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 25,701–25,800 of 56,201
What is the right way to represent document images?Preprint
Csurka, G., Larlus, D., Gordo, A., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.01076 · Cited by 7
Logistic Regression Region Weighting for Weakly Supervised Object Localization
Wang, L.-T., Wang, T., & Hu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2935011 · Cited by 7
A Keypoint-Based Region Duplication Forgery Detection Algorithm
Emam, M., Han, Q., Liyang, Y. U., & Zhang, H. (2016) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016edl8024 · Cited by 7
IDENTIFYING LOGICAL LOCATION VIA GPS-ENABLED MOBILE PHONE AND WEARABLE CAMERA
Zhang, D., Chen, C., Zhou, Z., & Li, B. (2012) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001412600075 · Cited by 7
Discrete Semantic Matrix Factorization Hashing for Cross-Modal Retrieval
Qin, J., Fei, L., Teng, S., et al. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413037 · Cited by 7
Local Feature Based Person Detection and Tracking Beyond the Visible Spectrum
Jüngling, K., & Arens, M. (2011) · Springer eBooks · doi:10.1007/978-3-642-11568-4_1 · Cited by 6
Zhang, X., Hua, G., Zhang, L., & Shum, H. (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.2010.5540042 · Cited by 7
Image compression via sparse reconstruction
Yuan, Y., Au, O. C., Zheng, A., et al. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853954 · Cited by 7
Automatic Threat Detection in Single, Stereo (Two) and Multi View X-Ray Images
Tuli, A., Bohra, R., Moghe, T., et al. (2020) · IEEE India Council International Conference (INDICON) · doi:10.1109/indicon49873.2020.9342253 · Cited by 7
Certifiably Optimal and Robust Camera Pose Estimation From Points and Lines
Sun, L., & Deng, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3006056 · Cited by 7
Tensor decomposition of SIFT descriptors for person identification
Liu, X., & Li, P. (2012) · doi:10.1109/icb.2012.6199818 · Cited by 7
Visual recognition of paper analytical device images for detection of falsified pharmaceuticals
Banerjee, S., Sweet, J., Sweet, C. R., & Lieberman, M. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477598 · Cited by 8
SYSTEMATIC CALIBRATION FOR A BACKPACKED SPHERICAL PHOTOGRAMMETRY IMAGING SYSTEM
Rau, J. Y., Su, B. W., Hsiao, K. 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-b1-695-2016 · Cited by 7
3-D reconstruction using the Kinect sensor and its application to a visualization system
Lim, Y., Lee, H.-Z., Yang, N.-E., et al. (2012) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2012.6378311 · Cited by 7
Obstacle Detection in Drones Using Computer Vision Algorithm
Aswini, N., & Uma, S., V (2019) · Communications in computer and information science · doi:10.1007/978-981-13-5758-9_9 · Cited by 7
PSGAN: Revisit the binary discriminator and an alternative for face frontalization
Duan, Q., Zhang, L., Zhang, Y., & Gao, X.-B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.033 · Cited by 7
DeepBag: Recognizing Handbag Models
Wang, Y., Li, S., & Kot, A. C. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2480228 · Cited by 7
HWNet v3: a joint embedding framework for recognition and retrieval of handwritten text
Krishnan, P., Dutta, K., & Jawahar, C. V. (2023) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-022-00423-6 · Cited by 7
Li, L., Jiang, S., & Huang, Q. (2011) · 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.2011.5995570 · Cited by 7
Applying SIFT Descriptors to Stellar Image Matching
Ruíz-del-Solar, J., Loncomilla, P., & Zorzi, P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85920-8_75 · Cited by 7
A Cascaded Zoom-In Method for Defect Detection of Solder Joints
Zhang, Z., Wang, H., Zhou, S., et al. (2018) · International Conference on Signal Processing · doi:10.1109/icsp.2018.8652335 · Cited by 7
StickyPillars: Robust feature matching on point clouds using Graph Neural NetworksPreprint
Simon, M., Fischer, K., Milz, S., Witt, C., & Gross, H. (2020) · arXiv.org · doi:10.48550/arxiv.2002.03983 · Cited by 7
Sadeghi, B., Jamshidi, K., Vafaei, A., et al. (2018) · The Visual Computer · doi:10.1007/s00371-018-01616-z · Cited by 7
Ali, I., & Zhang, H. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981283 · Cited by 7
Component-based robot system design for grasping tasks
Mae, Y., Takahashi, H., Ohara, K., et al. (2010) · Intelligent Service Robotics · doi:10.1007/s11370-010-0072-5 · Cited by 7
Boboc, I. K. Ş., Cojocaru, A., Nedelea, G., Cătălin, B., Bogdán, M., & Călina, D. C. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241914474 · Cited by 7
Li, J., Jiang, P., Song, S., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2944852 · Cited by 7
Naito, S., Tomozawa, H., Mori, Y., Nakamura, H., & Fujiwara, H. (2018) · Journal of Disaster Research · doi:10.20965/jdr.2018.p0928 · Cited by 7
Object Evidence Extraction Using Simple Gabor Features and Statistical Ranking
Kämäräinen, J., Ilonen, J., Paalanen, P., Hamouz, M., Kalviainen, H. A., & Kittler, J. (2005) · Lecture notes in computer science · doi:10.1007/11499145_14 · Cited by 6
Storing and recalling information for vision localization
Siagian, C., & Itti, L. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543476 · Cited by 7
Appearance-based odometry and mapping with feature descriptors for underwater robots
Botelho, S. S. D. C., Drews, P., Figueiredo, M. D. S., et al. (2009) · Journal of the Brazilian Computer Society · doi:10.1590/s0104-65002009000300005 · Cited by 7
Incremental Computation of Feature Hierarchies
Felsberg, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15986-2_53 · Cited by 7
Limsoonthrakul, S., Dailey, M. N., Marikhu, R., Timtong, V., Chairat, A., Suphavilai, A., Seetamanotch, W., & Ekpanyapong, M. (2021) · Sustainability · doi:10.3390/su13031210 · Cited by 8
A hybrid image-based modelling algorithm
Nguyen, H. M., Wünsche, B. C., Lutteroth, C., et al. (2013) · ResearchSpace (University of Auckland) · Cited by 7
Spatial analysis toolkits for RNA in situ sequencing
Chen, J., & Ke, R. (2024) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1842 · Cited by 10
“D registration algorithm by first performing quantile normalization on four channels' fluorescent intensity, subsequently calculating the key points and corresponding feature vectors on each image volume (Lowe, 2004; Murray et al., 2015).”
MethodLearning to refine object boundaries
Deng, R., Chen, Z., Chen, H., et al. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126742 · Cited by 7
Composite face sketch recognition based on components
Liu, D., Peng, C., Wang, N., et al. (2016) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2016.7752734 · Cited by 7
Wang, B., Kang, Z., Dong, P., Wang, F., Ma, P., Bai, J., Liang, P., & Li, C. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-022-1205-7 · Cited by 7
Image registration using a kernel partial least squares based mismatches removal method
Zhao, W., Tian, Z., Yang, L., et al. (2016) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2016.01.001 · Cited by 7
Wireless Multimedia Sensor Network Technology
Ang, L.-M., Seng, K. P., Chew, L. W., et al. (2013) · Springer eBooks · doi:10.1007/978-3-642-38203-1_2 · Cited by 7
Real time visual SLAM using cloud computing
Ayush, K., & Agarwal, N. K. (2013) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2013.6726744 · Cited by 7
Xu, G., Li, X., Su, J. D., et al. (2012) · Optik · doi:10.1016/j.ijleo.2012.08.020 · Cited by 7
Predictive digitisation of cultural heritage objects
Pratikakis, I., Savelonas, M. A., Mavridis, P., Παπαϊωάννου, Γ., Sfikas, K., Arnaoutoglou, F., & Rieke‐Zapp, D. H. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4928-y · Cited by 7
Multi‐scale homography estimation based on dual feature aggregation transformer
Li, Y., Chen, K., Sun, S., et al. (2022) · IET Image Processing · doi:10.1049/ipr2.12722 · Cited by 7
Enhanced Facial Expression Recognition Based on Facial Action Unit Intensity and Region
Chen, W., & Wang, A. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394636 · Cited by 7
Keyword Visual Representation for Image Retrieval and Image Annotation
Nguyen, N. V., Boucher, A., & Ogier, J.-M. (2015) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001415550101 · Cited by 7
Local convolutional features and metric learning for SAR image registration
Guo, Q., Xiao, J., Hu, X., & Zhang, B. (2018) · Cluster Computing · doi:10.1007/s10586-018-1946-0 · Cited by 7
A fine-tuned multimodal large model for power defect image-text question-answering
Wang, Q., Zhang, J., Du, J., Zhang, K., Li, R., Zhao, F., Zou, L., & Xie, C. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03539-w · Cited by 8
Feature coding via vector difference for image classification
Zhao, X., Yu, Y., Huang, Y., et al. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467561 · Cited by 7
Structured-Patch Optimization for Dense Correspondence
Qin, X., Shen, J., Mao, X., et al. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2395078 · Cited by 7
Macro-micro mechanics of Fujian River sand under triaxial shear using X-ray micro-tomography
Xiong, W., Wang, J., & Zhu, Z. (2025) · Canadian Geotechnical Journal · doi:10.1139/cgj-2024-0185 · Cited by 7
A machine learning-based mobile robot visual homing approach
Zhu, Q., Ji, X., Cai, C., et al. (2018) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpas.2018.124278 · Cited by 7
Heterogeneous spatial data: fusion, modeling, and analysis for GIS applications
Patanè, G., & Spagnuolo, M. (2016) · CERN Document Server (European Organization for Nuclear Research) · Cited by 7
Robust Tracking Occluded Human in Group by Perception Sensors Network System
Le, A. V., & Choi, J. (2017) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0667-6 · Cited by 7
Paramanik, B., Joseph, A., & Kumar, R., V (2024) · International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) · doi:10.1109/tqcebt59414.2024.10545296 · Cited by 7
“…involves a wide range of applications such as bowling action legality analysis using K-nearest neighbour, bowler action classification using VGG-16 [16] utilising Inception V3 to enable autonomous third umpire decisions, and utilising K-nearest neighbour to analyse the legality of bowling actions.”
MethodArranging and Interpolating Sparse Unorganized Feature Points With Geodesic Circular Arc
Xie, S., Zeng, D., Zhou, Z., et al. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2010146 · Cited by 7
Experimental Verification of a Walking Robot Self-Localization System with the Kinect Sensor
Nowicki, M., & Skrzypczyński, P. (2013) · Journal of Automation Mobile Robotics & Intelligent Systems · doi:10.14313/jamris_4-2013/43 · Cited by 7
Segmentation-Driven RetinaNet for Protozoa Detection
Pho, K., Amin, M. K. M., & Yoshitaka, A. (2018) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2018.00062 · Cited by 7
Dynamic and asymmetric colloidal molecules
Fang, H., Gao, Q., Rong, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58057-1 · Cited by 7
Automated cell junction tracking with modified active contours guided by SIFT flow
Lee, C.-Y., Kang, S., Chisholm, A. D., et al. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6867866 · Cited by 7
Hyperspectral band selection using structural information via hierarchical clustering
Delibaşoğlu, İ., & Çetin, M. (2019) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.13.014526 · Cited by 7
Self-Supervised Human Pose based Multi-Camera Video Synchronization
Yin, L., Han, R., Feng, W., et al. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547766 · Cited by 7
Who’s Counting? Real-Time Blackjack Monitoring for Card Counting Detection
Zutis, K., & Hoey, J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04667-4_36 · Cited by 7
Video luminance transient improvement using difference-of-Gaussian
Wang, Y., Chen, X., Han, H., et al. (2009) · Asia-Pacific Conference on Communications (APCC) · doi:10.1109/apcc.2009.5375645 · Cited by 7
Learning heterogeneous data for hierarchical web video classification
Liu, X., Yao, H., Ji, R., Xu, P., Sun, X., & Tian, Q. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072355 · Cited by 8
The Fraunhofer IDMT at ImageCLEF 2011 Photo Annotation Task
Nagel, K., Nowak, S., Kühhirt, U., et al. (2011) · Cited by 7
SPROSAC: Streamlined progressive sample consensus for coarse–fine point cloud registration
Liu, Z., Yue, X., & Zhu, J. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05400-6 · Cited by 10
“The commonly used strategy for feature matching is the nearest neighbor distance ratio [53], which is based on various distance calculation meth-ods, such as the Euclidean distance, Manhattan distance, and Chebyshev distance.”
Privacy-Preserving Image Retrieval Based on Thumbnail-Preserving Visual Features
An, D., Zhang, X., Hao, D., et al. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3550782 · Cited by 7
“Lowe et al. [28] introduced the SIFT algorithm, which has scale and rotation invariance but has a relatively high computational cost and a large amount of time.”
Analysis of Feature Point Distributions for Fast Image Mosaicking Algorithms
Behrens, A., & Röllinger, H. (2010) · Acta Polytechnica · doi:10.14311/1219 · Cited by 7
Temporal Aggregation of Visual Features for Large-Scale Image-to-Video Retrieval
García, N. (2018) · International Conference on Multimedia Retrieval · doi:10.1145/3206025.3206083 · Cited by 8
Inter-Sequence Error Concealment Techniques for Multi-Broadcast TV Reception
Troger, T., & Kaup, A. (2011) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2011.2173813 · Cited by 7
Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation
Proffitt, R. M., Ma, M., & Skubic, M. (2023) · Sensors · doi:10.3390/s23187872 · Cited by 7
Efficient Change Detection for Very Large Motion Blurred Images
Rengarajan, V., Punnappurath, A., Rajagopalan, A. N., et al. (2014) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2014.55 · Cited by 7
Guo, H., Xiao, H., Wang, S., et al. (2015) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2015.7237740 · Cited by 7
Local Patch Vectors Encoded by Fisher Vectors for Image Classification
Chen, S., Liu, H., Zeng, X., Qian, S., Wei, W., Wu, G., & Duan, B. (2018) · Information · doi:10.3390/info9020038 · Cited by 7
Markerless tracking using interest window for augmented reality applications
Hamidia, M., Zenati-Henda, N., Belghit, H., et al. (2014) · doi:10.1109/icmcs.2014.6911133 · Cited by 7
A Method of Improving SIFT Algorithm Matching Efficiency
Zhu, D., & Wang, X. (2009) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2009.5304375 · Cited by 7
Diffusion Geodesic Path: A Common Feature for Automatic Image-Map Registration
Tian, L., & Kamata, S. (2006) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2006.270933 · Cited by 7
Tiwari, V., & Jain, S. C. (2019) · Evolutionary Intelligence · doi:10.1007/s12065-019-00205-w · Cited by 8
Combinatorial clustering and the beta negative binomial processPreprint
Broderick, T., Mackey, L., Paisley, J. W., et al. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1111.1802 · Cited by 7
Esparza, D. M. C., Terven, J. R., Romero-González, J.-A., et al. (2020) · Applied Sciences · doi:10.3390/app10248851 · Cited by 7
Proximate sensing using georeferenced community contributed photo collections
Leung, D., & Newsam, S. D. (2009) · doi:10.1145/1629890.1629903 · Cited by 7
An Attention Selection Model with Visual Memory and Online Learning
Guo, C., & Zhang, L. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371145 · Cited by 7
Zhu, W., Yang, X., Liu, R., & Zhao, C. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103698 · Cited by 7
Early stages of LiDAR data processing
Pfeifer, N., & Böhm, J. (2008) · CRC Press eBooks · doi:10.1201/9780203888445.ch13 · Cited by 7
Object Recognition by Modified Scale Invariant Feature Transform
Gul-e-Saman, & Gilani, S. A. M. (2008) · International Workshop on Semantic Media Adaptation and Personalization · doi:10.1109/smap.2008.12 · Cited by 7
Zankowski, S. P., Wheeler, S., Barthelay, T., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69002-1 · Cited by 7
Camouflage in benthic cephalopods: what does it teach us?
Josef, N., & Shashar, N. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139058964.012 · Cited by 8
Vision-Based Automatic Identification Tracking of Steel Products for Intelligent Steel Manufacturing
Hsu, C.-Y., Kang, L., You, T.-Y., et al. (2017) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2017.75 · Cited by 7
Integrating Dashcam Views through Inter-Video Mapping
Chen, H., Chen, Y., Lee, W., et al. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.356 · Cited by 7
STDSD: A Spacecraft Target Detection Framework Considering Similarity and Diversity
Liu, Y., Zhou, R., Yao, Z., She, J., & Qi, N. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3531720 · Cited by 7
ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition
Xie, W., Luo, L., Ye, N., et al. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801556 · Cited by 7
On the Use of Computer Vision for Numismatic Research
Zambanini, S., Kampel, M., & Schlapke, M. (2008) · Eurographics · doi:10.2312/vast/vast08/017-024 · Cited by 7
Guo, X., Hou, B., Wu, Z., Ren, B., Wang, S., & Jiao, L. (2022) · Remote Sensing · doi:10.3390/rs14133042 · Cited by 7
Content-Based Filtering for Fast 3D Reconstruction from Unstructured Web-Based Image Data
Makantasis, K., Doulamis, A. D., Doulamis, N. D., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13695-0_9 · Cited by 7
An all‐hardware implementation of the subpixel refinement stage in SIFT algorithm
Rubio‐Ibáñez, P., Ruı́z, R., Doménech‐Asensi, G., et al. (2018) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.2482 · Cited by 7
Children's Drawing Psychological Analysis using Shallow Convolutional Neural Network
Yuan, Y., Huang, J., Ma, X., et al. (2020) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00120 · Cited by 7
SPECIES IDENTIFICATION FOR AQUATIC BIOMONITORING USING DEEP RESIDUAL CNN AND TRANSFER LEARNING
Milosavljević, A., Milošević, D., & Predić, B. (2021) · Facta Universitatis Series Automatic Control and Robotics · doi:10.22190/fuacr201118001m · Cited by 7
Heterogeneous multisensor fusion for mapping dynamic environments
Huang, G. P., Rad, A. B., Wong, Y.-K., et al. (2007) · Advanced Robotics · doi:10.1163/156855307780108268 · Cited by 7
MP-SfM: Monocular Surface Priors for Robust Structure-From-Motion
Pataki, Z., Sarlin, P.-E., Schönberger, J. L., & Pollefeys, M. (2025) · 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/cvpr52734.2025.02039 · Cited by 15
“Approaches: Among those based on sparse correspondences, we consider COLMAP [50], with correspondences estimated by SIFT [38] and by SuperPoint [13] and Light-Glue [36], as well as extensions for structure-less resection-ing (SLR) [73] and global SfM with GLOMAP [43].”
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