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 3,760
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 2,360
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 2,030
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 52,201–52,300 of 56,204
Vadivukarassi, M., Puviarasan, N., & Aruna, P. (2018) · International Journal of ChemTech Research · doi:10.20902/ijctr.2018.110841
Removing Spinning Motion from Spherical Video Using a Rotationally Invariant Feature Extractor
Slevin, M., & Cuffe, P. (2019) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2019.8926884
Unidentified Floating Object detection in maritime environment using dictionary learningPreprint
Venkatrayappa, D., Desolneux, A., Hubert, J.-M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.15757
Low-bitrate benefits of JPEG compression on sift recognition
Elhoseiny, M., Song, B., Sudol, J., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738754
AGM: Adaptive Graph Enhanced Graph Matching for Planar Tracking
Huang, S., Wang, T., Zhao, G., et al. (2023) · doi:10.1109/paap60200.2023.10391335
Contour-Guided Feature Selection for Visual Relocalization
Lin, H.-C., Lin, Y.-H., HONG, M.-C., et al. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3664619
“In feature matching, a long used technique, feature detectors are employed to extract local keypoints from images [8], [9], [10], after which their descriptors are computed and a three-dimensional (3D) point cloud model is constructed through Structure from Motion (SfM).”
Structure and camera calibration from active correspondence of lines and intersection points
Gatla, C. S. (2010) · UNM’s Digital Repository (University of New Mexico)
Günzel, L., Osanen, J. E., Løvås, H. S., Johnsen, G. H., García-Ortiz, A., & Ludvigsen, M. (2025) · IEEE Underwater Technology (UT) · doi:10.1109/ut61067.2025.10947375
Upper airways segmentation using principal curvatures
Bustacara-Medina, C. J. (2020) · doi:10.11144/javeriana.10554.40991
Cross-view geo-localization through consistency-guided transformer and adversarial feature alignment
Chen, J., He, Y., Xiong, L., Yang, Y., & Fu, B. (2026) · International Journal of Remote Sensing · doi:10.1080/01431161.2026.2731638
Nwana, A. O., & Chen, T. (2016) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2016.0016
Towards human interaction analysis
Gavari, M. A. (2013) · UPCommons institutional repository (Universitat Politècnica de Catalunya)
Wang, F., & Zhang, S. (2012) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2012.6376744
(2017) · doi:10.1145/3122865.3122866
Bakkay, M. C., Chambon, S., Rashwan, H. A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06663
HomoMamba for Self-supervised Homography Estimation
Zhong, J., Wang, J., Hou, K., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9856-1_27
Perception: Insights from the sensori-motor approach
Hafemeister, L., Gaussier, P., Maillard, M., et al. (2010) · European Workshop on Visual Information Processing · doi:10.1109/euvip.2010.5699148
Video change detection in web processing service
Yang, X., & Mao, J. (2013) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2013.6818211
Aboutalib, S., & Veloso, M. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354330
An integrated graph-based face segmentation approach from Kinect videos
Zhang, J., Wang, H., Liu, S., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738563
Fuzzy Cognitive Maps: Learning Algorithms and Biomedical Applications
Chen, Y. (2015) · OhioLink ETD Center (Ohio Library and Information Network)
Tang, F., & Zhang, Q. (2024) · International Journal of Computer Science and Information Technology · doi:10.62051/ijcsit.v2n1.20
“Object detection technology, characterized by its rapid response, high accuracy, and non-contact nature, is well-suited for the dynamic and complex environments of drilling operations[5].”
BackgroundMethodEnergy-Guided Automatic Prompting for Segment Anything Model Using Point of Interest Detection
Huynh, T. P., Vo, T. V., & Huynh, H. X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2885-0_29
VSLG-net: visual-spatial latent graph network for two-view correspondence learning
Lv, W., Guo, H., Shen, Z., et al. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05642-6
“In this study, we employ established feature extraction techniques, such as the traditional SIFT [34], to create an N × 4 set of putative feature correspondences C , which serves as input to the neural network.”
Designing a Marker Set for Vertical Tangible User Interfaces
Attard, G., Raffaele, C. D., & Smith, S. (2021) · doi:10.1109/icit52682.2021.9491659
Model-based Pose Estimation for Texture-less Objects with Differential Evolution Algorithm
Tao, L., Nguyen, T. V., & Hasegawa, H. (2017) · MATEC Web of Conferences · doi:10.1051/matecconf/201710815001
Method of student identification in the context of college classroom
Sun, B., Guo, M., He, J. Y. D., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2235675
Multiframe superimposed pilot-based reference frame estimation for display field communication
Singh, P., Lee, D.-I., & Jung, S. (2025) · Physical Communication · doi:10.1016/j.phycom.2025.102905
Filtering Feature Mismatches using Multiple Descriptors
Kim, J.-Y., & Jun, H. (2014) · Journal of the Korea Society of Computer and Information · doi:10.9708/jksci.2014.19.1.023
MUFeat: Multi-Level CNN and Unsupervised Learning for Local Feature Detection and Description
Kuo, S.-H., Wu, T.-H., Chen, Z.-Y., et al. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342482
Song, H., Wang, C., Shen, K., et al. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055905
Aesthetics-Diven Online Summarization to First-Person Tourism Videos
Shao, Y., Guo, B., Zhang, Y., et al. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-2385-4_24
Moradipour, H., Ashtiani, H., & Aliabadian, A. (2010)
Refining local descriptors by embedding semantic information for visual categorization
Zheng, Y., Wei, R., Lu, H., et al. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072020
Shape retrieval and matching based on line segments.
Chi, Y. L. (2007) · doi:10.32657/10356/2478
Kim, S., Malik, S., Lipka, N., et al. (2017) · doi:10.1145/3041021.3054256
Model Optimization Methods for Real-time 2D Human Pose Estimation Applied to Robots: A Survey
He, W., Zeng, B., Liu, J., Ye, X., & Zhou, F. (2023) · International Conference on Robotics, Automation and Intelligent Control (ICRAIC) · doi:10.1109/icraic61978.2023.00060
Enhancing Alzheimer's Diagnosis with Pre-trained Embeddings and Attention Mechanisms
Hasan, M. S., Badhon, A. I. M., & Alam, M. G. R. (2025) · International Conference on Software Engineering and Information Management (ICSIM) · doi:10.1145/3725899.3725932
“Despite the differences between natural and medical images, image classifications established for object identification in image features, such as the Scale-Invariant Feature Transform (SIFT) introduced in [21.] and the Histogram of Oriented Gradients (HOG) introduced in [22.]”
Emotion Detection Using Hybrid Structural and Appearance Descriptors
Sanchez-Mendoza, D., Masip, D., Baró, X., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41550-0_10
YOLO-Point: Effective Key Points Detection for Optimal Break Points on Vehicle Windows
Zhang, W., Zhang, S., Du, G.-X., et al. (2024) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi61353.2024.10778796
“Historically, traditional methods such as Harris corner detection [1] and feature descriptors like SIFT [2] (Scale-Invariant Feature Transform) and SURF [3] (Speeded-Up Robust Feature) dominated key points detection.”
Nagy, A., & Hollósi, J. (2025) · doi:10.3390/engproc2025113016
“Keypoints were extracted using detectors such as SIFT [8], ORB [9], BRISK [10], or AKAZE [11], described with BRIEF [13], FREAK [14], RootSIFT [15], or BEBLID [16], and matched using either Brute-Force [17] or FLANN [18].”
BMAX: A Bag of Features Based Method for Image Classification
Senna, P., Drummond, I. N., & Bastos, G. S. (2015) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2015.24
A Generic Middle Layer for Image Understanding
Terzić, K. (2011)
Chaudhary, P., Foley, C., Samiappan, S., Kohler, L., Senyurek, V. Y., Boes, D., & Chakrabarti, P. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.102005
Hybrid BYOL-ViT: Efficient approach to deal with small datasetsPreprint
Naimi, S., van Leeuwen, R., Souidene, W., & Saoud, S. B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.04845 · Cited by 2
Radiation-Induced Noise Resilience of Neuromorphic Architectures
Roffe, S. S., Akolkar, H., George, A. D., & Benosman, R. B. (2023) · Journal of Aerospace Information Systems · doi:10.2514/1.i011192
Visual Localization Evaluation Focused on Feature Extraction and Matching
Otake, Y., Hara, Y., & Nakamura, S. (2026) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim65483.2026.11658112
“While handcrafted features such as SIFT [18] have been widely used in the past, many learning-based methods have been proposed in recent years.”
Kumar, N., Chauhan, A., & Malhotra, M. V. (2023) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-9757
A Kinect-Based Front-End for Graph-SLAM Using Plane Matching in Planar Indoor Environments
Yuan, Z., Rosa, S., Russo, L. O., & Bona, B. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-08338-4_29
A GEOGRAPHIC DATA GATHERING SYSTEM FOR IMAGE GEOLOCALIZATION REFINING
Semaan, B., Servieres, M. C. J., Moreau, G., et al. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-4-w3-63-2017
Recent Advances in Feature Extraction and Description Algorithms
Salahat, E. N., & Qasaimeh, M. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5204-8.ch002
Dimensionality reduction algorithms with applications to collaborative data and images
Shelton, C. R., & Mei, G. (2008)
MMSVC: An efficient unsupervised learning approach for large-scale datasets
Gu, H., Zhao, G., & Zhang, J. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.04.028
Missile-borne SAR image scene matching based on improved ORB algorithm
Ma, C., Hu, X., Duan, H., et al. (2017) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2017.8282974
Smart Dietary Planning with Food Recognition and Calorie intake Estimation
Naveen, H. S. (2025) · International Journal of Innovative Research in Advanced Engineering · doi:10.26562/ijirae.2025.v1204.05
Machine learning techniques to build geometrical transformations for object matching a review
Jadhav, P. A., & Chatur, P. N. (2016) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs.2016.7586381
Semantic Line Map: A Novel Visual Map Representation Scheme for Autonomous Driving Systems
Zhu, D., He, D., Zhou, Y., et al. (2023) · doi:10.1109/icoias61634.2023.00010
Ruiz‐Hidalgo, J., Borsum, M., Kochale, A., et al. (2013) · doi:10.1002/9781118706350.ch8
Boosting VLAD with Weighted Fusion of Local Descriptors
Zhang, C., Zhao, Q., Liu, H., et al. (2018) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2018.00014
Visual location recognition in man-made and natural environments
Baatz, G. (2013) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-009770924
Image Demoiréing With Image Classification Priors
Song, M., & Kim, H. (2026) · IEEE Access · doi:10.1109/access.2026.3734076
Assam, A. (2013) · University of Birmingham Institutional Research Archive (University of Birmingham)
Zhao, W., Wang, X., Lin, X., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5957784
Advancing Image Registration with Multi-angle Projection Keypoint Descriptors
Lai, Y. H., & Lin, D.-T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88217-3_7
Li, S., Jiang, Y., Zhang, Y., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2199216
Performance Analysis of Computer Vision Based Feature Detectors for 3D Laser Rasterized Images
Ali, W., Liu, P., & Ying, R. (2021) · International Conference on Automation, Control and Robots (ICACR) · doi:10.1109/icacr53472.2021.9605179
Mosaicing of Fetoscopic Acquired Images using SIFT and FAST
Fransson, S. (2017)
Nakabayashi, T., KAMIYAMA, K., KUROSAWA, N., et al. (2013) · AIJ Journal of Technology and Design · doi:10.3130/aijt.19.775
Enhanced infrared ship detection via AAF-YOLO: a lightweight and accurate approach
Li, Z., Zhang, D., Xu, M., Su, Y., & Xu, Z. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08419-3
Multi-Scale Feature Pyramid Network With Camera Artifact Augmentation for Pedestrian Detection
Shrivastava, A., & Poonkuntran, S. (2025) · IEEE Access · doi:10.1109/access.2025.3556756
Spatial Relationship Constrained Binocular Object Matching and Localization Algorithm
Gong, P., Zheng, K., Liu, T., et al. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11559880
Stability of Feature Detection Algorithms in Low Quality Video Encoding
Luca, V. D., & Paolis, L. T. D. (2023) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine58569.2023.10405756
pour l'apprentissage non supervis e
Bourrier, A. (2014)
Semantic-Guided Underwater Visual Enhancement and Robust Feature Matching
Yang, J., Geng, H., Ge, X., et al. (2026) · International Conference on Signal Image Processing and Communication (ICSIPC) · doi:10.1109/icsipc69751.2026.11584094
“Traditional methods such as SIFT, SURF, and ORB perform well in common scenes [9], but in underwater environments they often suffer from insufficient keypoints and high mismatch rates due to low contrast, texture degradation, and suspended particle interference [10].”
Fast and efficient computation of stereo depth maps
Ghosh, P., & Venkatesh, K. S. (2013) · doi:10.1109/3dtv.2013.6676651
Tröger, T., Hirschbeck, M., & Kaup, A. (2011)
Hyperspectral Image-Based Feature Point Matching for High-Performance Novel View Synthesis
Yuguchi, T., Houjho, K., Fuse, R., et al. (2025) · International Conference on Frontiers of Signal Processing (ICFSP) · doi:10.1109/icfsp67350.2025.11353896
“Keypoint detectors and descriptors such as SIFT [7], AKAZE [8], and ORB [9] have been widely adopted due to their robustness to scale and rotation changes.”
Multifeature Fusion for Facial Expression Recognition
Wunake, P., Boante, L. M., Wilson, M. S., et al. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-2053-8_12
Image Processing Techniques in Vision-Based Robot Navigation
Munguía, R., Bolea, Y., & Grau, A. (2018) · International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) · doi:10.1109/iccairo.2018.00036
Image-Based Target Tracking of Observable Features in Precision Projectiles
Fairfax, L. D. (2014) · AIAA Atmospheric Flight Mechanics Conference · doi:10.2514/6.2014-2196
KRAFT: Keypoint Robust and Adaptive Feature Tracking
Venjarski, J., Rozinaj, G., Minárik, I., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3649147
Tong, B., Chen, W., Du, L., Wang, H., Yan, X., & Si, F. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3711340
Face detection of migrating learning based on constrained scene
Ding, X., Chen, J., Lei, Y., et al. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224501011
Prior surface model guided dense reconstruction via graph-cuts
Zuo, W., Ding, J., & Liu, J. (2014) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2014.7053271
Li, K., Dai, Z., Huang, X., Dong, S., & Wang, S. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131297
Comparing Feature Matching Algorithms for Measuring In-Plane Strains on Civil Infrastructures
Feng, Y., Dai, F., & Zhao, J. (2017) · doi:10.24928/jc3-2017/0165
Delving into the Depths of Image Retrieval Systems in the Light of Deep Learning: A Review
Khan, O. R., & Bhat, J. I. (2023) · Indian Journal of Science and Technology · doi:10.17485/ijst/v16i34.1341
Copy Detection Using Graphical Model
Wei, S., Zhao, Y., & Zhu, Z. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-1891-6.ch014
Human Action Recognition in Videos with Shallow and Deep Learning
Phan, T. H. H. (2019) · theses.fr (ABES) · doi:10.70675/fa813329z9321z4fc2za918zd4282f6ec3ac
Peng, L., & Yan, R. (2025) · SpringerBriefs in computer science · doi:10.1007/978-981-96-7898-3_1
Fan, Y., & Li, M. (2013) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-06-2013-0029
3D Shape Retrieval Using Point Spatial Distributions on the Surface
Liu, Z., Mitani, J., Fukui, Y., et al. (2007)
What and where you have seen? Bag of Words-based local feature pooling for visual event detection
Kumar, N., & Sukavanam, N. (2019) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1117/12.2523250
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
