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 52,101–52,200 of 56,204
Point cloud data enhancement based on homomorphic filter
Yang, H., & Chen, J. (2014) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2014.6933617
Multi-modal Datasets for Super-resolutionPreprint
Li, H., Quan, W., Yan, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.05804
Development of laser speckle metrology and its identification techniques
Yeh, R.-N., Sung, P.-Y., Yeh, C., et al. (2011) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2011.6016876
Feature Point Extraction Method of X-Ray Image Based on Scale Invariant
Wang, Y. W., Zhang, S. Q., Lin, B., et al. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.274.667
Joint Optical Flow and Temporally Consistent Semantic SegmentationPreprint
Hur, J., & Roth, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.07716
Fast Detection Method of Chemical Plant Equipment Based on Improved YOLOv5 Network
Cao, K., Zhang, G., Chen, Y., et al. (2022) · International Conference on Mechanical and Electronics Engineering · doi:10.1109/icmee56406.2022.10093450
Ji, F., Xiao, G., & Sun, N. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625503840
Segmentation-Guided Homography Estimation for Long-Term Planar Tracking
Šerých, J., & Matas, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37595-7_4
Novel Filtering Methods for Image and Video Processing Applications
Ibrahim, M. T. (2021) · doi:10.32920/ryerson.14662023
A SuperPoint-based Method for Automatic Stitching of Corneal Nerve Microscopy Images
Li, T., Li, G., Li, F., et al. (2022) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3532213.3532314
Prognosis of Structural and Materials Health in Heritage Conservation
Groves, R. M. (2016) · PHM Society European Conference · doi:10.36001/phme.2016.v3i1.1645
Pojar, D., Jeong, P., & Nedevschi, S. (2010) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2010.5606435
Improved methods for rapid and scalable tissue clearing and labeling
Murray, E. M. (2016) · DSpace@MIT (Massachusetts Institute of Technology)
Graphical models for segmenting images, volumes and videos
Bhole, C., Pal, C., & Kautz, H. A. (2013) · UR Research (University of Rochester)
Research on saliency detection algorithm of rural homestead
Chen, X., Li, W., Gao, J., et al. (2022) · International Conference on Algorithms, Microchips, and Network Applications (AMNA) · doi:10.1117/12.2636770
Konno, A., SEKIYA, H., & Ashiya, H. (2018) · Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) · doi:10.2208/jscejcei.74.ii_41
Image Impression Estimation by Clustering People with Similar Tastes
Kojima, B., Komamizu, T., Kawanishi, Y., Doman, K., & Ide, I. (2023) · doi:10.23919/mva57639.2023.10216055
Hyperspectral Imaging-based Change Detection in Paintings
Chang, C.-L., & Zhu, Z.-Y. (2026) · International Conference on Computer Communication and the Internet (ICCCI) · doi:10.1109/iccci70321.2026.11666501
“…steps: 1) Image Registration : Due to potential repositioning errors between the two scanning periods, the Scale-Invariant Feature Transform algorithm (SIFT) [9] combined with Random Sample Consensus (RANSAC) [10] is utilized to accurately align the bitemporal local images in spatial dimensions.”
Boissenin, M. (2009) · SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University)
Image features correspondence based on voting of geometric constraint
Han, L. (2012) · Journal of Computer Applications · doi:10.3724/sp.j.1087.2011.01595
Data clustering and graph-based image matching methods
Fang, Y. J. (2012) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)
SConE: Siamese Constellation Embedding Descriptor for Image MatchingPreprint
Trzcinski, T. P., Komorowski, J., Dąbała, Ł., Czarnota, K., Kurzejamski, G., & Lynen, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.11054
Clustering of count data using nested multinomial Dirichlet finite mixture model and its extensions
Alkhawaja, F., Amayri, M., & Bouguila, N. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-026-01250-7
Improvement of SURF Feature Image Registration Algorithm Based on Cluster Analysis
Long, X., Qiang, C. H. E. N., Xiaoya, G. A. O., et al. (2014) · DOAJ (DOAJ: Directory of Open Access Journals)
IVDA: Intelligent Real-Time Video Detection Agent for Virtual Classroom Presentation
Xu, R. Y. D., Jin, J. S., & Allen, J. G. (2005) · Advanced Technology for Learning · doi:10.2316/journal.208.2005.2.208-0844
ACFR-Net: Multi-modal image registration network via attention to common features
Zhang, X., Hu, Q., Li, G., et al. (2026) · Infrared Physics & Technology · doi:10.1016/j.infrared.2026.106791
Design of Image Classification System Based on Feature Extraction
Guo, G. N., Chu, M., Wang, X., et al. (2011) · Key engineering materials · doi:10.4028/www.scientific.net/kem.474-476.1859
Implementierung und Evaluierung von SIFT auf der GPU
von der Heidt, S.-R. (2007)
Incremental Tag Suggestion for Landmark Image Collections
Chimlek, S., & Piamsa-nga, P. (2016) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v6i1.pp139-150
Shahbaz, H., Ali, A., Rashid, A., et al. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3543
Sumiharto, R., Putra, R. G., & Demetouw, S. (2020) · International Journal of Computer Science and Software Engineering · doi:10.47277/ijcsse/9(1)1
Directional geometric histogram feature extraction and applications
Han, H. W., & Gou, J. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4729-3
Molecular architecture and spatial organization of proteasomes in the human sperm nucleus
Kolata, P., dos Santos, Á., Knowles, O., Dendooven, T., & Allegretti, M. (2026) · Nature Structural & Molecular Biology · doi:10.1038/s41594-026-01870-z
“Briefly, contrast-limited adaptive histogram equalization was used to enhance contrast 89 ; then, images were aligned using the SIFT algorithm 90 .”
Bridging the Gap Between Unsupervised and Supervised Models with Self-supervised LearningPreprint
Humbert, W., & Nadine, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5153873
Chen, Z., Zhou, J., Gao, D., et al. (2026) · Measurement · doi:10.1016/j.measurement.2026.120581
A Review of Registration Methods on Mobile Robots
Morell, V., Orts‐Escolano, S., García‐Rodríguez, J., et al. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2672-0.ch009
Feature Reduction and Noise Removal in SURF Framework for Efficient Object Recognition in Images
Ejaz, N., Baik, R., & Baik, S. W. (2012) · Lecture notes in electrical engineering · doi:10.1007/978-94-007-5860-5_63
Expression recognition in the wild with transfer learning
Xia, T., Zhang, Y., Liu, Y., & Sun, Y. (2017) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2017.8170910
Li, F., Zhang, G., Zhang, R., et al. (2025) · International Journal of Remote Sensing · doi:10.1080/01431161.2025.2600096
“The traditional 3D reconstruction methods involve extracting feature points from multiple images using algorithms such as SIFT (Scale Invariant Feature Transform) (Lowe 2004), establishing matching relationships between views, and determining the camera’s intrinsic and extrinsic parameters.”
Object Detection with Semi-local Features
Sorschag, R. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-36530-0_3
Aregawi, T. A., Kwon, O., Park, S.-Y., et al. (2013) · Journal of Sensor Science and Technology · doi:10.5369/jsst.2013.22.6.393
Use of Multiple Low Level Features to Find Interesting Regions
Borck, M., WEST, G. A. W., & Tan, T. (2014) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0004827506540661
Zhang, J., Cui, X., Li, J., et al. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3632889
Vehicle Detection in Stereoscopic Images Using Symmetry-Based Approach
Soufiane, E. A., & Zebbara, K. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-65038-3_14
Wirth, J., Chernysheva, A., Lemke, B., Giray, I., Lavandera, A. E., & Steiger, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.07.641860
Liang, Y., Ma, D., & Wu, X. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3688165
Effectiveness feature for micro-expression recognition
Le, T. T. Q., & Rege, M. (2021) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri51335.2021.00057
Semantic-Aware Monocular Point-Line Visual Odometry in Dynamic Environments
Wen, H., Chen, X., Xu, L. J., Ren, X., & Wang, X. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865087
Liu, T., Liu, Y., Liu, K., et al. (2019) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai48974.2019.9010534
Investigation of the effect of face regions on local shape descriptor based 3D face recognition
İnan, T., & Halıcı, U. (2013) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2013.6531497
ALGORITHM OF VARIATIVE FEATURE DETECTION AND PREDICTION IN CONTEXT-DEPENDENT RECOGNITION
Mukhina, M. P., & Barkulova, I. V. (2018) · Electronics and Control Systems · doi:10.18372/1990-5548.55.12785
Broader Application of the Time‐SIFT Method
Feurer, D., Bemis, S. P., Coulouma, G., et al. (2024) · doi:10.1002/9781394306657.ch1
Mobile Random Video Chat: Understanding User Behavior and Misbehavior Detection
Tian, L. F. (2016) · CU Scholar (University of Colorado Boulder)
Payandeh, S. (2016) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315367484-8
The 3D Geometric Information Acquisition Algorithm using Virtual Plane Method
Park, S. (2009) · Journal of Institute of Control Robotics and Systems · doi:10.5302/j.icros.2009.15.11.1080
Evaluation of Medical Image Registration by Using High-Accuracy Image Matching Techniques
Li, Z., & Kurihara, T. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8498-1_19
Buffat, J., Elibol, A., Scharr, H., et al. (2026) · SoftwareX · doi:10.1016/j.softx.2026.103065
Space camera optical axis pointing precision measurement system
Chen, G., Meng, F., Yang, Z., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2213722
Geometric Correspondence Consistency in RGB-D Relative Pose Estimation
Kumar, S., Chien, C.-H., & Kimia, B. B. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00048
“A robust feature-based estimation process typically follows a three-step paradigm [11], namely, ( i ) detect and extract features, e.g. , SIFT [24] or SuperPoint [8]; ( ii ) measure pairwise feature similarity and form a rank-ordered list of potential matches; ( iii ) apply RANSAC by selecting a…”
Attribute reduction for SIFT local descriptors using PCA and CAIM
Zhao, J. Y., Guo, H., & Wu, J. (2014) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2014.7003790
Multimodal Remote Sensing Image Match Based on Structure Tensor Feature Transform
Zhang, T., Wang, F., Huang, X., et al. (2022) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec55388.2022.10019939
Four-level video summary coding
Fu, Z., Lu, H., Deng, N., et al. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5648288
AUTOMATED MOSAICKING OF MULTIPLE 3D POINT CLOUDS GENERATED FROM A DEPTH CAMERA
Kim, H., Yoon, W., & Kim, T. (2016) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xli-b3-269-2016
An optimized FPGA implementation based on scale invariant feature transform feature points detection
Gu, Y. Q., Qu, X., Sun, Y., et al. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603266
이종환, 강승헌, 이만희, et al. (2014)
Skewness map: estimating object orientation for high speed 3D object retrieval system
Aoki, T., Sintunata, V., & Kaminishi, K. (2018) · International Journal of Intelligent Engineering Informatics · doi:10.1504/ijiei.2018.10012062
Video-Based Camera Localization Using Anchor View Detection and Recursive 3D Reconstruction
Taira, H., Onbe, K., Miyashita, N., et al. (2021) · doi:10.23919/mva51890.2021.9511397
High dynamic range lensless on-chip microscopy via an overexposure inverse-projection operator
Wu, Z., Gao, S., Li, Y., et al. (2026) · Applied Physics Letters · doi:10.1063/5.0339793
Real-Time Visual Tracking Using a New Weight Distribution
Shi, H. W., Li, C., & Jin, T. (2011) · doi:10.1109/isccs.2011.47
Deep Learning-Based Image Registration for Train Chassis
Zhou, K., Li, J., Zhang, W., et al. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-4897-2_39
Joint Sparse Coding Spatial Pyramid Matching for Classification of Color Blood Cell Image
Shi, J., & Cai, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02267-3_30
Sparsity and phase collapse in deep convolutional networks
Zarka, J. (2022) · theses.fr (ABES) · doi:10.70675/4349d1b1z1f33z4ae2z9c0bza72efb3287fd
PICCOLO: Point Cloud-Centric Omnidirectional Localization
Kim, J., Choi, C., Jang, H., & Kim, Y. M. (2021) · Seoul National University Open Repository (Seoul National University) · doi:10.1109/iccv48922.2021.00330 · Cited by 15
Advanced SeqSLAM using Discriminative Information for Light-Rail Localization at High Frame Rate.
Meng, Y., Jia, K., & Siu, W.-C. (2019) · J. Inf. Hiding Multim. Signal Process.
Identifying authoritative sources of multimedia content
Kennedy, L. S., & Slaney, M. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2071975
Sri lanka driving license forgery detection
Samarasinghe, P. D., Lakmal, L. K. P., Weilkala, A. V., et al. (2017) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip.2017.8313795
Susaki, J., Mishra, B., & Ota, Y. (2013) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse.2013.6550652
A Novel Extracting Blob-like Object Method Based on Scale-Space
Chen, W., Wang, X., Li, Q., et al. (2011) · doi:10.1109/icic.2011.25
Fast and Generalisable License Plate Re-identification using Neural Embedding of Fisher Vectors
Kumar, A., Gupta, S. S., Kozitsky, V., & Madhvanath, S. (2018) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3293353.3293402
Cognitive-inspired visual navigation system
Mukawa, M. (2017) · doi:10.32657/10356/72658
From Semantics to Geometry: Location-Prior-Free Cross-View Geo-Localization in Urban EnvironmentsPreprint
wang, S., Chen, B., Lü, Z., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6826579
INR-TEM: Robust cavity detection in multifocus TEM images via implicit neural representations
Hsu, C.-M., Xu, S.-Y., Lynch, M. J., et al. (2026) · APL Machine Learning · doi:10.1063/5.0301417 · Cited by 1
HIGH-FREQUENCY BEACH MONITORING WITH UAV PHOTOGRAMMETRY
SUZUKI, A., Mitobe, Y., Tanaka, H., et al. (2017) · 土木学会論文集B2(海岸工学) · doi:10.2208/kaigan.73.i_661
A Novel Keypoints-Based Image Registration Method with Fully Convolutional Neural Network
Yang, X., Li, D., He, Q., et al. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-8083-3_9
A strategy for interactive exploration of multimodal image collections
Mendoza, C., & Eliécer, J. (2014)
A Multi-scale SAR Scene Matching Algorithm Based on Compressed Contour Feature
Juan, S., Wei, C., & Yang-yang, Z. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21978-3_17
Compact and Robust Image Fingerprints Based on CCA of Local Features
Yang, J., & Sun, F. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03731-8_80
VecAlign: Efficient Vector Sequence Alignment for Similarity Search in the Embedding Space
Wang, Z., & Deng, D. (2026) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icdew71238.2026.00018
“Traditional methods typically use local feature descriptors such as SIFT [58], SURF [59] and ORB [60] for feature representation and matching.”
Contributions into holistic human action recognition.
Toudjeu, T. I. (2020) · ResearchSpace (University of KwaZulu-Natal)
Deep Learning Driven Investigation of Nanoplastic Impacts on Soil Protist Behavior in Soil Chips
(2025) · doi:10.1101/2025.09.04.674150
“Furthermore, SIFT (Scale-Invariant Feature Transform)[20, 21] was used for feature extraction, and a transformer-based model adapted from LightGlue[22] was developed to comprehensively register protist movements.”
Range image recognition using invariant local features
Bayramoğlu, N., & Alatan, A. A. (2010) · IEEE Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2010.5651360
Comparative study of feature extraction approaches for maritime vessel identification in CBIR
dos Santos, B. L. G., Lorena, A. C., & Cruz, J. E. C. (2024) · doi:10.5753/eniac.2024.245117
Mikolka-Flöry, S., Simböck, D., Lindorfer, V., et al. (2026) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2026.100134
The PvS-Index: An Indexing Method for Local Image Descriptors
Lejsek, H. (2005)
Passive Localization of a Robot Using Multiple-View Geometry
Khoramshahi, E., Honkavaara, E., Hyyppa, J. M., et al. (2014) · International Journal of Robotics Applications and Technologies · doi:10.4018/ijrat.2014070102
Subspace Projection Methods for Large Scale Image Data Analysis
Gupta, A., & Yılmaz, A. (2017) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2017.75 · Cited by 1
Zhong, Y. (2018) · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · doi:10.2991/erms-18.2018.17
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
