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 45,701–45,800 of 56,200
The Application of Image Retrieval Technology in the Prevention of Diseases and Pests in Fruit Trees
Wang, Z., Liu, X., Jiang, M., et al. (2014) · IFIP advances in information and communication technology · doi:10.1007/978-3-642-54344-9_20 · Cited by 1
Variable-Length HashingPreprint
Yu, H., Moulin, P., Ng, H. W., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.05414 · Cited by 1
Image matching by affine speed-up robust features
Chen, L., Liu, J., & Cao, L. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.900333 · Cited by 1
Image Collection Filed of Plant Roots Based on Hypoid Mirror
Kun, B., Feifei, H., Zhao, X., et al. (2011) · International Conference on Intelligent Computation Technology and Automation · doi:10.1109/icicta.2011.426 · Cited by 1
Optimized image super-resolution based on sparse representation
Zhu, Y., Jiang, J., & Li, K. (2012) · Cited by 1
Lightweight Coal Flow Foreign Object Detection Algorithm
Nie, R., Shen, X., Li, Z., Jiang, Y., Liao, H., & You, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5588-2_33 · Cited by 1
Improved Multi-feature Fusion Approach Based on End-to-end Convolutional Neural Network for Dehazing
Liu, Y., Al-Shehari, H., & Min, J. (2021) · IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) · doi:10.1109/seai52285.2021.9477535 · Cited by 1
USRRM: Pairwise Ranking and Scoring Images Using Its Aesthetic Quality
Dahal, B., & Zhan, J. Z. (2019) · IEEE Access · doi:10.1109/access.2019.2943460 · Cited by 1
Kamoona, A. M., Gostar, A. K., Bab‐Hadiashar, A., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01882 · Cited by 1
Liu, X., Nie, R., Cao, J., et al. (2026) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.114594 · Cited by 2
Detection and restoration of color-timing echo artifact for HD digital cinema films
Bao, Q., Luong, M., & Beghdadi, A. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5651976 · Cited by 1
Stereo Matching of Urban Remote Sensing Image Based on Edge Block Coincidence Rate
Ning, H. Y., Sun, Y., Li, Z., et al. (2020) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma49215.2020.9233815 · Cited by 1
A Velocity-Based Rao-Blackwellized Particle Filter Approach to Monocular vSLAM
Farrokhsiar, M., & Najjaran, H. (2011) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2011.33013 · Cited by 1
Learning relationship between speech and image
Bui, T. D., & Nguyen, Q. T. (2016) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2016.7758037 · Cited by 1
Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching
Han, Y., Huang, Z., Zhang, Y., et al. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3621651 · Cited by 1
“Traditional registration pipelines often rely on handcrafted features such as SIFT [37] and ORB [38] to detect and match keypoints across images.”
Detection of moving objects from a moving platform in urban scenes
ter Haar, F. B., den Hollander, R. J. M., & Dijk, J. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.848034 · Cited by 1
Transformational Approach for Alignment-free Image Matching Applications
Komal, K., Bhattacharjee, N., Albrecht, D. W., et al. (2017) · doi:10.1145/3151848.3151855 · Cited by 1
Voice-Based Image Captioning System for Assisting Visually Impaired People Using Neural Networks
Nivedita, M., AsnathVictyPhamila, Y., Kumaravelan, U., & Karthikeyan, N. K. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-3843-5.ch011 · Cited by 1
A Novel Content Based Image Retrieval Scheme in Cloud Computing
Liu, Z., Huang, C., Suo, H., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-24265-7_52 · Cited by 1
Oriented FAST Rotated BRIEF and Trie-Based Efficient Copy-Move Forgery Detection Algorithm
Soni, B., & Das, P. K. (2022) · Studies in computational intelligence · doi:10.1007/978-981-16-9041-9_8 · Cited by 1
Quasi Epipolar Resampling of Linear Scanner Triplets for Stereo Matching
Bhalerao, R. H. (2015) · Asian Journal of Geoinformatics · Cited by 1
Sakuramoto, Y., & Hashimoto, M. (2015) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.81.678 · Cited by 1
Computational Speed and Qualitative Assessment of Real-Time Image Stitching Algorithm
Kumareswaran, D., Nasir, N., Rahman, M. Z. A., et al. (2021) · International Conference on Communication, Control and Information Sciences (ICCISc) · doi:10.1109/iccisc52257.2021.9484887 · Cited by 1
Accurate 3-D reconstruction of sands from UAV image sequence
Matsunaga, R., Hashimoto, M., Kanazawa, Y., et al. (2016) · doi:10.1109/icaicta.2016.7803121 · Cited by 1
Performance optimization of rotation-tolerant Viola–Jones-based blackbird detection
Jalil, N., Smith, S. C., & Green, R. (2018) · Journal of Real-Time Image Processing · doi:10.1007/s11554-018-0795-7 · Cited by 1
Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data
Shahraki, F. F. (2020) · Digital Scholarship - UNLV (University of Nevada Reno) · doi:10.34917/11156724 · Cited by 1
Pyramid Transformer-Based Triplet Hashing for Robust Visual Place RecognitionPreprint
Li, Z., & Xu, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4797440 · Cited by 1
Automatic Target Recognition from Inverse Synthetic Aperture Radar Images
Kondaveeti, H. K., & Vatsavayi, V. K. (2016) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-5225-0773-4.ch017 · Cited by 1
Face Reconstruction from Profile to Frontal Evaluation of Face Recognition
Afra, S., & Alhajj, R. (2019) · Studies in big data · doi:10.1007/978-3-030-32587-9_8 · Cited by 1
Detecting texture paths and patterns in aerial images
Michalopoulos, K., Tsakalakis, M., & Bourbakis, N. (2013) · doi:10.1109/iisa.2013.6623731 · Cited by 1
Localization of Moving Robots By Using Omnidirectional Camera in State Space Framework
Tanaka, M., Umetani, T., Hirono, H., et al. (2011) · Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications · doi:10.5687/sss.2011.19 · Cited by 1
CPQN: Central Product Quantization Network for Semi-supervised Image Retrieval
Guo, Z., Hong, C., Zhuang, W., et al. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671490 · Cited by 1
Improved Action Recognition Using an Efficient Boosting Method
Abouelenien, M., & Yuan, X. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-35326-0_20 · Cited by 1
Probabilistic Diffusion Classifiers for Object Detection
Bauckhage, C. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761249 · Cited by 1
Designing Efficient Parallel Processing in 3D Standard-Chip Stacking System with Standard Bus
Ohkawa, T., Ootsu, K., Yokota, T., Kikuchi, K., & Aoyagi, M. (2017) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc.2017.27 · Cited by 1
Using Anisotropic Diffusion in the Multiscale Ridge Detection Method
Mamaev, N. V., & Krylov, A. S. (2019) · Computational Mathematics and Modeling · doi:10.1007/s10598-019-09446-x · Cited by 1
Kisku, D. R., Gupta, P., & Sing, J. K. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-015-0.ch010 · Cited by 1
Wang, Z., Hou, J. L., Liu, Z., et al. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727686 · Cited by 1
Zhu, L., Mao, Y., Chen, C., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11010023 · Cited by 1
“The scale-invariant feature transform algorithm proposed by David Lowe [11] establishes the basic framework of point-based feature alignment methods, which is the most classical point-matching method and has received numerous extended applications.”
Automatic Computation of Fundamental Matrix Based on Voting
Li, X., & Yuan, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04375-9_33 · Cited by 1
Lü, C., Yang, X., Wang, Z., et al. (2018) · doi:10.1109/prrs.2018.8486368 · Cited by 1
Efficient Person Search: An Anchor-Free ApproachPreprint
Yan, Y., Li, J., Qin, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.00211 · Cited by 1
Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
Zhang, D., & Deng, J. (2023) · Applied Sciences · doi:10.3390/app13074282 · Cited by 1
Aircraft Defect Localization and Detection Using Object Detection Techniques
Malik, P., Lakshakar, H., Hazra, J., Siddhant, R., & Kumar, H. (2023) · IEEE International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC) · doi:10.1109/aespc59761.2023.10390046 · Cited by 1
The impact of information volume on SIFT descriptor
Lin, S. C. F., Wong, C. Y., Ren, T. R., et al. (2013) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2013.6599332 · Cited by 1
Reconstruction of dense point cloud from uncalibrated widebaseline images
Wan, Y., Miao, Z., & Tang, Z. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495399 · Cited by 1
Sumiyoshi, S., & Yoshida, Y. (2019) · International Journal of Automation Technology · doi:10.20965/ijat.2019.p0464 · Cited by 1
Chen, Z., Zhang, X., Ou, Y., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15937-4_60 · Cited by 1
Towards robust and optimal image stitching for pavement crack inspection and mapping
Li, H., Liu, Y., Fan, L., et al. (2017) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2017.8324777 · Cited by 1
Multimodal human centric object recognition framework for personal robots
Chandarr, A., Rudinac, M., & Jonker, P. P. (2014) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2014.7041340 · Cited by 1
Point Supervised Extended Scenario Nuclear Analysis Framework Based on LSTM-CFCN
Sui, D., Guo, M., Yang, F., & Zhang, L. (2020) · IEEE Access · doi:10.1109/access.2020.2984996 · Cited by 1
A Global Image Feature Construction Method Based on Local Jet Structure
Xie, J., & Cai, Z. (2014) · Acta Automatica Sinica · doi:10.1016/s1874-1029(14)60012-4 · Cited by 1
Compact Descriptor for Video Sequence Matching in the Context of Large Scale 3D Reconstruction
Parys, R., Liefers, F., & Schilling, A. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-32335-5_6 · Cited by 1
FROMFusion: Fast and Robust On-Manifold Dense Reconstruction for Low-Cost Wheeled Robots
Bao, M., Fan, J., Fan, Z., et al. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3481537 · Cited by 1
Mapping and Localization for Mobile Robots through Environment Appearance Update
Bacca, E., Joaquím, S., & Xavier, C. (2010) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-643-0-291 · Cited by 1
Ackerman, D., Avetissian, E., Bleck, C. K. E., Bogovic, J., Innerberger, M., Korff, W. L., Li, W., Lu, Z., Petruncio, A., Preibisch, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.05.611438 · Cited by 1
Lian, Z., Tang, S., Han, J., et al. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3546224 · Cited by 1
Toward semantic loop closure in simultaneous localization and mapping systems
Zhang, Y.-C., Huang, C., Yang, C.-Y., & Chen, Y.-H. (2018) · Current Developments in Lens Design and Optical Engineering · doi:10.1117/12.2320035 · Cited by 1
Markerless monocular vision-based localisation for autonomous inspection drones
Nel, G., Engelbrecht, J. A. A., & Engelbrecht, H. A. (2022) · MATEC Web of Conferences · doi:10.1051/matecconf/202237007012 · Cited by 1
3D Intra-articular Dense Reconstruction from Arthroscopic Images
Zhang, Y., Cheng, C., Falque, R., et al. (2023) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio58561.2023.10354866 · Cited by 1
Applying the Robust Back-End in a Complete SLAM System on A Real-World Dataset
Sünderhauf, N. (2023) · Springer tracts in advanced robotics · doi:10.1007/978-3-031-24017-1_7 · Cited by 1
Medioni, G. G., & Chang, Y. (2007) · Cited by 1
Gutiérrez, J. A. V., Palomino‐Ángel, S., & Anaya, J. A. (2022) · Earth Sciences Research Journal · doi:10.15446/esrj.v26n1.95405 · Cited by 1
A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
Le, V.-H., & Nguyen, T.-H.-P. (2025) · Algorithms · doi:10.3390/a18070394 · Cited by 1
Effective camera motion analysis approach
Ma, S., & Wang, W. (2010) · International Conference on Networking, Sensing and Control · doi:10.1109/icnsc.2010.5461530 · Cited by 1
Analyse Discriminante de Haute Dimension
Bouveyron, C., Girard, S., & Schmid, C. (2005) · INRIA a CCSD electronic archive server · Cited by 1
Software Architecture for Automated 3D Image Measurements
Tsonkov, G., Garvanova, M., & Garvanov, I. G. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-98033-6_15 · Cited by 1
SpiderTailed: A Tool for Detecting Presentation Failures Using Screenshots and DOM Extraction
Okajima, T., Tanaka, T., Hazeyama, A., & Hashiura, H. (2023) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-17583-1_4 · Cited by 1
Towards Intelligent Systems with Incremental Learning Ability
Reiff, T., & Sinčák, P. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-03737-5_45 · Cited by 1
LOAD: Local Orientation Adaptive Descriptor for Texture and Material ClassificationPreprint
Qi, X., Zhao, G., Shen, L., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.05809 · Cited by 1
M-VCR: Multi-View Consensus Recognition for Real-Time Experimentation
McKee, D. W., Townend, P., Webster, D., et al. (2014) · IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC) · doi:10.1109/isorc.2014.37 · Cited by 1
Satellite Image Classification with Deep LearningPreprint
Pritt, M. D., & Chern, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06497 · Cited by 25
3D scanning data processing and registration for the construction of urban scenes via image methods
Liu, W., Yang, C., Zhou, X., et al. (2017) · The Imaging Science Journal · doi:10.1080/13682199.2017.1316433 · Cited by 1
Global Image Registration Based on Invariant Representation of Polar Complex Exponential Transform
Yi, M., Guo, B., & Yan, C. (2014) · Journal of information science and engineering · doi:10.6688/jise.2014.30.3.18 · Cited by 1
Identify the surgically altered face images using granular-PCA approach
Baby, B. (2014) · IOSR Journal of Computer Engineering · doi:10.9790/0661-16510814 · Cited by 1
DEPTH SPATIAL PYRAMID: A POOLING METHOD FOR 3D-OBJECT RECOGNITION
Nakashika, T., Hori, T., Takiguchi, T., et al. (2014) · Cited by 1
Ye, J. C. (2021) · Mathematics in industry · doi:10.1007/978-981-16-6046-7_2 · Cited by 1
Open-Ended Learning of Visual and Multi-Modal Patterns
Luo, J. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-5233 · Cited by 1
A Novel 3D Video Format Identification Algorithm
Su, S., & Chen, Z. (2013) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-00536-2_26 · Cited by 1
Deep Inside Feature Learning for Image Classification Using Transfer Learning Approach
Surendran, R., Anitha, J., & Hemanth, D. J. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220051 · Cited by 1
Three-dimensional projective invariants of points from multiple images
Chen, X., Li, J., & Zhu, Z. (2008) · Optical Engineering · doi:10.1117/1.3028347 · Cited by 1
Feature Point Matching Algorithm Based on Local Relative Velocity Consensus
Shao, F., An, J., & ZhaoXia, L. (2021) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp51882.2021.9408953 · Cited by 1
Research on the Algorithm for Solving the Indoor Vision Positioning Model of Mobile Robot
Zhou, G., Shen, B., & Yan, J. (2018) · International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA) · doi:10.2991/mmsa-18.2018.4 · Cited by 1
Cheeseman, A. K., Tizhoosh, H., & Vrscay, E. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27272-2_13 · Cited by 1
A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues
Shu, C., Li, L., Li, G., et al. (2020) · Applied Sciences · doi:10.3390/app10031156 · Cited by 1
Zhang, X., & Wu, D. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5647945 · Cited by 1
Simsek, E., & Özyer, B. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105732 · Cited by 2
Content-based search in multilingual audiovisual documents using the International Phonetic Alphabet
Quénot, G., Tan, T.-P., Le, V.-B., et al. (2009) · Multimedia Tools and Applications · doi:10.1007/s11042-009-0377-6 · Cited by 1
Medical image retrieval using bag of meaningful visual words
Foncubierta–Rodríguez, A., de Herrera, A. G. S., & Müller, H. (2013) · doi:10.1145/2505323.2505336 · Cited by 1
Intersection distance for object recognition
Zhou, Y., & Liang, J. (2014) · IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) · doi:10.1109/wartia.2014.6976245 · Cited by 1
MDF-YOLO: A Hölder-Based Regularity-Guided Multi-Domain Fusion Detection Model for Indoor Objects
Luan, F., Yang, J., & Zhang, H. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9100673 · Cited by 2
“Lowe’s SIFT performed keypoint detection in scale-space and represented appearance using orientation-aligned local gradient histograms, demonstrating robustness to viewpoint and illumination changes and establishing the foundation for recognition based on local invariant features [31].”
Berger, M., Tscharf, A., & Mayer, G. (2017) · BHM Berg- und Hüttenmännische Monatshefte · doi:10.1007/s00501-017-0604-9 · Cited by 1
Eye Detection Method Using Particle Filter and Gradient Directional Features for Eye Input Device
Asano, M., Takanol, H., & Nakamurat, K. (2011) · International Journal of Intelligent Computing in Medical Sciences & Image Processing · doi:10.1080/1931308x.2008.10644190 · Cited by 1
Bacterial foraging-based SIFT for full moon image direction estimation
Ma, L., & Zhang, Y. A. (2015) · International Journal of Wireless and Mobile Computing · doi:10.1504/ijwmc.2015.068633 · Cited by 1
Geometric-visual descriptor for improved image based localization
Ouni, A., Royer, E., Chevaldonné, M., & Dhome, M. (2020) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip49819.2020.9301831 · Cited by 2
Virtual Reality Control Systems
Reichenbach, T., Vasiljević, G., & Kovačić, Z. (2010) · BiblioBoard Library Catalog (Open Research Library) · doi:10.5772/intechopen.84005 · Cited by 1
A statistical design approach to unsupervised codeword selection in image retrieval
Ji, M., Zhao, W. Z., & Liu, Z. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.10.030 · Cited by 1
Affine Invariant Interesting Descriptors
Kimmel, R., Zhang, C., Bronstein, A. M., et al. (2009) · Cited by 1
Refining object recognition using scene specific object appearance frequencies
Costea, A. D., Varga, R., Mariţa, T., & Nedevschi, S. (2011) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2011.6047866 · Cited by 1
A multichannel optical neural network for advanced machine visionPreprint
Fang, L., Xu, Z., Yuan, X., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1283910/v1 · Cited by 1
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
