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 52,501–52,600 of 56,204
Deep Convolutional Neural Network for Object Classification
Al-Sharkawy, A. A., Bahgat, G. A., Hemayed, E. E., & Mashali, S. A. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-6690-9.ch016
Spherical Panorama Stitching Based on Feature Matching and Optical Flow
Wang, B., Tang, F., & Liu, H. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-6499-9_9
Liu, Y., Liu, C., Hou, R., Ding, Z., He, K., & Zhou, D. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02485-6
“Commonly used feature extraction algorithms include SIFT [5], SURF [6], ORB [7].”
Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming
Man, X., & Song, Y. (2024) · Current Computer Science · doi:10.2174/0129503779291012240424070357
Locality and Sparsity Preserving Embedding Convolutional Neural Network for Image Classification
Yu, X., & Zhan, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36189-1_37
Automatic Calculation of Resolution in Lateral Cephalogram Based on Scale Mark DetectionPreprint
Guo, J., Wang, S., & Li, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03537
Face Verification Based on the Age Progression Rules
Fang, K., & Liu, S. (2015) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2014edl8178
Foreground Object Extraction Using Variation of Blurs Based on Camera Focusing
Takayama, N., & Takahashi, H. (2015) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2015.34
Chen, Y., Liang, X., Zhang, Z., et al. (2015) · World Academy of Science, Engineering and Technology, International Journal of Bioengineering and Life Sciences
Research on feature matching algorithm of remote sensing scenes
Xu, C., Dong, E., & Zhang, H. (2022) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit55263.2022.9987211
Energy-efficient real-time computer vision applications in practice
Kramer, M. A. M., & Roth, P. M. (2024) · Real-time Processing of Image, Depth, and Video Information · doi:10.1117/12.3025261
Somchaipeng, K., Sporring, J., Johansen, P., et al. (2007) · PAMM · doi:10.1002/pamm.200700534
Biometric Analysis of Human Ear Matching Using Scale and Rotation Invariant Feature Detectors
Sarkar, S., Liu, J., & Wang, G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20801-5_20
Category-Level 3D Correspondence in Camera Space via Morphable Object Priors
Sommer, L., Jesslen, A., Sunagad, B., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37602-2_9
Robust irregular region matching based on inner product and exterior product
Liu, H., & Wang, Z. (2010) · doi:10.1109/icinfa.2010.5512191
Audition for multimedia computing
Friedland, G. H., Smaragdis, P., McDermott, J. H., & Raj, B. (2017) · doi:10.1145/3122865.3122868 · Cited by 1
Metric and appearance based visual SLAM for mobile robots
Şahin, C. (2013) · Sabanci University
Cheoi, K. J. (2015) · Journal of Korea Multimedia Society · doi:10.9717/kmms.2015.18.4.460
Rodrigues, M. V., Maris, A., Bezerra, A. M., et al. (2025) · Studies on Neotropical Fauna and Environment · doi:10.1080/01650521.2025.2560979
“This algo-rithm characterizes variation patterns by comparing all possible image combinations in the database (Lowe 2004).”
Recursive Structure from Motion
Chebiyyam, M., Chaudhury, S., & Kar, I. N. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68124-5_10
Multi-feature Fusion Matching Method Based on Low-Rank Constraint
Zhang, Y. X., Sun, K., Liu, J., et al. (2026) · International Conference on Image and Graphics Processing (ICIGP) · doi:10.1109/icigp68997.2026.11619848
“The SIFT ratio test [7] addresses this by using a distance constraint to filter out a large portion of incorrect matches.”
InfluentialA Predictive Model of Handwritten Digits Recognition Using Expert Systems
Praveen, P., Abhishek, N., Reddy, K. J., et al. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-5301-8_43
Robust estimation of chip clogging with supervised learning using tool surface image
Furuki, T., Nishigaki, K., Suda, T., et al. (2025) · CIRP Annals · doi:10.1016/j.cirp.2025.04.039
Ma, J., Zhang, W., Han, J., et al. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2026.109408
Energy and Orientation Maps for Interactive Visualization and Retrieval of Ancient Coins
Marchand, S., Gomez‐Krämer, P., & Lardeux, F. (2023) · International Conference on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3617233.3617268
FM-MFIF: A motion-aware multi-focus image fusion network based on multi-scale focus migration
Li, Z. K., Zhulun, Y., Yang, Y., et al. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.106136
Automatic Stitching System for Images of Plane-Like Scenes
Huang, D., Wen, M., Xue, Y., et al. (2013) · doi:10.2316/p.2013.798-075
The reactivity of single magnesium nanoparticles towards corrosion and galvanic replacement
Brabant, A. L. Y., Knight, P., Joyce, K., Elabbadi, M., Lomonosov, V. I., Boukouvala, C., & Ringe, E. (2026) · Nanoscale · doi:10.1039/d6nr00806b
Large scale visual classification with SVM, create the unique article through NLP
Madushani, M. A. N. (2017) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter.2017.8257788
Fast Coherent Point DriftPreprint
Feng, X.-W., Da-zheng, F., & Zhu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06281
Resampling-free jitter-aware bundle adjustment for HiRISE multi-CCD stereo mapping on Mars
Wang, B., Zhao, S., Wang, S., Guo, X., Zhan, Z., & Wang, K. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.09.020
Reconnaissance d'objets en vision artificielle : application à la reconnaissance de piétons
Leyrit, L. (2010) · theses.fr (ABES) · doi:10.70675/f5aceee4zad85z4ec6zab1fz4c6d0fe7be33
Zhang, B., Li, C. G., Wang, Z., Zhou, D., Lv, M., & Zhang, R. (2026) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2026.3710658
Empleo de microdrones para la reconstrucción tridimensional del terreno
Ramírez-Torres, J. G., & Larrañaga-Cepeda, A. (2019) · Revista de Ingeniería Civil · doi:10.35429/jce.2019.9.3.17.27
A Review of Image Stitching Methods: From the Original Manual to the Modern Automatic Algorithms
Barranco, K. K. M., Gálvez, J., & Wario, F. (2025) · doi:10.61728/ae20255077
Residual-Consensus Driven Linear Matching
Wang, H. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2586860
Freiburg, A., & Informatik, U. (2006)
LGPR: Local Feature Learning Brings More Generalizable Visual Place Recognition
Su, S., Yang, J., Du, J., et al. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247194
A Comparative Study of Local Image Feature Description
Zhou, L., & Tao, Y. (2016) · doi:10.2991/icsnce-16.2016.66
Sparsity cue in image copy detection
Hsu, H.-C., Huang, C.-R., & Lu, C.-S. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2396351
Guided Sparse Camera Pose Estimation
Schenk, F., Mohr, L., Rüther, M., et al. (2020) · TUGraz OPEN Library (Graz University of Technology) · doi:10.3217/978-3-85125-528-7-12
Yao, T., Song, J., An, P., et al. (2016) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2016.7832104
A Comparative Evaluation of Driver Drowsiness Detection Techniques
Albalawi, W. M., Sabbeh, S., & Zamzami, N. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.17766
“…LBP operator evaluates a 3 × 3 neighborhood by applying a threshold and determines the center element's value by converting the adjacent values into decimals [29]. c) Scale-Invariant Feature Transform SIFT, introduced in 2004, is a commonly used feature detection and description algorithm [30].”
Banerjee, A., & Ghosh, R. K. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-16-1574-0_12
The Improvement of RoMa Dense Matching Framework With the Introduction of KAN
Zhou, Z., Shi, A., & Wu, W. (2026) · IEEE Access · doi:10.1109/access.2026.3654104
CONTENT-BASED VIDEO COPY DETECTION USING MULTIMODAL ANALYSIS
Küçüktunç, O. (2009) · Bilkent University Institutional Repository (Bilkent University)
A Weak Texture Image Registration Algorithm Based on Improved Disk
Yin, Z., Wang, X., & Wang, M. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11559818
“Among them, the DISK feature point extraction algorithm, due to its unique reinforcement learning training mechanism, performs well under conditions such as weak texture, large viewpoint changes, and dynamic lighting, with high robustness [11].”
Relation-aware multimodal data hashing for scalable recommendation systems
Liu, Z., Liang, H., & Ojha, V. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-026-01202-1
“For image nodes, the Scale-Invariant Feature Transform (SIFT) (Lowe 2004) and the Oriented FAST and Rotated BRIEF (ORB) (Rublee et al. 2011) capture key visual features, facilitating their representation in high-dimensional spaces.”
Hierarchical Knowledge-Guided Recognition for Wolfberry PestsPreprint
Zhao, H., Wang, X., Man, R., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6293238
A Approach of Evaluation the Lettuce Height Based on Convolutional Neural NetworkPreprint
Guo, X., Zhong, Y., Feng, Y., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4089874
HIP 2013 FamilySearch Competition - Contribution of
Camillerapp, J. (2013)
Multiple Objects and Road Detection in Unmanned Aerial Vehicle
Saranya, M., Reddy, K. T., Raju, M., et al. (2022) · International Journal of Computer Science Engineering and Information Technology · doi:10.5121/ijcseit.2022.12401
Copy-Move Forgery Detection Utilizing SIFT and FLANN with Optimized Keypoint Matching
Rabby, S., & Ahsan, S. M. M. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022130
“The process begins with feature extraction from the input image using the SIFT [7] algorithm.”
InfluentialClasificación de género utilizando vectores de frecuencia basados en descriptores locales
Aguilar, E., & Bekios-Calfa, J. (2016) · Ingeniare. Revista chilena de ingeniería · doi:10.4067/s0718-33052016000100012
Design of a Mobile Data Collection Robot for Learning-based Localization and Autonomous Driving
Baykar, A. Ö., Lambrecht, J., Kural, A., et al. (2023) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu58738.2023.10296622
Object Classification and Tracking in Real Time
Das, A., Saikia, N., & Sarma, K. K. (2016) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-4666-9685-3.ch011
Zooming Assisted Stereo Matching
Lin, H., & Chen, Y. (2022) · Springer eBooks · doi:10.1007/978-3-031-09791-1_3
Machine Learning in Epidemiology
Wright, M. N., Burk, L., Golchian, P., Kapar, J., Koenen, N., & Langbein, S. (2024) · Springer eBooks · doi:10.1007/978-1-4614-6625-3_81-1
“The nearest-neighbors distance ratio (NNDR) (Lowe, 2004) measures the ratio between the distance for the closest and second closest real neighbor in the training set for any synthetic record.”
Shape-Based Object Discovery in Images
Todorović, S., & Payet, N. (2013) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-5195-1_27
Online Vicept learning for web-scale image understanding
Li, L., Jiang, S., & Huang, Q. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116166
A 3D Plane Recognition Algorithm Based on Image Matching
Jiang, J., Hu, X., Zhang, H., et al. (2012) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2012.143
Asymmetric Feature Representation for Object Recognition in Client Server System
Yamauchi, Y., Ambai, M., Sato, I., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16865-4_39
A novel approach for image feature description based on dual gradient orientation histogram
Bao, J.-Q., & Mao, X. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2281669
Assistive Technology and Applications: Independent living of severe motor disabled users
Castellina, E. (2010) · PORTO@Iris
Learning Image Manifolds from Local Features
Ma, Y., & Fu, Y. (2011) · CRC Press eBooks · doi:10.1201/b11431-16
(2015) · American Institute of Aeronautics and Astronautics eBooks · doi:10.2514/5.9781624101960.0871.0900
A comparative study of image feature detection and description methods for robot vision
Gonzalez-Ruiz, M., Diaz-Ramirez, V. H., & Juarez-Salazar, R. (2019) · doi:10.1117/12.2528581
(2024) · European Journal of Public Administration Research · doi:10.47743/ejpar.2023-2
Exemplar-Based Image Inpainting Using an Affine Invariant Similarity Measure
Fedorov, V., Arias, P., Facciolo, G., et al. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-64870-5_22
Improved Image Retrieval using Visual Sorting and Semi-Automatic Semantic Categorization of Images
Barthel, K. U., Richter, S., & Goyal, A. K. (2008) · doi:10.5220/0002339400670077
Covariant-Scale: Riemannian Manifold Learning for Scale-Invariant Video Anomaly Detection
Kingori, S. W., Nderu, L., & Njagi, D. (2025) · Journal of Computer and Communications · doi:10.4236/jcc.2025.1311008
A Deep Multialgorithm Facial Biometric System
Mehraj, H., & Mir, A. H. (2025) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661825700579
Enhancing instance search with weak geometric correlation consistency
Zhang, Z., Albatal, R., Gurrin, C. G., et al. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.09.104
YOLO-VLAD: A Lightweight Global Descriptor Network for Efficient Loop Closure Detection
Li, W., & Wang, W. (2025) · International Conference on Artificial Intelligence and Automation Control (AIAC) · doi:10.1109/aiac68175.2025.11332523
“Although descriptors such as SIFT [3] provided strong invariance, their computational cost restricted real-time deployment, motivating the transition toward learning-based methods.”
Machine Recognition of Objects
Poggio, T., & Ullman, S. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_793
Normal Vector Descriptor and Grid-Based Optimization for Panorama Stitching
Denisov, A. (2026) · International Conference on Digital Signal Processing and its Applications (DSPA) · doi:10.1109/dspa69176.2026.11476764
A pattern matching method using geometric information of images
Wang, K., Xia, Q., Shi, T., et al. (2011) · IEEE International Conference on Nano/Micro Engineered and Molecular Systems · doi:10.1109/nems.2011.6017288
Robust image matching using local affine region and Mahalanobis metric
Ye, Z., Tang, L., & Zhao, B. (2016) · International Conference on Signal Processing · doi:10.1109/icsp.2016.7877962
Monocular Vision Based Mobile Robot 3D Map Building
Li, M. H., & Sun, L. (2010) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.43.49
1-Point Rigid Motion Estimation and Segmentation with a RGB-D Camera
Perera, S., & Barnes, N. M. (2013) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2013.6691469
Emotion Index of Cover Song Music Video Clips Based on Facial Expression Recognition
Kavalakis, G., Vidakis, N., & Triantafyllidis, G. A. (2017) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-55834-9_29
Learning-based visual SLAM with optical flow and probabilistic volumetric fusion
Park, M.-H., Cho, J.-H., & Kim, Y.-T. (2026) · Intelligent Service Robotics · doi:10.1007/s11370-026-00691-9
“Algorithms such as SIFT [11], SURF [12], and ORB [13] have been widely adopted for this purpose.”
Support Vector Guided Locally Aggregated Descriptors for Feature Encoding
Tian, Y., & Wang, Q. (2022) · International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · doi:10.1109/cei57409.2022.9950151
Feature Matching Method for Aircraft Positioning on Airdrome
Wang, J., & Ni, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21963-9_26
Kumar, L., & Satyarthi, M. K. (2024) · doi:10.1109/globalaisummit62156.2024.10947981
“…for acquiring data, including vision-based [9], ultrasonography [10], noise generation [11], scanning using lasers [12], electrical [13], radiography [14-15], and thermographic ways [8]. due to its computerization and dependability, vision-based detecting has grown in popularity subsequently [9].”
Tuytelaars, T. (2020) · Springer eBooks · doi:10.1007/978-3-030-03243-2_191-1
Darong, 朱. Z., Yexiang, 韦. W., Feng, 王. W., Fangbin, 汪. W., Congju, 左. Z., & Xue, 龚. G. (2026) · Laser & Optoelectronics Progress · doi:10.3788/lop252224
Design and Implementation of Smart System for Interaction Between Blind, Deaf and Mute People
(2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.b1368.0982s1119
Wang, Z., Ruan, S., Feng, X., et al. (2025) · doi:10.1117/12.3057807
“Scale-invariant Feature Transform (SIFT) [5] constructs the basic framework of image matching and realizes the scale-invariant description of features through Gaussian scale space and gradient histogram.”
Vishnuraj., P., & Kumar, D. V. (2020) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.c5923.049420
Research on Wrong Match Pairs Elimination of SIFT Algorithm
Zhang, D. X., Zhu, M. B., & Li, X. P. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.333-335.963
Beerten, P., Hamesse, C., & Haelterman, R. (2024) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0012320000003636
Piecewise Planar Region Matching for High-Resolution Aerial Video Tracking
Yi, M., & Sui, L. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-48499-0_10
Tippey, K., Nakano, A., Nakano, J., et al. (2022) · Ceramics International · doi:10.1016/j.ceramint.2022.08.259
Dual-domain extraction and fusion framework for underwater image enhancement
Hu, Z., & Chen, Q. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114458
Podor, R., Duprat, T., Pedraza, F., et al. (2026) · High Temperature Corrosion of Materials · doi:10.1007/s11085-026-10441-y
“Image registration was performed using the ‘Linear Stack Align - ment with SIFT’ method [19].”
Optimization of YOLOv8 Vehicle Detection Algorithm in Autonomous Driving Technology
Chu, C., Hui, S., Liu, X., et al. (2024) · doi:10.1109/icaica63239.2024.10823036
LEFT: LOCAL EDGE FEATURES TRANSFORM
Тихомирова, Т. А., Fedorenko, G. Т., Nazarenko, K. M., et al. (2020) · Vestnik komp iuternykh i informatsionnykh tekhnologii · doi:10.14489/vkit.2020.03.pp.011-018
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
