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 29,001–29,100 of 56,201
Hüser, M., & Zhang, J. (2011) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2011.07.018 · Cited by 5
A Feature Averaging Method for Kinship Verification
Yadav, N., Goyal, A., & Meenpal, T. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-0617-4_38 · Cited by 5
An efficient approach for dimension selection and classification in HSI images
Srivastava, V., & Biswas, B. (2019) · Remote Sensing Letters · doi:10.1080/2150704x.2019.1623430 · Cited by 5
ARRA: Absolute-Relative Ranking Attack against Image Retrieval
Li, S., Xu, X., Zhou, Z., et al. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548138 · Cited by 5
Vegetation Segmentation in Cornfield Images Using Bag of Words
Campos, Y., Rodner, E., Denzler, J., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48680-2_18 · Cited by 5
Multi-level Semantic Representation for Flower Classification
Lin, C., Yao, H., Yu, W., & Tang, W. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77380-3_31 · Cited by 4
Hu, Z., & Hu, L. (2023) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-023-01912-4 · Cited by 5
Integrated Colormap and ORB detector method for feature extraction approach in augmented reality
Anggara, D. W., Rahim, M. S. M., Ismail, A. W., Wong, S. Y., Ismail, N. A. F., Machfiroh, R., Budiman, A., Rahmansyah, A., & Dahliyusmanto, D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13548-x · Cited by 4
Object concept modeling based on the relationship among appearance, usage and functions
Nakamura, T., & Nagai, T. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5652296 · Cited by 5
Category sensitive codebook construction for object category recognition
Zhang, C., Liu, J., Ouyang, Y., et al. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414450 · Cited by 5
A Fusion-Denoising Attack on InstaHide with Data Augmentation
Luo, X., Xiao, X., Wu, Y., Liu, J., & Ooi, B. C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20084 · Cited by 7
Yang, C., Liu, Y., & Zell, A. (2021) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-021-01439-6 · Cited by 5
A skin surface characterization system based on capacitive image analysis
Gherardi, A. (2008) · AMS Dottorato Institutional Doctoral Theses Repository (University of Bologna) · doi:10.6092/unibo/amsdottorato/1135 · Cited by 5
Autonomous Rover Reflectance Spectroscopy with Dozens of Targets
Calderon, F., Thompson, D. R., & Wettergreen, D. S. (2018) · Figshare · doi:10.1184/r1/6552146 · Cited by 5
Semantic Structure from Motion: A Novel Framework for Joint Object Recognition and 3D Reconstruction
Bao, S. Y., & Savarese, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34091-8_17 · Cited by 5
On the Induction of Topological Maps from Sequences of Colour Histograms
Werner, F., Sitte, J., & Maire, F. (2007) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2007.4426792 · Cited by 5
A fast and robust descriptor for multiple-view object recognition
Rudinac, M., & Jonker, P. P. (2010) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2010.5707414 · Cited by 5
PEDESTRIAN DETECTION SYSTEM USING CASCADED BOOSTING WITH INVARIANCE OF ORIENTED GRADIENTS
Wang, C.-C. R., WU, J.-Y., & Lien, J.-J. J. (2009) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001409007363 · Cited by 5
Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih
Yohannes, Y., Devella, S., & Hadisaputra, W. (2021) · Jurnal Teknik Informatika dan Sistem Informasi · doi:10.28932/jutisi.v7i2.3707 · Cited by 5
Vision System for Robotized Weed Recognition in Crops and Grasslands
Kounalakis, T., Triantafyllidis, G. A., & Nalpantidis, L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68345-4_43 · Cited by 5
A More Robust Feature Correspondence for more Accurate Image Recognition
El-Mashad, S. Y., & Shoukry, A. (2014) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2014.32 · Cited by 6
MultiFacet: A Faceted Interface for Browsing Large Multimedia Collections
Henry, M. J., Hampton, S. D., Endert, A., et al. (2013) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2013.66 · Cited by 5
Pencil Drawing of Microscopic Images Through Edge Preserving Filtering
Singh, H., Sánchez, C., Cristóbal, G., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31321-0_17 · Cited by 5
Corneal imaging in localization and HMD interaction
Plopski, A., Kiyokawa, K., Takemura, H., et al. (2014) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2014.6948505 · Cited by 5
Bottom-Up and Top-Down Object Matching Using Asynchronous Agents and a Contrario Principles
Burrus, N., Bernard, T. M., & Jolion, J.-M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-79547-6_33 · Cited by 5
Indoor location recognition using fusion of SVM-based visual classifiers
Sjöberg, M., Koskela, M., Viitaniemi, V., et al. (2010) · IEEE International Workshop on Machine Learning for Signal Processing (MLSP) · doi:10.1109/mlsp.2010.5589019 · Cited by 5
A Rapid Automatic Image Registration Method Based on Improved SIFT
Hongbo, Z., Xu, X., Wang, J., et al. (2011) · Procedia Environmental Sciences · doi:10.1016/j.proenv.2011.12.014 · Cited by 5
Markerless Visual Tracking for Augmented Books
Cho, K., Yoo, J., & Yang, H. S. (2009) · Eurographics · doi:10.2312/egve/jvrc09/013-020 · Cited by 5
Local spatio-temporal interest point detection for human action recognition
Li, F., & Du, J. (2012) · IEEE International Conference on Advanced Computational Intelligence · doi:10.1109/icaci.2012.6463231 · Cited by 5
Condition-invariant and compact visual place description by convolutional autoencoder
Ye, H., Chen, W., Yu, J., He, L., Guan, Y., & Zhang, H. (2023) · Robotica · doi:10.1017/s0263574723000085 · Cited by 5
Moradi, S., Moradi, J., Aghaziyarati, S., et al. (2022) · International Journal of Image and Graphics · doi:10.1142/s0219467824500141 · Cited by 5
Endoscopic video deblurring via synthesis
Peng, L., Liu, S., Xie, D., et al. (2017) · IEEE Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2017.8305021 · Cited by 5
A Novel 3D Convolutional Neural Network for Action Recognition in Infrared Videos
Nie, J., Yan, L., Wang, X., et al. (2021) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp54369.2021.9611896 · Cited by 5
The effectiveness of detector combinations
Li, Z., Gong, W., Nee, A. Y. C., et al. (2009) · Optics Express · doi:10.1364/oe.17.007407 · Cited by 5
Qi, Y., & Cao, H. (2025) · IEEE Access · doi:10.1109/access.2025.3578737 · Cited by 5
Screen recording segmentation to scenes for eye-tracking analysis
Šimko, J., & Vrba, J. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6369-7 · Cited by 5
RGB-D SLAM Combining Visual Odometry and Extended Information Filter
Zhang, H., Liu, Y., Tan, J., & Xiong, N. N. (2015) · Sensors · doi:10.3390/s150818742 · Cited by 5
Three-dimensional measurement of large-scale texture-less bending plates
Bai, S., Shi, J., Qian, Q., et al. (2015) · Journal of Electronic Imaging · doi:10.1117/1.jei.24.1.013001 · Cited by 5
Widget Detection-based Testing for Industrial Mobile Games
Wu, X., Ye, J., Chen, K., et al. (2023) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip58684.2023.00021 · Cited by 5
Multiple Face Recognition Using Local Features and Swarm Intelligence
Chidambaram, C., Neto, H. V., Dorini, L. E. B., et al. (2014) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e97.d.1614 · Cited by 5
Indoor Scene recognition for Micro Aerial Vehicles Navigation using Enhanced SIFT-ScSPM Descriptors
Anbarasu, B., & Anitha, G. F. S. (2019) · Journal of Navigation · doi:10.1017/s0373463319000420 · Cited by 6
Looking into the world on Google Maps with view direction estimated photos
Li, H., Tang, J., Wang, Y., et al. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.06.040 · Cited by 5
Using Concept Recognition to Annotate a Video Collection
Mallik, A., & Chaudhury, S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-11164-8_82 · Cited by 5
Automatic Relevance Determination for the Estimation of Relevant Features for Object Recognition
Ulusoy, İ., & Bishop, C. M. (2006) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2006.1659843 · Cited by 5
Maximum kernel density estimator for robust fitting
Wang, H. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4518377 · Cited by 5
A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition
Hayee, S., Hussain, F., & Yousaf, M. H. (2023) · Sensors · doi:10.3390/s23187920 · Cited by 6
NLOOK: a computational attention model for robot vision
Heinen, M. R., & Engel, P. M. (2009) · Journal of the Brazilian Computer Society · doi:10.1007/bf03194502 · Cited by 5
A review of UAV-based data applications for landslide mapping and monitoring
Ilinca, V., & Sandric, I. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-823868-4.00003-9 · Cited by 5
Research on Binocular Vision Image Calibration Method Based on Canny Operator
Yan, L. (2024) · Automatic Control and Computer Sciences · doi:10.3103/s0146411624700585 · Cited by 5
“(11) can be written as two homogeneous equations [24–26]: (11) Equation (11) is two constraint equations for camera internal parameters. i Assuming that n calibration images are shot, 2 n constraint equations will be generated, which can be expressed by Eq.”
An, X., Hong, W., & Xia, H. (2015) · Chinese Automation Congress · doi:10.1109/cac.2015.7382571 · Cited by 5
Long Term Hand Tracking with Proposal Selection
Chen, Q., & Zhu, F. M. (2018) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2018.8551542 · Cited by 5
Gyamfi, E. O., Qin, Z., Danso, J. M., et al. (2024) · Information · doi:10.3390/info15100602 · Cited by 5
Towards Automatic Face Recognition in Unconstrained Scenarios
Sarfraz, M. S. (2009) · DepositOnce · doi:10.14279/depositonce-2125 · Cited by 5
Change Detection via Graph Matching and Multi-View Geometric Constraints
Shen, J., Lyu, S., Zhang, X., et al. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803527 · Cited by 5
An Analysis of Object Embeddings for Image Retrieval.Preprint
Chen, B.-C., Davis, L. S., & Lim, S.-N. (2019) · arXiv (Cornell University) · Cited by 4
DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based mapping
Chen, Z., Li, J., Li, Q., et al. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104190 · Cited by 6
Fine-Grained Vehicle Recognition in Traffic Surveillance
Wang, Q., Wang, Z., Xiao, J., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48890-5_28 · Cited by 5
Quantifying Visual-Representativeness of Social Image Tags Using Image Tag Clarity
Sun, A., & Bhowmick, S. S. (2011) · Springer eBooks · doi:10.1007/978-0-85729-436-4_1 · Cited by 5
Scalable ranked retrieval using document images
Jain, R., Oard, D. W., & Doermann, D. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2038656 · Cited by 5
A Study of Household Object Recognition Using SIFT-Based Bag-of-Words Dictionary and SVMs
Sampath, A., Sivaramakrishnan, A., Narayan, K., et al. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2671-0_55 · Cited by 5
Cheng, L., Jiang, Z., Li, H., et al. (2014) · Robotica · doi:10.1017/s0263574714001970 · Cited by 5
Is Repeatability an Unbiased Criterion for Ranking Feature Detectors?
Rey-Otero, I., & Delbracio, M. (2015) · SIAM Journal on Imaging Sciences · doi:10.1137/15m1007732 · Cited by 5
Min, Z., Hassan, G. M., & Jo, G. (2024) · IEEE Access · doi:10.1109/access.2024.3432156 · Cited by 5
IconChecker: Anomaly Detection of Icon-Behaviors for Android Apps
Li, Y., Feng, R., Chen, S., et al. (2021) · Proceedings - Asia Pacific Software Engineering Conference/Proceedings · doi:10.1109/apsec53868.2021.00028 · Cited by 5
Video Copy Detection Based on Path Merging and Query Content Prediction
Nan, N., & Liu, G. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2395771 · Cited by 5
Use of Deep Learning for Firearms Detection in Images
Cardoso, G. V. S., Ciarelli, P. M., & Vassallo, R. F. (2019) · doi:10.5753/wvc.2019.7637 · Cited by 5
Inclined Image Recognition for Aerial Mapping by Unmanned Aerial Vehicles
Attamimi, M., Mardiyanto, R., & Irfansyah, A. N. (2018) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia.2018.8710975 · Cited by 5
ROS-based ground stereo vision detection: implementation and experiments
Hu, T., Zhao, B., Tang, D., et al. (2016) · Robotics and Biomimetics · doi:10.1186/s40638-016-0046-y · Cited by 5
Performance Evaluation of SIFT and Convolutional Neural Network for Image Retrieval
Devi, V. S. V., Baber, J., Bakhtyar, M., et al. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.081268 · Cited by 5
Finding Suits in Images of People
Huang, L., Xia, T., Zhang, Y., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-27355-1_45 · Cited by 5
Intrusion detection of railway clearance from infrared images using generative adversarial networks
Li, Y., Liu, Y., Han, D., et al. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-192141 · Cited by 5
Xie, J., Ren, C., Jiao, H., et al. (2021) · IET Image Processing · doi:10.1049/ipr2.12306 · Cited by 5
Surgical video retrieval using deep neural networks
Varytimidis, C., Rapantzikos, K. E., Loukas, C., & Kollias, S. (2016) · Lincoln Repository (University of Lincoln) · Cited by 6
Ivelja, T., Bechor, B., Hasan, O., et al. (2020) · 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-xliii-b1-2020-457-2020 · Cited by 5
Optimal Number of Image Keypoints for Real Time Visual Odometry
Nannen, V., & Oliver, G. (2012) · IFAC Proceedings Volumes · doi:10.3182/20120410-3-pt-4028.00055 · Cited by 5
Multi-scale local features based on anisotropic heat diffusion and global eigen-structure
Li, S., Qin, H., & Hao, A. (2012) · Science China Information Sciences · doi:10.1007/s11432-012-4678-4 · Cited by 4
Fan, W., & Bouguila, N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35527-1_17 · Cited by 5
iMatching: Imperative Correspondence Learning
Zhan, Z., Gao, D., Lin, Y.-J., Xia, Y., & Wang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72933-1_11 · Cited by 6
Analysis of Deep Feature Matching Algorithms in UAV Visual Localization
Prutyanov, V. V., Ternov, M. A., & Kostrov, D. S. (2024) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon61949.2024.10694214 · Cited by 5
“Then SIFT algorithm [30] is used to extract key points which are compared with the points of the vector map image.”
Method3D human modeling using virtual multi-view stereopsis and object-camera motion estimation
Lam, D., Hong, R. Z., & DeSouza, G. N. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354040 · Cited by 5
Using Redundant Bit Vectors for Near-Duplicate Image Detection
Foo, J. J., & Sinha, R. (2007) · Springer eBooks · doi:10.1007/978-3-540-71703-4_41 · Cited by 5
Vision-Based Terrain Relative Navigation on High Altitude Balloon and Sub-Orbital Rocket
Maggio, D. R., Mario, C., Streetman, B. J., Steiner, T. J., & Carlone, L. (2023) · AIAA SciTech Forum · doi:10.2514/6.2023-0875 · Cited by 5
Unsupervised Learning of Landmarks by Descriptor Vector Exchange
Thewlis, J., Albanie, S., Bilen, H., & Vedaldi, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00646 · Cited by 7
Learning Optical Expansion from Scale Matching
Ling, H., Sun, Y., Sun, Q., et al. (2023) · 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/cvpr52729.2023.00527 · Cited by 5
Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models
Wang, S., Ji, Z., Xiang, L., Zhang, H., Wang, X., Zhou, C., & Li, B. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.23326 · Cited by 5
Detailed 3D mapping based on image edge-point ICP and recovery from registration failure
Tomono, M. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354114 · Cited by 5
A Simple and Practical Solution to the Rigid Body Motion Segmentation Problem Using a RGB-D Camera
Perera, S., & Barnes, N. M. (2011) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2011.89 · Cited by 5
Video Query Reformulation for Near-Duplicate Detection
Chiu, C., Li, S., & Hsieh, C.-Y. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2207115 · Cited by 5
Joshi, P., Rastegarpanah, A., & Stolkin, R. (2021) · Computers & Graphics · doi:10.1016/j.cag.2021.01.014 · Cited by 5
Dasari, S., Dorn, C. J., Yang, Y., et al. (2017) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-319-54735-0_10 · Cited by 5
Architecture and Tracking Algorithms for a Distributed Mobile Industrial AR System
Koch, R., Evers‐Senne, J., Schiller, I., et al. (2019) · doi:10.2390/biecoll-icvs2007-87 · Cited by 5
Visual Interaction for Real-Time Navigation of Autonomous Mobile Robots
Vedova, M. L. D., Facchinetti, T., Ferrara, A., et al. (2009) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2009.24 · Cited by 5
Zhang, S., Yu, H. L., Wang, T., et al. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105699 · Cited by 5
Adaptive Client-Side LUT-Based Digital Watermarking
Pun, C., Jiang, J., & Chen, C. L. P. (2011) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2011.104 · Cited by 5
An efficient multistage CBIR based on Squared Krawtchouk-Tchebichef polynomials
Hameed, I. M., & Abdulhussain, S. H. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1090/1/012100 · Cited by 5
On shortened 3D local binary descriptors
Quan, S., & Ma, J. (2019) · Information Sciences · doi:10.1016/j.ins.2019.09.028 · Cited by 5
Bag-of-Words Against Nearest-Neighbor Search for Visual Object Retrieval
Zhu, C.-Z., Zhou, X., & Satoh, S. (2013) · IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2013.56 · Cited by 5
Go with the flow: Optical flow-based transport operators for image manifolds
Sankaranarayanan, A. C., Hegde, C., Nagaraj, S., & Baraniuk, R. G. (2011) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2011.6120390 · Cited by 4
Selective variance based kinship verification in parent's childhood and their children
Oruganti, M., Meenpal, T., & Majumder, S. (2021) · National Conference on Communications · doi:10.1109/ncc52529.2021.9530084 · Cited by 5
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