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 38,601–38,700 of 56,202
ColourFAST: GPU-based feature point detection and tracking on mobile devices
Ensor, A., & Hall, S. (2013) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2013.6727003 · Cited by 2
Improved CT algorithm based on target block division and feature points matching
Wang, W., Jiang, M., Yan, Y., et al. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0302-x · Cited by 2
Öksüz, C., Narter, A., Ece, B., et al. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109857 · Cited by 2
Discriminative Spatial Tree for Image Classification
Xu, Y., Yu, X., Wang, T., et al. (2017) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud.2017.53 · Cited by 2
Graph-Based Multimodal Topic Modeling With Word Relations and Object Relations
Zhu, B., Cai, Y., & Wang, J. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3378173 · Cited by 3
Development of a Robust Monocular-Based Vehicle Detection and Tracking System
Teoh, S. S. (2011) · Cited by 2
[POSTER] Marker Identification Using IR LEDs and RGB Color Descriptors
Koutaki, G., Hirata, S., Sato, H., et al. (2015) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2015.30 · Cited by 2
Object Detection Algorithms with Deep Learning: An Overview
Tang, W. D., Chen, J., Ning, Y., & Xu, K. (2023) · International Conference on Electronic Information Engineering and Computer Science (EIECS) · doi:10.1109/eiecs59936.2023.10435579 · Cited by 2
A comparative study of finger vein recognition by using Learning Vector Quantization
Khusnuliawati, H., Fatichah, C., & Soelaiman, R. (2017) · IPTEK Journal of Proceedings Series · doi:10.12962/j23546026.y2017i2.2311 · Cited by 2
Accurately matching serial whole slide images for tumor heterogeneity analysis
Li, X., Mei, L., Long, M., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106825 · Cited by 2
Review on Applicability of Vision-based Detection Technology in Field Obstacle Detection
Wang, Q., Meng, Z., & Liu, H. (2020) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/474/3/032017 · Cited by 2
Pose Estimation Method Based on Bidirectional Recurrent Neural Network in Visual OdometryPreprint
Yu, Y., Hua, C., Li, R., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4155315 · Cited by 2
Hierarchical multi-view metric learning with HSIC regularization
Deng, H., Meng, X., Wang, H., et al. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.073 · Cited by 2
Multi-layer feature extractions for image classification — Knowledge from deep CNNs
Ueki, K., & Kobayashi, T. (2015) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2015.7313925 · Cited by 2
Uncooperative Spacecraft Pose Estimation With Normalized Segmentation Coordinate Space
Zhong, L., Chen, S., Wang, W., Yang, W., Yuan, G., Guo, P., Yang, X., & Zhang, X. (2024) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2024.3442570 · Cited by 2
Development of an Algorithm to Artificially Create Virtual Brain Deformations for Brain DICOM
Mori, T., Nonaka, M., Kunii, T., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05409-9_30 · Cited by 2
FNon R-CNN: A multi-scale ground object detection and recognition network
Yan, Z., Zhu, W., Zhong, X., et al. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100359 · Cited by 2
Improving bag-of-words representation with efficient twin feature integration
Wang, L., & Wang, H. (2015) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2808492.2808543 · Cited by 2
Discovering Semantics from Visual Information
Wang, Z., & Feng, D. D. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch808 · Cited by 2
Texture Recognition Using InceptionNeXt-based Texture Perception Network
Sahoo, T. K., & Mawari, G. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10725462 · Cited by 2
A camera calibration method based on scene and human semantics
Shu, Y., & Wang, Y. (2022) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac57282.2022.10023705 · Cited by 2
Proposal of an Encoded Marker for Working Robots
Kobori, N., Deguchi, D., Ide, I., et al. (2016) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.136.1367 · Cited by 2
Mei, T., & Rui, Y. (2009) · Encyclopedia of Database Systems · doi:10.1007/978-0-387-39940-9_1014 · Cited by 2
Object tracking using parallel local colour histogram method
Panda, C. S., Mallick, P. K., & Patnaik, S. (2010) · International Journal of Computational Vision and Robotics · doi:10.1504/ijcvr.2010.038191 · Cited by 2
Majhi, M., & Mallick, A. K. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21337-z · Cited by 3
“● Scale-invariant feature transform (SIFT) : The SIFT scheme is a powerful frame - work described by authors of [75, 76].”
Vision-Based Human Activity Recognition
Mahmud, T., & Hasan, M. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-68590-4_1 · Cited by 2
YOLO-Night: Lighting the Path for Autonomous Vehicles with Robust Nighttime Perception
Tian, J., Ghaffar, M. A., & Li, Z. (2026) · Sensors · doi:10.3390/s26041138 · Cited by 2
“Early object detection methods relied on handcrafted features such as Histogram of Oriented Gradients (HOG) [14] and Scale-Invariant Feature Transform (SIFT) [15], which https://doi.org/10.3390/s26041138 demonstrated reasonable performance under well-illuminated and high-quality imaging conditions.”
InfluentialXu, X., Yang, P., Ran, B., Xian, H., & Liu, Y. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-191649 · Cited by 2
SIFT Feature Image Stitching Based on Improved Cuckoo Algorithm
Li, J., & Liu, D. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/782/3/032100 · Cited by 2
Jin, B., & Yang, F. (2018) · Global Intelligent Industry Conference (GIIC) · doi:10.1117/12.2504048 · Cited by 2
Initial Registration Method of Point Cloud Based on Sequence Images
Dianzhu, S. U. N., Jianghua, S., Yanrui, L., & Wei, L. (2020) · Journal of Mechanical Engineering · doi:10.3901/jme.2020.09.215 · Cited by 2
Semantic Digital Twin Modeling of Urban Elements: Methods and Applications in Smart Cities
Li, Y., Yang, J. Y., Zhang, S., Li, Y., Ye, S., Qin, Z., & Guan, Y. (2025) · Transactions in GIS · doi:10.1111/tgis.70144 · Cited by 2
“A 3D point cloud model is then constructed using the Delaunay triangulation method, and interpolation algorithms such as Kriging and IDW are applied to derive elevation data (Lowe 2004).”
Fast Visual Object Identification and Categorization
Grabner, M., Gräbner, H., & Bischof, H. (2008) · Cited by 2
Object detection in gray scale images based on invariant polynomial features
Schindler, A., & Maier, G. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5649524 · Cited by 2
Bag of Freebies Techniques to Improve Face Detection Accuracy for Small Faces
Ebrahimian, N. S., Ashouri, A., Akhoundzade, R., & Mohammadi, M. R. (2024) · doi:10.1109/icee63041.2024.10667746 · Cited by 2
Size-Sensitive Optimization of Loss Function on Vision-based Object Detection
Li, T., Shu, X., Chen, G., et al. (2021) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3501409.3501689 · Cited by 2
A Deep Model Combining Structural Features and Context Cues for Action Recognition in Static Images
Wang, X., Li, K., & Li, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70136-3_66 · Cited by 2
COLLECTIVE LEARNING OF CONCEPTS USING A ROBOT TEAM
Palacios-García, A. C., Muñoz-Meléndez, A., & Morales, E. F. (2010) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0002952800790088 · Cited by 2
PERFORMANCE OF LOCALIZATION BASED ON VISUAL SLAM AND LIDAR POINT CLOUD REGISTRATION
Lu, P. R., Chiang, K., Lin, Y.-J., et al. (2023) · 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-xlviii-1-w1-2023-257-2023 · Cited by 2
Salient object detection in video streams
Țapu, R., Mocanu, B., & Tapu, E. (2012) · International Symposium on Electronics and Telecommunications (ISETC) · doi:10.1109/isetc.2012.6408089 · Cited by 2
Indoor Localization Using Cluster Analysis
Naga, R. A., Elias, R., & ElNahas, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20915-5_1 · Cited by 3
UAS multispectral imaging for detecting plant stress due to iron chlorosis in grain sorghum
Garcia, I. A. (2018) · ProQuest Demo Repository · Cited by 2
On Challenges of Monocular Pose Estimation for Endoluminal Navigation
Mackutė, E., Abdalla, A., Dickson, S., et al. (2024) · Journal of Medical Robotics Research · doi:10.1142/s2424905x24400099 · Cited by 2
Perzanowski, A., & Lindeberg, T. (2025) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-025-01245-x · Cited by 2
“…properties of the Gaussian scale-space, previously explored for constructing scale-covariant and scale-invariant feature detectors in classical computer vision, see Lindeberg (1998a; 1998b; 2021b), Bretzner and Lindeberg (1998), Lowe (2004), and Bay et al. (2008) for examples of such approaches.”
Dong, X., Feng, Q., Xia, J., et al. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1631/1/012045 · Cited by 2
Evaluation of Image Descriptors for Retrieval of Similar Images
Frączek, R., & Cyganek, B. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-24809-2_13 · Cited by 2
Face Synthesis and Partial Face Recognition from Multiple Videos
Nualtim, W., Suwansantisuk, W., & Kumhom, P. (2023) · Engineering Journal · doi:10.4186/ej.2023.27.4.29 · Cited by 2
Exploiting Part-Based Models and Edge Boundaries for Object Detection
Sullivan, J., Danielsson, O., & Carlsson, S. (2008) · Digital Image Computing: Techniques and Applications · doi:10.1109/dicta.2008.88 · Cited by 2
Fast Implementation of a New Radial Symmetry Measure for Photogrammetry
Fuciños, M., López, J., Pardo, X. M., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38628-2_26 · Cited by 2
Blind DIBR-synthesized Image Quality Assessment using multi-scale DoG and GRNN
Sandic-Stankovic, D. D., Bokan, D., & Kukolj, D. (2018) · Symposium on Neural Networks and Applications (NEUREL) · doi:10.1109/neurel.2018.8587020 · Cited by 2
Oil spill segmentation in fused Synthetic Aperture Radar images
Longman, F. S., Mihaylova, L. S., & Coca, D. (2016) · International Conference on Control, Engineering & Information Technology · doi:10.1109/ceit.2016.7929055 · Cited by 2
An Autonomous Selection Method for Spatial Pooling Based on Chi-Square Test
Liu, Z., & Yu, Y. (2018) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2018.8812586 · Cited by 2
A Projector Calibration Method Using a Mobile Camera for Projection Mapping System
Xie, C., Shishido, H., Kameda, Y., et al. (2019) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct.2019.00-33 · Cited by 2
Brain Neuron Network Extraction and Analysis of Live Mice from Imaging Videos
Yu, R., Lai, J.-H., Wang, S.-X., & Lin, C. (2017) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2017070101 · Cited by 2
Benabdelaziz, R., Gaceb, D., & Haddad, M. (2019) · International Journal of Computer Vision and Image Processing · doi:10.4018/ijcvip.2019070103 · Cited by 2
La visión artificial, un nuevo aliado para el análisis de imágenes artísticas
Rodrigo, P. R., Ferrer, E. F., Planas-Rosselló, M., et al. (2016) · Arte individuo y sociedad · doi:10.5209/rev_aris.2016.v28.n2.48802 · Cited by 2
Interest points based object tracking with controlled cameras
Xu, Z., Wu, H. R., & Yu, X. (2009) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2009.4939565 · Cited by 2
Scalable Feature Extraction for Visual Surveillance
Nazare, A. C., Ferreira, R., & Schwartz, W. R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12568-8_46 · Cited by 2
Users Personalized Sketch-Based Image Retrieval Using Deep Transfer Learning
Huo, Q., Wang, J., Qi, Q., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99365-2_14 · Cited by 2
Exploiting class label in generative score spaces
Wang, B., Wang, C., & Liu, Y. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.05.004 · Cited by 2
Pedestrian Target Tracking Algorithm Based on Improved RANSAC and KCF
Li, W., Wu, J., Lu, H., et al. (2020) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc49329.2020.9164874 · Cited by 2
Accurate multi-spectral image registration based on scale invariant feature
Zhang, H., Liu, X., Li, L., et al. (2012) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt.2012.6526062 · Cited by 2
AANet: Motorcycle ReID Using Multi-Atrous Convolution and Self-Attention Mechanisms
Nguyen-Mau, T.-H., Phu-Thi, K.-T., Le-Dinh, A.-D., et al. (2023) · International Conference on Multimedia Analysis and Pattern Recognition (MAPR) · doi:10.1109/mapr59823.2023.10288740 · Cited by 2
Smooth localization independent of GPS using coarse height maps
Wei, C., Wu, T. T., & Fu, H. (2015) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2015.7162620 · Cited by 2
Multi-scale matching for data association in vision-based SLAM
Chen, L., Pei, M., & Yang, J. (2010) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2010.5723496 · Cited by 2
Partially occluded human detection by boosting SVM
Tang, S., & Goto, S. (2009) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa.2009.5069221 · Cited by 2
FACA v1 – Fully Automated Co-Alignment of UAV point clouds
Schüßler, N., Torizin, J., Gunkel, C., et al. (2025) · Geoscientific model development · doi:10.5194/gmd-18-5913-2025 · Cited by 2
“In the case of Agisoft Metashape a proprietary variant (Semyonov, 2011) of the broadly used SIFT algorithm (Lowe, 2004) is employed.”
Pedestrian Tracking from a Moving Host Using Corner Points
Meuter, M., Müller, D., Müller-Schneiders, S., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-76856-2_36 · Cited by 2
An Overview of Modern Classification Methods of Histopathological Breast Cancer Scans
Niedziela, P., & Cyganek, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70595-3_17 · Cited by 2
2DIGH: a polar invariant local image descriptor based on joint histogram
Sadeghi, B., Jamshidi, K., Vafaei, A., et al. (2017) · The Visual Computer · doi:10.1007/s00371-017-1433-2 · Cited by 2
Designing a resource-allocating codebook for patch-based visual object recognition
Ramanan, A. (2010) · ePrints Soton (University of Southampton) · Cited by 2
Image Background Matching for Identifying Suspects
Fogg, P. N., Peterson, G. L., & Veth, M. J. (2008) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-0-387-84927-0_24 · Cited by 2
Human Motion Tracking in Video
Tung, T., & Matsuyama, T. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-900-7.ch001 · Cited by 2
A novel heuristic target-dependent neural architecture search method with small samples
Fu, L., Li, S., Rao, Y., Liang, J., Jie, T., & He, Q. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.897883 · Cited by 3
Wang, X., Fairhurst, M. C., & Canuto, A. M. P. (2020) · Intelligent Data Analysis · doi:10.3233/ida-194798 · Cited by 2
Preprint ARPPS Augmented Reality Pipeline Prospect SystemPreprint
Zhang, X., Han, Y., Hao, D., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.04238 · Cited by 2
An Intelligent Tracking System for Moving Objects in Dynamic Environments
Hakami, N. A., Mahmoud, H. A. H., & AlArfaj, A. A. (2022) · Actuators · doi:10.3390/act11100274 · Cited by 2
The Implementation of Fast Object Recognition Using Parallel Processing on CPU and GPU
Kim, J.-C. (2009) · Journal of Institute of Control Robotics and Systems · doi:10.5302/j.icros.2009.15.5.488 · Cited by 2
Loncomilla, P., & Ruíz-del-Solar, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35699-6_12 · Cited by 2
On scale invariant features and sequential Monte Carlo sampling for bronchoscope tracking
Luó, X., Feuerstein, M., Kitasaka, T., et al. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877515 · Cited by 2
Hierarchical Denoising Method of Crop 3D Point Cloud Based on Multi-view Image Reconstruction
Chen, L., Yuan, Y., & Song, S. (2019) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-06137-1_38 · Cited by 2
Reference Point-Based SIFT Feature Matching
Cao, L., Liao, D., & Xue, B. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.543-547.2670 · Cited by 2
Object class recognition with supervised nonlinear neighborhood embedding of visual words
Han, X., Chen, Y., & Ruan, X. (2009) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/1734605.1734615 · Cited by 2
Content-Based Image Retrieval Using Multiresolution Feature Descriptors
Srivastava, P., & Khare, A. (2018) · Studies in computational intelligence · doi:10.1007/978-3-030-03000-1_9 · Cited by 2
Hybrid framework for single-pointer meter identification
Xiao, Z., Chen, W., & Xu, G. (2023) · Applied Optics · doi:10.1364/ao.483386 · Cited by 2
Learning Representative Features for Robot Topological Localization
Zhao, Z., Feng, X., Fang, W., et al. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/56324 · Cited by 2
Affine Correction Based Image Watermarking Robust to Geometric Attacks
Zhang, W., Chen, J., Wang, R., et al. (2016) · International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) · doi:10.1109/pdcat.2016.046 · Cited by 2
Multilayer Ferns: A Learning-based Approach of Patch Recognition and Homography Extraction
Gao, C., Yixu, S., & Peifa, J. (2010) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2010.36 · Cited by 2
Ly, N. Q., Thành, N. T., & Bao, L. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110406 · Cited by 2
Si, L., Chen, M., Li, J., et al. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae0e9d · Cited by 2
“Traditional object detection algorithms primarily rely on manually designed features [25], such as scale-invariant feature transform [26] and local binary patterns [27].”
Bauaufnahme, Gebäudeerfassung und BIM
Blankenbach, J. (2017) · Springer Reference Naturwissenschaften · doi:10.1007/978-3-662-47188-3_36 · Cited by 2
Efficient Data-Driven MCMC sampling for vision-based 6D SLAM
Min, J., Kim, J., Shin, S., et al. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6225135 · Cited by 2
Koyama, J., Makar, M. A., Araújo, A. D. M., et al. (2014) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2014.6890631 · Cited by 2
Li, Z., & Wu, B. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3594413 · Cited by 2
Joint Forward-Backward Visual Odometry for Stereo Cameras
Sardana, R., Kottath, R., Karar, V., et al. (2019) · doi:10.1145/3352593.3352651 · Cited by 2
Ocular and Periocular Biometrics
Jillela, R. R., & Ross, A. A. (2016) · Wiley Encyclopedia of Electrical and Electronics Engineering · doi:10.1002/047134608x.w8307 · Cited by 2
Continuous piecewise affine transformation for image registration
Lee, Y.-S., Hwang, W.-L., & Tian, X. (2015) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s021969131550006x · Cited by 2
T-SLAM: Registering Topological and Geometric Maps for Robot Localization
Ferreira, F., Amorim, I., Rocha, R. P., et al. (2009) · Lecture notes in electrical engineering · doi:10.1007/978-3-540-89859-7_29 · Cited by 2
Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
Liu, Q., Xie, X., Zhang, X., et al. (2020) · Applied Sciences · doi:10.3390/app10217632 · Cited by 2
On Inferring Image Label Information Using Rank Minimization for Supervised Concept Embedding
Bespalov, D., Dahl, A. L., Bai, B., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21227-7_10 · 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
