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 39,101–39,200 of 56,201
Additive margin cosine loss for image registration
Ma, Y., Sun, S., Wu, F., Yang, Y., Yang, X., Xu, B., & Luo, Z. (2021) · The Visual Computer · doi:10.1007/s00371-021-02105-6 · Cited by 2
Zhang, X., Ji, X., Cao, P., & Xiao, Q. (2026) · Journal of Business Research · doi:10.1016/j.jbusres.2026.116003 · Cited by 6
Condition-Invariant Robot Localization Using Global Sequence Alignment of Deep Features
Oh, J., Han, C., & Lee, S. (2021) · Sensors · doi:10.3390/s21124103 · Cited by 2
Springer, D., Frank, M., Simmet, F., et al. (2013) · Picture Coding Symposium · doi:10.1109/pcs.2013.6737718 · Cited by 2
Deep Structure, Singularities, and Computer Vision
Somchaipeng, K., Sporring, J., Kreiborg, S., et al. (2004) · Cited by 2
Gaussian-Bernoulli Based Convolutional Restricted Boltzmann Machine for Images Feature Extraction
Li, Z., Cai, X., & Liang, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46672-9_66 · Cited by 2
Efficient Object Embedding for Spliced Image RetrievalPreprint
Chen, B.-C., Wu, Z., Davis, L. S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11903 · Cited by 2
DENSE IMAGE MATCHING WITH TWO STEPS OF EXPANSION
Zhang, Z., He, J., Huang, S., et al. (2016) · 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-xli-b3-143-2016 · Cited by 2
3D reconstruction from a single family camera
Shen, Y., Peng, P., & Gao, W. (2012) · IEEE International Conference on Advanced Computational Intelligence · doi:10.1109/icaci.2012.6463131 · Cited by 2
Yan, C. Y., & Peng, W. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4767705 · Cited by 2
An AI Approach to Measuring Resident-on-Resident Physical Aggression In Nursing Homes
Chen, D., Wactlar, H. D., Bharucha, A. J., et al. (2008) · Cited by 2
Benefits of 3D topos for information sharing and planning in rock climbing
Kajastila, R., & Hämäläinen, P. (2014) · Sports Technology · doi:10.1080/19346182.2014.968249 · Cited by 2
An unsupervised method for anomaly detection from crowd videos
Güler, P., Temizel, A., & Temizel, T. T. (2013) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2013.6531292 · Cited by 2
Pfeifer, N., Mandlburger, G., & Glira, P. (2017) · Springer eBooks · doi:10.1007/978-3-662-47094-7_44 · Cited by 2
Sreela, S. R., & Idicula, S. M. (2017) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tenconspring.2017.8070036 · Cited by 2
UAS Navigation in the Real World Using Visual Observation
Han, Y., Wei, J., & Yılmaz, A. (2022) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors52175.2022.9967103 · Cited by 2
Experimental study of image representation spaces in variational disparity calculation
Ralli, J., Díaz, J., Guzmán, P., & Ros, E. (2012) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/1687-6180-2012-254 · Cited by 2
C-FCN: Corners-based fully convolutional network for visual object detection
Jiao, L., Wang, R., & Xie, C. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09503-3 · Cited by 2
The Impact of Parameters on the Efficiency of Keypoints Detection and Description
Fesiuk, A., & Furgala, Y. (2023) · IEEE International Conference on Electronics and Information Technologies (ELIT) · doi:10.1109/elit61488.2023.10310866 · Cited by 2
SCOD: Dynamical Spatial Constraints for Object Detection
Zhang, K., Guo, C., Niu, Z.-H., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05710-7_2 · Cited by 2
A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission
Yu, D., Liu, P., Qiao, D., & Tang, X. (2021) · Algorithms · doi:10.3390/a14060188 · Cited by 2
Dual Convolutional Neural Network for Lung Nodule Classification
Shi, P., Yu, W., Liu, Y., et al. (2021) · doi:10.1109/ijcnn52387.2021.9533336 · Cited by 2
Vision Based Micro-UAV Navigation Through Narrow Passages
Kumar, J., ., H., Kandath, H., et al. (2023) · International Conference on Control, Mechatronics and Automation (ICCMA) · doi:10.1109/iccma59762.2023.10375021 · Cited by 2
Modeling Hidden Topics with Dual Local Consistency for Image Analysis
Li, P., Cheng, J., & Lu, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37331-2_49 · Cited by 2
Interest points matching system based On Non-Subsampled Contourlet Transform
Qatran, M. A., Mahar, K., & Ismail, O. (2010) · International Conference on Computer Technology and Development · doi:10.1109/icctd.2010.5645876 · Cited by 2
Multi-channel Glow network pre-trained on white-balance dataset for underwater image enhancement
Takao, S., Watanabe, K., & Kobayashi, T. (2025) · Signal Processing · doi:10.1016/j.sigpro.2025.110165 · Cited by 2
Collision simulation of trucks and pedestrians based on Pc-Crash in the blind area of truck driving
Zhang, Z. (2021) · International Conference on Traffic Engineering and Transportation System (ICTETS) · doi:10.1117/12.2620026 · Cited by 2
Small object detection with random decision forests
Ma, J., Pan, Q., Hu, J., Zhao, C., Guo, Y., & Wang, D. (2017) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus.2017.8278409 · Cited by 2
Simultaneous localization and mapping with learned object recognition and semantic data association
Rogers, J. G., Trevor, A. J. B., Nieto-Granda, C., et al. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6048722 · Cited by 2
CLASSIFICATION OF VIDEO-OBJECTS IN ATTRIBUTE SPACE OF THE WALSH FUNCTIONS
Vlasenko, N., & Sytnik, O. V. (2013) · Telecommunications and Radio Engineering · doi:10.1615/telecomradeng.v72.i19.60 · Cited by 2
Identifying Giant Clams Species using Machine Learning Techniques
Et.al, J. T. D., Edullantes, C. M. A., Buladaco, M. V. M., et al. (2021) · doi:10.1145/3507971.3508013 · Cited by 2
Soccer Field Registration Based on Geometric Constraint and Deep Learning Method
Li, P., Li, J., Zong, S., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88007-1_24 · Cited by 2
Mobile Augmented Reality: Fast, Precise, and Smooth Planar Object Tracking
Matveichev, D., & Lin, D.-T. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412445 · Cited by 2
Registrating large mismatching SAR images based on corners and surface extremum strategy
Yao, G., Zhang, L., Shi, T., et al. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1547930 · Cited by 2
Unveiling contrast in darkness
Loh, Y. P., & Chan, C. S. (2015) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2015.7486507 · Cited by 4
Image Mosaicing Technique for Wide Angle Panorama
Divya, G., & Chandrasekhar, C. (2015) · TELKOMNIKA Indonesian Journal of Electrical Engineering · doi:10.11591/tijee.v15i3.1558 · Cited by 2
Clothing retrieval based on image bundled features
Chen, Q., Li, J., Lu, G., et al. (2012) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2012.6664323 · Cited by 2
Xu, T., Guo, J., Hong, M., et al. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.06.105 · Cited by 2
Hierarchical Neural Semantic Representation for 3D Semantic Correspondence
Du, K., Hu, J., Li, H., Xu, H., Huang, H., Fu, C., & Liu, S. (2025) · SIGGRAPH Asia Conference Papers · doi:10.1145/3757377.3763921 · Cited by 1
Parameterized scheduling for signal processing systems using topological patterns
Wu, S., Shen, C.-C., Sane, N., et al. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288190 · Cited by 2
A Crowd Anomaly Behavior Detection Algorithm
Zhang, F., Xue, W., Cui, L., et al. (2018) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2018.8455412 · Cited by 2
Xu, Y., Shi, Y., & Huang, C. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.4.043048 · Cited by 2
Image-based texture replacement using multiview images
Tal, D., Shimshoni, I., & Tal, A. (2008) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/1450579.1450619 · Cited by 2
Feature matching of remote‐sensing images based on bilateral local–global structure consistency
Chen, Q., & Feng, D. (2023) · IET Image Processing · doi:10.1049/ipr2.12907 · Cited by 2
Nonlinear Metric Learning with Deep Convolutional Neural Network for Face Verification
Huang, R., Lang, F., & Shu, C.-M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25417-3_10 · Cited by 2
Automatic montaging of adaptive optics SLO retinal images based on graph theory
Luo, T., Gilbert, R. N., Sapoznik, K. A., et al. (2024) · Biomedical Optics Express · doi:10.1364/boe.505013 · Cited by 2
“Li et al. [29] presented an automated method incorporating local scale-invariant feature transforms (SIFT) [30] to detect and describe salient key points in overlapping images and then used random sample consensus (RANSAC) to estimate the translation matrix.”
MethodClassification of Neuroblastoma Histopathological Images Using Machine Learning
Panta, A., Khushi, M., Naseem, U., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63836-8_1 · Cited by 2
Corner Feature Detection Based on Discrete Spherical Model
Qin, X., & Li, S. (2012) · Journal of Signal Processing · doi:10.2299/jsp.16.593 · Cited by 2
An automated approach for difference detection in cultural heritage applications
Saiti, E., Saha, S., Bunsch, E., et al. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20690-9 · Cited by 2
shapeDTW: shape Dynamic Time WarpingPreprint
Zhao, J., & Itti, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01601 · Cited by 2
CAViz, an interactive graphical tool for image mining
Pham, N.-K., Morin, A., & Gros, P. (2008) · doi:10.1109/iti.2008.4588438 · Cited by 2
Multicamera fusion for shape estimation and visibility analysis of unknown deforming objects
Nuger, E., & Benhabib, B. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.4.041009 · Cited by 2
Research on branch and bound for pedestrian detection
Ye, Z.-Q., Su, S., & Li, S. (2011) · IEEE International Conference on Computer Science and Automation Engineering · doi:10.1109/csae.2011.5952489 · Cited by 2
Building a cognitive map using an SOM2
Tokunaga, K., & Furukawa, T. (2010) · Journal of Automation Mobile Robotics & Intelligent Systems · Cited by 2
An Expert System for Quality Assurance of Document Image Collections
Graf, R., Huber-Mörk, R., Schindler, A., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34234-9_25 · Cited by 2
Landscape or Portrait? The Impact of Page Orientation on the Understandability of Scientific Posters
Beck, M. B., Chanijani, S. S. M., Bukhari, S. S., et al. (2017) · doi:10.1109/icdar.2017.376 · Cited by 2
An Intelligent Hybrid Model for Identity Document ClassificationPreprint
Khandan, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04345 · Cited by 2
Monocular Vision Based Distance Measurement of Frontal Obstacles for Unmanned Aerial Vehicle
Wang, Z., Wang, L., Zhang, X., et al. (2022) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai55851.2022.9904011 · Cited by 2
Semantic Correspondence: Unified Benchmarking and a Strong Baseline
Zhang, K., Li, X., Lu, J., & Han, K. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3640429 · Cited by 5
“To provide a structured understanding of semantic correspondence methods, we present a taxonomy categorizing approaches into handcrafted descriptors, architectural improvements, and training strategy improvements, as shown in Fig.”
Synthetic aperture radar image matching based on improved scale-invariant feature transform
Wang, Q., Tang, T., & Su, Y. (2016) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2016.7852727 · Cited by 2
An Adaptability for Arctic Sea Ice Drift Retrieval from Spaceborne SAR Data
Qiu, Y., & Li, X. (2021) · SAR in Big Data Era (BIGSARDATA) · doi:10.1109/bigsardata53212.2021.9574372 · Cited by 2
Holomorphic Filters for Object Detection
Reisert, M., Ronneberger, O., & Burkhardt, H. (2007) · Springer eBooks · doi:10.1007/978-3-540-74936-3_31 · Cited by 2
Evaluation of a YARP-based architectural framework for robotic vision applications
Stefánsson, S. F., Jónsson, B. Þ., Þórisson, K. R., et al. (2008) · Skemman · Cited by 2
Stereo Odometry Based on Careful Frame Selection
Fan, H., & Zhang, S. (2017) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2017.88 · Cited by 2
Dithering-based Sampling and Weighted α-shapes for Local Feature Detection
Varytimidis, C., Rapantzikos, K., Avrithis, Y., et al. (2015) · IPSJ Transactions on Computer Vision and Applications · doi:10.2197/ipsjtcva.7.189 · Cited by 2
Application of an Improved Fast Corner Detection Algorithm in ORB-SLAM2
Huo, X., Zhang, L., Liu, G., et al. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-9492-9_180 · Cited by 2
SemMatcher: Semantic-aware feature matching with neighborhood consensus
Jiang, Q., Lu, X., Liang, D., et al. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104611 · Cited by 2
Attention-Aware Age-Agnostic Visual Place RecognitionPreprint
Wang, Z., Li, J., Khademi, S., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.05163 · Cited by 2
Cho, E.-H., & Kim, Y. (2021) · IET Image Processing · doi:10.1049/ipr2.12336 · Cited by 2
DPDM: Feature-Based Pose Refinement with Deep Pose and Deep Match for Monocular Visual Odometry
Huang, L.-Y., Huang, S.-S., & Chien, S. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10221966 · Cited by 2
Towards Maritime Videosurveillance Using 4K Videos
Marié, V., Bechar, I., & Bouchara, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04375-9_11 · Cited by 2
Maslikova, U. V., & Supilnikov, A. A. (2021) · Bulletin of the Medical Institute REAVIZ (REHABILITATION DOCTOR AND HEALTH) · doi:10.20340/vmi-rvz.2020.5.16 · Cited by 2
An Effective Method for UAV Aerial Image Stitching
Yu, W., Xue, X., & Yue, R. (2023) · International Conference on Digital Image Processing · doi:10.1145/3604078.3604142 · Cited by 2
CBIR based on linear SPM using SIFT sparse codes
Bhargavi, K., & Rani, C. S. (2016) · IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) · doi:10.1109/iccic.2016.7919709 · Cited by 2
Milani, S., & Tronca, E. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.1.011009 · Cited by 2
Puerto-Souza, G. A., Castaño-Bardawil, A., & Mariottini, G.-L. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38085-3_15 · Cited by 2
Image set querying based localization
Deng, L., Huang, S., Duan, Y., et al. (2015) · Visual Communications and Image Processing · doi:10.1109/vcip.2015.7457924 · Cited by 2
Evaluation of Gradient Vector Flow for Interest Point Detection
Stöttinger, J., Donner, R., Szumilas, L., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89639-5_33 · Cited by 2
Object localization using positive features
Lei, H., Jiang, G., Wang, R., & Quan, L. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.06.061 · Cited by 2
Scene text localization using keypoints
Erdoğmuş, N., & Özuysal, M. (2015) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2015.7130235 · Cited by 2
Incorporating feature point-based motion hypotheses in distributed video coding
Hänsel, R., Richter, H., & Müller, E. (2011) · Cited by 2
The New Approach to Camera Calibration – GCPs or TLS Data?
Markiewicz, J. S., Podlasiak, P., Kowalczyk, M., et al. (2016) · 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-xli-b3-75-2016 · Cited by 2
Improved Object Detection Algorithm for Small Mechanical Parts Based on YOLOv7
Xu, Q., Guo, J. C., & He, Z. (2024) · Advances in transdisciplinary engineering · doi:10.3233/atde240138 · Cited by 2
A mahout based image classification framework for very large dataset
He, J., Xue, Z.-Y., Gao, M.-W., & Wu, H. (2014) · International Conference on Cloud Computing and Internet of Things (CCIOT) · doi:10.1109/cciot.2014.7062518 · Cited by 2
Modeling Visual Representations: Defining Properties and Deep Approximations
Soatto, S., & Chiuso, A. (2016) · Padua Research Archive (University of Padova) · Cited by 2
Designing A Sustainable Marine Debris Clean-up Framework without Human Labels
Wang, R., Record, N. R., King, D. W., et al. (2024) · ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3674829.3675076 · Cited by 2
“To extract features from the detected objects, we utilized SIFT (Scale-Invariant Feature Transform), a computer vision algorithm that detects and describes local features in an image [13].”
MethodDeep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace ClusteringPreprint
Sun, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03783 · Cited by 2
Preproessing and modeling for visual-based 3D indoor scene reconstruction
Lin, S., & Wen, C. (2013) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2013.6554127 · Cited by 2
Feature Points Detection and Image Matching
Lucas, L., Loscos, C., & Rémion, Y. (2013) · doi:10.1002/9781118761915.ch6 · Cited by 2
Pixel‐parallel feature detection on vision chip
Yang, J., Shi, C., Liu, L., et al. (2014) · Electronics Letters · doi:10.1049/el.2014.1787 · Cited by 2
An Image Fingerprinting Method Robust to Complicated Image Modifications
Yu, X., & Huang, T. (2008) · International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) · doi:10.1109/iih-msp.2008.93 · Cited by 2
Portrait Shadow Removal via Self-Exemplar Illumination Equalization
Huang, Q., Xu, C., Li, G., et al. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681000 · Cited by 2
“Early works like HOG [6] and SIFT [31] estimate this correspondence at the image or feature level.”
Image Set-Based Face Recognition: A Local Multi-keypoint Descriptor-Based Approach
Liu, N., Lim, M.-H., Yuen, P. C., et al. (2013) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2013.31 · Cited by 2
3D Reconstruction of Scale-Invariant Feature for Mobile Robot localization
Shen, D., Kil, S.-K., Lee, E.-H., et al. (2006) · Cited by 2
Enlarging the discriminability of bag-of-words representations with deep convolutional features
Manger, D., & Willersinn, D. (2017) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2017.8310096 · Cited by 2
HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration
Tang, C., Li, Y., Du, S., et al. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186709 · Cited by 2
Particle filter based multi-pedestrian tracking by HOG and HOF
Yang, C., Li, B., & Xu, G. (2014) · IEEE International Conference on Information Science and Technology (ICIST) · doi:10.1109/icist.2014.6920577 · Cited by 2
Gender recogniotion methods useful in mobile authentication applications
Stawska, Z., & Milczarski, P. (2016) · Information Systems Management · 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
