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 37,901–38,000 of 56,201
Combining multiple iris texture features for unconstrained recognition in visible wavelengths
Andersen-Hoppe, E., Rathgeb, C., & Busch, C. (2017) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf.2017.7935090 · Cited by 2
Shen, S., Yu, G., Zhang, L., et al. (2025) · Remote Sensing · doi:10.3390/rs17040653 · Cited by 2
OF-DFN: Optical flow prediction network for different perspective image fusion
You, T., Liu, M., Zhao, Y., & Dong, L. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127737 · Cited by 2
Pengenalan Barang Pada Kereta Belanja Menggunakan Metode Scale Invariant Feature Transform (SIFT)
Akbar, R. M., & Sunarmi, N. (2018) · Jurnal Teknologi Informasi dan Ilmu Komputer · doi:10.25126/jtiik.2018561046 · Cited by 2
Moses, I. A., & Reinhart, W. F. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ada2da · Cited by 2
“…images were analyzed using several metrics, including microstructural features such as the grain size, grain density, and Difference of Gaussian (DoG) blob, [72] and topographical and defect-related features such as the surface roughness, step edges, defect density, anisotropy, and local variation.”
Azimi, E., Behrad, A., Ghaznavi‐Ghoushchi, M. B., et al. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.063014 · Cited by 2
Pavelka, K., & Řezníček, J. (2011) · Geoinformatics FCE CTU · doi:10.14311/gi.6.31 · Cited by 14
A Moving Object Detection Algorithm Based on ORB under Dynamic Scene
Luo, W. H. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.602-605.1638 · Cited by 2
With a little help from my friends
Cusano, C., & Santini, S. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1096-y · Cited by 2
Fast Stitching Algorithm by using Feature Tracking
Park, S.-Y., Kim, J.-H., & Yoo, J. (2015) · Journal of Broadcast Engineering · doi:10.5909/jbe.2015.20.5.728 · Cited by 2
Visual and Inertial Sensor Fusion Approach for Visual Inertial Odometry Used in Vehicle Localization
Jo, H., & Yoon, S. W. (2024) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors60989.2024.10785070 · Cited by 2
Adaptive Multi-Task Learning for Multi-PAR in Real World
Sun, H., Zhao, H., Zhang, W., Xu, L., & Guan, H. (2024) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2024.3371881 · Cited by 2
Corner Detection and Classification of Simple Objects in Low-Depth Resolution Range Images
Kovács, V., & Tevesz, G. (2013) · Periodica Polytechnica Electrical Engineering · doi:10.3311/ppee.2075 · Cited by 2
Representing Images with χ 2 Distance Based Histograms of SIFT Descriptors
Viitaniemi, V., & Laaksonen, J. T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04277-5_70 · Cited by 2
Local Feature Extractors Accelerating HNNP for Phoneme Recognition
Janning, R., Schatten, C., & Schmidt-Thieme, L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11206-0_23 · Cited by 2
Lluis-Gomez, A., Saravi, S., & Edirisinghe, E. A. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.922712 · Cited by 2
Distributed Implementation for Person Re-Identification
Sthapit, S., Thompson, J., Hopgood, J. R., et al. (2015) · Sensor Signal Processing for Defence (SSPD) · doi:10.1109/sspd.2015.7288501 · Cited by 2
Infinite, Sparse 3D Modelling Volumes
Funk, E., & Börner, A. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-64870-5_29 · Cited by 2
DecTrain: Deciding When to Train a Monocular Depth DNN Online
Fu, Z.-S., Sudhakar, S., Karaman, S., et al. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3536206 · Cited by 2
“…data that will not be reduced by training), the poses of the robot in the current and previous two frames (capturing motion), and the number of landmarks extracted using SIFT [26] in the current and previous two frames (capturing texture) to indicate low or high ability to improve as seen in Fig.”
RRecT: Chinese Text Recognition with Radical-Enhanced Recognition Transformer
Deng, X., Huang, Z., Ma, K., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44223-0_41 · Cited by 2
Treier, S., Vuille‐dit‐Bille, N., Visse‐Mansiaux, M., et al. (2025) · The Plant Phenome Journal · doi:10.1002/ppj2.70039 · Cited by 2
Wang, L., Wang, Y., Wang, Y., et al. (2025) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.109282 · Cited by 2
A 100Hz real-time sensing system of textured range images
Ishiyama, H., Terabayashi, K., & Umeda, K. (2010) · International Symposium on Optomechatronic Technologies · doi:10.1109/isot.2010.5687379 · Cited by 2
Minimizing Drift in monocular SLAM real time systems
Munguía, R., & Grau, A. (2008) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2008.4677083 · Cited by 2
Vision SLAM Maps: Towards Richer Content
Asmar, D., Abdallah, S. M., & Zelek, J. (2009) · Studies in computational intelligence · doi:10.1007/978-3-540-89933-4_15 · Cited by 2
A Taxonomy of 3D Occluded Objects Recognition Techniques
Soleimanizadeh, S., Mohamad, D., Saba, T., et al. (2016) · 3D Research · doi:10.1007/s13319-016-0080-0 · Cited by 2
Thermal Face Recognition Using Face Localized Scale-Invariant Feature Transform
Uke, S. R., & Nandedkar, A. V. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-2104-6_54 · Cited by 2
A Family of Stereo-Based Stochastic Mapping Algorithms for Noisy Speech Recognition
Afify, M., Cui, X., & Gao, Y. (2008) · InTech eBooks · doi:10.5772/6374 · Cited by 2
Regression of feature scale tracklets for decimeter visual localization
Wong, D., Deguchi, D., Kawanishi, Y., et al. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.07.004 · Cited by 2
Ultrasound guidance for epidural anesthesia
Ashab, H. A.-D. (2013) · cIRcle (University of British Columbia) · doi:10.14288/1.0073744 · Cited by 2
Multi-Session Visual SLAM for Illumination Invariant Localization in Indoor Environments.Preprint
Labbé, M., & Michaud, F. (2021) · arXiv (Cornell University) · Cited by 2
An analysis of content-based image retrieval
Koyuncu, H., Dixit, M., & Koyuncu, B. (2021) · International Advanced Researches and Engineering Journal · doi:10.35860/iarej.811927 · Cited by 3
A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data MatchingPreprint
Yang, J., Xian, K., Wang, P., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02890 · Cited by 2
Classifying Images at Scene Level: Comparing Global and Local Descriptors
Hentschel, C., Gerke, S., & Mbanya, E. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37425-8_6 · Cited by 2
Image annotation by semi-supervised clustering constrained by SIFT orientation information
Sayar, A., & Yarman-Vural, F. T. (2008) · International Symposium on Computer and Information Sciences · doi:10.1109/iscis.2008.4717882 · Cited by 2
Ahmed, M. W., Ahmed, M. W., Adnan, M., et al. (2025) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2025.101801 · Cited by 2
He, K., Zhao, Y. L., Liu, Z., et al. (2021) · The Visual Computer · doi:10.1007/s00371-021-02107-4 · Cited by 2
Effective and Efficient Multi-Facet Web Image Annotation
Chen, J., Zhu, Y., Wang, H., et al. (2012) · Journal of Computer Science and Technology · doi:10.1007/s11390-012-1242-z · Cited by 2
Learning Object Appearance from Occlusions Using Structure and Motion Recovery
Cordes, K., Scheuermann, B., Rosenhahn, B., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37431-9_47 · Cited by 2
Lei, M., He, L., & Li, F. (2024) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs61748.2024.00064 · Cited by 2
“We have performed feature point detection using the sift[4] algorithm surf[5] algorithm and orb[6] algorithm respectively and compared their results as shown in Figs.”
MethodResultImage Content Based Curve Matching Using HMCD Descriptor
Wang, Z., Liu, H., & Wu, F. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12297-2_43 · Cited by 2
Integrating Augmented Reality and Geolocation for Outdoor Interactive Educational Experiences
Mourelatos, C., & Vrigkas, M. (2025) · Virtual Worlds · doi:10.3390/virtualworlds4020018 · Cited by 2
Research of Object Recognition and Tracking Based on Feature Matching
Ahn, H., & Rhee, S.-B. (2014) · Lecture notes in electrical engineering · doi:10.1007/978-3-662-45402-2_152 · Cited by 2
He, Z., Zhu, Q., Wang, W., et al. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3628896 · Cited by 2
Hierarchical Data-Driven Descent for Efficient Optimal Deformation Estimation
Tian, Y., & Narasimhan, S. G. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.284 · Cited by 2
Using Neural Networks and Self-Organizing Maps for Image Connecting
Ding, Y., Wang, T., & Fu, X. (2012) · Cognitive Computation · doi:10.1007/s12559-012-9161-4 · Cited by 2
Robust Self-supervised Correspondence Learning for SAR-optical Image Matching
An, C., Cao, F., Wang, P., et al. (2022) · International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · doi:10.1109/cei57409.2022.9950181 · Cited by 2
Large Parallax Image Stitching Using an Edge-Preserving Diffeomorphic Warping Process
Jacob, G. M., & Das, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01449-0_44 · Cited by 2
Loop-closing By Using SIFT Features for Mobile Robots
Fengda, Z., Lingfu, K., & Li, X. (2006) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2006.4347506 · Cited by 2
Oho, E., Suzuki, K., & Yamazaki, S. (2021) · Scanning · doi:10.1155/2021/2226577 · Cited by 2
Point-Based Object Recognition in RGB-D Images
Wilkowski, A., Kornuta, T., & Kasprzak, W. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-11310-4_51 · Cited by 2
Eyeball Image Registration and Fusion Based on SIFT+RANSAC Algorithm
Liu, A. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-3867-4_26 · Cited by 2
Chen, C., & Huang, C.-L. (2016) · Applied Sciences · doi:10.3390/app6050140 · Cited by 2
Ushanandhini, M., Rajesh, S., & Rajakani, M. (2016) · International Conference on Recent Trends in Information Technology · doi:10.1109/icrtit.2016.7569524 · Cited by 2
Tecnologías big data para análisis y recuperación de imágenes web
Rodríguez-Vaamonde, S., Torre-Bastida, A. I., & Garrote, E. (2014) · El Profesional de la Informacion · doi:10.3145/epi.2014.nov.02 · Cited by 2
Favorskaya, M. N., Pyankov, D., & Popov, A. (2015) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-13545-8_19 · Cited by 2
Functional Asplund Metrics for Pattern Matching, Robust to Variable Lighting Conditions
Noyel, G., & Jourlin, M. (2020) · Image Analysis & Stereology · doi:10.5566/ias.2292 · Cited by 3
A Simulation Software for Testing of Image Matching Algorithms in UAV Terrain-Aided Navigation
Grzywiński, S., Turek, P., & Bużantowicz, W. (2021) · International Conference on Military Technologies · doi:10.1109/icmt52455.2021.9502773 · Cited by 2
INTEGRATION OF KEYPOINTS AND EDGES FOR IMAGE RETRIEVAL
Chen, L.-H., Hung, Y.-L., & Su, C.-W. (2013) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001413550136 · Cited by 2
AENet: attention efficient network for cross-view image geo-localization
Xu, J., Zhu, M., Qi, B., et al. (2023) · Electronic Research Archive · doi:10.3934/era.2023210 · Cited by 2
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images
Melo, W., Potje, G., Cadar, F., et al. (2022) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi55357.2022.9991805 · Cited by 2
Multimodal late fusion bag of features applied to scene detection
Lopes, B. L., & Goularte, R. (2013) · Brazilian Symposium on Multimedia and the Web · doi:10.1145/2526188.2526202 · Cited by 2
FUSION OF MULTI-VIEW AND MULTI-SCALE AERIAL IMAGERY FOR REAL-TIME SITUATION AWARENESS APPLICATIONS
Zhuo, X., Kurz, F., & Reinartz, P. H. (2015) · 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/isprsarchives-xl-1-w4-201-2015 · Cited by 2
Efficient and Scalable 4th-Order Match Propagation
Ok, D., Marlet, R., & Audibert, J.-Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37331-2_35 · Cited by 2
Binary Feature Descriptor for Omnidirectional Images Processing
Benseddik, H. E., Hadj-Abdelkader, H., Cherki, B., et al. (2015) · doi:10.1145/2816839.2816848 · Cited by 2
Visual Search of multiple objects from a single query
Paracchini, M. B. M., Marcon, M., Plebani, E., et al. (2016) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin.2016.7684712 · Cited by 2
Wu, Y., Maoguo, G., Jia, J., et al. (2015) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai.2015.7407062 · Cited by 2
Development of adaptive acquisition high-frequency vision strain sensor and engineering validation
Bai, B., Lu, B., Wen, Z., et al. (2025) · Measurement · doi:10.1016/j.measurement.2025.118844 · Cited by 2
A Parameter Adaptive Method for Image Smoothing
Wang, S., Ma, X., & Li, X. (2024) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010068 · Cited by 2
Introducing the structural bases of typicality effects in deep learning
Pino, O. V., Nascimento, E. R., & Campos, M. F. M. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104249 · Cited by 2
AstroLoc2: Fast Sequential Depth-Enhanced Localization for Free-Flying Robots
Soussan, R., Moreira, M., Coltin, B., & Smith, T. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128673 · Cited by 2
Ahmad, S. S., & Lu, Z. (2012) · Digital Rights Management · doi:10.4018/978-1-4666-2136-7.ch013 · Cited by 2
NeRF-Supervised Feature Point Detection and Description
Youssef, A., & Vasconcelos, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91989-3_7 · Cited by 2
Mathematical models for some problems in textile engineering and its corresponding discussions
Zhang, L., You, X., Chen, J., et al. (2013) · Advances in Complex Systems · doi:10.1142/s179396231350030x · Cited by 2
Image Retrieval Based on RST Invariant Features Extracted from Scale Invariant Keypoints
Lu, Z., Zheng, W., & Wang, J. (2010) · International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) · doi:10.1109/iihmsp.2010.116 · Cited by 2
Towards Secure Private Image Matching
Abduljabbar, Z. A., Jin, H., Ibrahim, A., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39077-2_20 · Cited by 2
SGNet: Salient Geometric Network for Point Cloud Registration
Wu, Q., Ding, Y., Luo, L., et al. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802262 · Cited by 2
Profiling Online Auction Sellers Using Image-Editing Styles
Yang, L., Chen, W.-B., Zhang, C., et al. (2012) · IEEE Multimedia · doi:10.1109/mmul.2012.12 · Cited by 2
Biometric System Based on Registration of Dorsal Hand Vein Configurations
Lefkovits, S., Emerich, S., & Szilágyi, L. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92753-4_2 · Cited by 2
Video Data Hierarchical Retrieval via Deep Hash Method
Suo, Y., Zhang, C. G., Xi, X., et al. (2019) · IEEE International Conference on Communication Software and Networks · doi:10.1109/iccsn.2019.8905258 · Cited by 2
Determine the local orientation by matrix singular value decomposition for the 3D SIFT algorithm
Li, C., Li, W., Huang, Z., et al. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae18f0 · Cited by 2
“Among various feature matching methods, the SIFT algorithm demonstrates robust performance under challenging conditions, including noise, rotation, scaling, and multiscale variations [13], and has been widely applied across multiple disciplines [14].”
Liao, L., Ogawa, Y., Zhao, C., & Sekimoto, Y. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.09.026 · Cited by 3
Small Blob Detection in Medical Images
Zhang, M. (2015) · Cited by 2
Cheoi, K. J., & Kim, M.-H. (2017) · Wireless Personal Communications · doi:10.1007/s11277-017-5043-0 · Cited by 2
Product sub-vector quantization for feature indexing
Pham, T.-A., Le, D.-N., & Nguyen, T. (2019) · Journal of Computer Science and Cybernetics · doi:10.15625/1813-9663/35/1/13442 · Cited by 2
A CNN-Transformer Hybrid for Precise Object Detection in UAV Aerial Imagery
Onteddu, A. R., Bandhela, R. R., Kundavaram, R. R., & V.Jagannaveen (2025) · IEEE Global Conference on Wireless Computing and Networking (GCWCN) · doi:10.1109/gcwcn66157.2025.11448412 · Cited by 2
“The use of deep learning, and specifically convolutional neural networks (CNNs) changed everything as they provided the ability to extract features and classify data end-to-end using raw images as input data [2].”
Stereo Vision Based Speed Estimation for Autonomous Driving
Umamaheswaran, S., Malavika Nair, M., Joseph, A. Z., et al. (2019) · doi:10.1109/icit48102.2019.00042 · Cited by 2
Automated design of image recognition in capturing environment
Ogata, T., Yukisawa, T., Arai, T., Ueyama, T., Takada, T., & Ota, J. (2017) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.22551 · Cited by 2
An Image Dataset of Bishnupur Terracotta Temples for Digital Heritage Research
Ghorai, M., Santra, S., Samanta, S., Purkait, P., & Chanda, B. (2018) · Springer eBooks · doi:10.1007/978-981-10-7221-5_13 · Cited by 2
Fast Approximate GMM Soft-Assign for Fine-Grained Image Classification with Large Fisher Vectors
Krapac, J., & Šegvić, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24947-6_39 · Cited by 2
Singh, K., Luu, K., Le, N., et al. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351030 · Cited by 2
Geo-distinctive Visual Element Matching for Location Estimation of ImagesPreprint
Li, X., Larson, M. A., & Hanjalić, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.07884 · Cited by 2
Extraction of ORB Features with an Improved Method
Yang, B., Wang, Z., & Yu, X. (2019) · International Conference on Vision, Image and Signal Processing (ICVISP) · doi:10.1145/3387168.3387169 · Cited by 2
Feature-Based Detection of Inverted-Stereo for Stereoscopic 3D Viewing Comfort
Kim, W.-J., Leigh, S.-W., Hur, N., et al. (2012) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2012.2190193 · Cited by 2
Affine Invariant Topic Model for Generic Object Recognition
Zhen-xiao, L., & Zhang, L. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13318-3_20 · Cited by 2
Ye, M., & Tang, Z. (2013) · Journal of Earth Science · doi:10.1007/s12583-013-0341-2 · Cited by 2
Automatic Facial Expression Recognition System Using Convolutional Neural Networks
Do, H. N., Trang, K., Vuong, B. Q., et al. (2019) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-13-5859-3_82 · Cited by 2
Automated visual classification of indoor scenes and architectural styles
Solmaz, B., Yücesoy, V., & Koç, A. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960205 · Cited by 2
Friendly Faces: Weakly Supervised Character Identification
Marter, M., Hadfield, S., & Bowden, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-13737-7_11 · Cited by 2
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
