. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.7 · Cited by 1
Background subtraction with a moving camera
Topçu, O., Kalem, A., & Esen, E. (2013) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2013.6531577 · Cited by 1
Stratified SIFT Matching for Human Iris Recognition
Bakshi, S., Mehrotra, H., & Majhi, B. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22720-2_17 · 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