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 44,201–44,300 of 56,200
Evaluation of Close-Range Stereo Matching Algorithms Using Stereoscopic Measurements
Shin, D., Tao, Y., & Müller, J. (2018) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.84.3.159 · Cited by 1
Towards extensible automatic image annotation with the bag-of-words approach
Nagy, R., & Meyer-Wegener, K. (2010) · doi:10.1145/1878137.1878148 · Cited by 1
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex ScenesPreprint
Abbasnejad, I., Sridharan, S., Denman, S., Fookes, C., & Lucey, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04122 · Cited by 3
An Efficient 3-D Mapping Algorithm for RGB-D SLAM
Yu, J., You, Z., An, P., et al. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-8108-8_42 · Cited by 1
Kamerabasierte Fußgängerdetektion
Schiele, B., & Wojek, C. (2009) · Springer eBooks · doi:10.1007/978-3-658-05734-3_23 · Cited by 1
AttFeat: Attention-Based Features for Infrared and Visible Remote Sensing Image Matching
Li, J., Chen, X., Wang, X., et al. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3568888 · Cited by 1
Co-Registration of 2D Imagery and 3D Point Cloud Data
Weinmann, M. (2016) · Springer eBooks · doi:10.1007/978-3-319-29246-5_5 · Cited by 1
Codebook-free exemplar models for object detection
Becker, J. H., Tuytelaars, T., & Gool, L. V. (2012) · International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS) · doi:10.1109/wiamis.2012.6226768 · Cited by 1
Derpanis, K. G. (2006) · Cited by 1
Vision-Enhanced Localization for Cooperative Robotics
Boga, S. (2009) · Cited by 1
Object Detection Algorithms Compression CNN, YOLO and SSD
Pagare, S., & Kumar, R. (2023) · International Journal of Computer Applications · doi:10.5120/ijca2023922726 · Cited by 1
Fast Hand Detection and Tracking for Human-Robot Interaction
He, Q., Wang, W., & Xiao, R. (2022) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea54703.2022.10005915 · Cited by 1
Optical Music Recognition in Manuscripts from the Ricordi Archive
Simonetta, F., Mondal, R., Ludovico, L. A., & Ntalampiras, S. (2024) · Audio Mostly Conference (AM) · doi:10.1145/3678299.3678324 · Cited by 2
“The Difference of Gaussians (DoG) method, as implemented by the scikit-image Python module [12], was utilized to identify the musical symbols in the images.”
Aiger, D., Cao, B., Chen, K., et al. (2025) · IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw69036.2025.00433 · Cited by 1
“Initial promising results in this area were dominated by local-to-local methods, where local feature search was used to find candidates and re-ranking employed geometric verification [15, 18, 21].”
Discrete semantic embedding hashing for scalable cross-modal retrieval
Liu, J., Fei, L., Jia, W., et al. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9658664 · Cited by 1
An Automatic Registration Method Based on Fiducial Marker for Image Guided Neurosurgery System
Yin, M., Shen, X., Hu, Y., et al. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-45037-2_11 · Cited by 1
Kim, T. K. (2019) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126620200030 · Cited by 1
In Situ Structural Characterization of Cardiomyocyte Microenvironment by Multimodal STED Microscopy
Zhang, Z., Gao, B. Z., & Ye, T. (2024) · Photonics · doi:10.3390/photonics11060533 · Cited by 1
“Z-stacks were aligned to correct sample drift during Z-stack imaging using a Fiji plugin, Linear Stack Alignment with SIFT [19].”
MethodKassab, R., Treuillet, S., Marzani, F. S., et al. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2002016 · Cited by 1
Calzadillas, E. A. (2019) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2019-13841 · Cited by 1
Image retrieval using underlying importance feature histogram
He, Q., & Liu, G. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09735-6 · Cited by 2
“Local features (e.g. key points and salient patches) were used in diverse applications [23–29].”
A structure-based region detector for retinal image registration
Ghassabi, Z., Shanbehzadeh, J., & Mohammadzadeh, A. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350875 · Cited by 1
KLN: A Deep Neural Network Architecture for Keypoint Localization
Hurtík, P., & Tyshchenko, O. K. (2020) · IEEE International Conference on Data Stream Mining & Processing (DSMP) · doi:10.1109/dsmp47368.2020.9204211 · Cited by 1
VIEW GRAPH CONSTRUCTION FOR LARGE-SCALE UAV IMAGES: AN EVALUATION OF STATE-OF-THE-ART METHODS
Liu, J., Ma, Y., Jiang, S., 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-w2-2023-1059-2023 · Cited by 1
Parallax handling of image stitching using dominant-plane homography
Pang, Z., Li, C., Zhao, B., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2199793 · Cited by 1
Optimal Region Gradated Fusion Based Automatic Image Stitching for Industrial SystemsPreprint
Huang, Y., Zheng, M., Song, Z., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-1098265/v1 · Cited by 1
A Score Level Fusion Scheme for Iris Recognition
Sharma, V., Gautam, G., & Mukhopadhyay, S. (2019) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct45611.2019.9033880 · Cited by 1
Target Recognition and Features ExtractionBased on ROI and SIFT Descriptors
Ma, J., Li, Y., & Wang, Q. L. (2012) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.220-223.1153 · Cited by 1
Research on a kind of remote sensing registration algorithm based on improved SIFT
Wang, Y., Xiaoxia, Y., & Zhang, C. (2016) · International Conference on Agro-Geoinformatics · doi:10.1109/agro-geoinformatics.2016.7577691 · Cited by 1
Wireless Capsule Endoscope Low-light Image Enhancement with Balanced Brightness and Saturation
Li, W., Wang, Y., Li, W., et al. (2024) · International Conference on Multimedia Retrieval · doi:10.1145/3652583.3658034 · Cited by 1
“For the feature point detection task, we use scale-invariant feature transform (SIFT) [21] to detect feature points on the enhanced image.”
MethodLi, C., You, Y., Cao, J., et al. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9553197 · Cited by 1
Comprehensive Study of Indexing Techniques Used for Extracting CNN Features
Saini, M., Gupta, P., & Kaur, H. (2023) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca57980.2023.10220679 · Cited by 1
Live photo mosaic with a group of wireless image sensors
Li, F., Barabas, J., & Santos, A. L. (2009) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1644038.1644101 · Cited by 1
LEGO Minifigure Recognition Based on Deep Learing
Wang, P. (2024) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai62207.2024.10826947 · Cited by 1
“Deep learning as a part of the artificial intelligence network, before it was widely used, visual recognition tasks largely relied on the Scale-Invariant Feature Transform (SIFT) algorithm [3] and the Histogram of Oriented Gradient (HOG) method [4].”
Local and global semantic relationship network for remote sensing scene classification
Song, F., Ma, R., Tao, L., et al. (2023) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1117/12.2683561 · Cited by 1
PlanarTrack: A high-quality and challenging benchmark for large-scale planar object tracking
Jiao, Y., Liu, X., Liu, X., Yuan, X., Fan, H., & Zhang, L. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104553 · Cited by 1
“DeepKeypointDirect WOFT (Šer`ych and Matas, 2023) RAFT ✓ HDN (Zhan et al., 2022) ResNet-50 ✓ GIFT (Liu et al., 2019) CNN ✓ LISRD (Pautrat et al., 2020) VGG16 ✓ SIFT (Lowe, 2004) - ✓ Gracker (Wang and Ling, 2017) - ✓ SOL (Hare et al., 2012) - ✓ SCV (Richa et al., 2011) - ✓ ESM (Benhimane and Malis, 2004) - ✓ IC (Baker…”
InfluentialRicher Aggregated Features for Optical Flow Estimation with Edge-aware Refinement
Wang, X., Zhu, D., Song, J., et al. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341544 · Cited by 1
Matching of Feature Points Based on TSSC Method from MR Images of Nonrigid Deformed Tissues
Zhang, X., Zhang, P., & Hirai, S. (2014) · Bio-Medical Materials and Engineering · doi:10.3233/bme-130924 · Cited by 1
Real-time oriented edge detection via difference of shifted image
Jeong, K., Jin, M., Hwang, D., et al. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2051381 · Cited by 1
AI-Driven Waste Detection in Russia: Towards a National Environmental Monitoring System
Макаров, А. А., & Shabunin, A. (2025) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct67853.2025.11239218 · Cited by 1
“Nevertheless, these methods were outperformed by neural networks in both speed and accuracy [9].”
Security analysis of image copy detection systems based on SIFT descriptors
Do, T.-T. (2012) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 1
Efficient data association for robot 3D vision-SLAM
Wang, X., & Zhu, D. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.867477 · Cited by 1
Clady, X., Negri, P., Milgram, M., et al. (2009) · Cited by 1
Large-scale model-assisted bundle adjustment using Gaussian max-mixtures
Ozog, P., & Eustice, R. M. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487775 · Cited by 1
Transparent Deep Rule-Based Classifiers
Angelov, P. P., & Gu, X. (2018) · Studies in computational intelligence · doi:10.1007/978-3-030-02384-3_9 · Cited by 1
Tang, J., Yu, J., Xiang, Y., Min, X., Xu, Y., Dong, Y., & Wang, Y. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11164141 · Cited by 1
Age Invariant Face Recognition using multiclass SVM
Pal, R., & Gautam, A. K. (2015) · International Conference on Electrical, Electronics, Signals, Communication and Optimization · doi:10.1109/eesco.2015.7253984 · Cited by 1
Rapid target recognition and tracking under large scale variation using Semi-Naive Bayesian
Sun, K., Wang, B., & Hao, Z. (2010) · Cited by 1
Collaborative tracking in video sequences using corners and gradient information
Monti, F., Asadi, M., & Regazzoni, C. S. (2008) · Cited by 1
Desmarescaux, M., Kaddah, W., Alfalou, A., & Deconninck, J.-C. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11742-0 · Cited by 5
“Unlike traditional pattern recognition methods [3, 52, 60, 61, 75], which focus on classifying individual instances, OD literature seeks to classify multiple instances within the same class.”
Asymmetric Activation of Retinal ON and OFF Pathways by AOSLO Raster-Scanned Visual Stimuli
Patterson, S. S., Cai, Y., Yang, Q., Merigan, W., & Williams, D. R. (2024) · bioRxiv · doi:10.1101/2024.12.17.628952 · Cited by 3
“Because small changes in pupil position during the course of an experiment could lead to small off-sets in registered videos, a secondary registration us-ing SIFT (Lowe, 2004) where F 0 was the average fluorescence during the 10 seconds preceding stimulus onset during which the mean background light level was present.”
Building Stateless Serverless Vector DBs via Block-based Data Partitioning
Barcelona-Pons, D., Gracia-Tinedo, R., Cañadilla-Domingo, A., Roca-Canals, X., & Garcı́a-López, P. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3769769 · Cited by 1
“…normalized with L2 distance extracted from the last fully-connected layer of a GoogLeNet [33] model trained with the ImageNet [9] dataset; (ii) SIFT [21], which consists of 10M embeddings with 128 dimensions, the Scale Invariant Feature Transform (SIFT) transforms the image data into a large…”
Huang, H., Wang, Y., Cai, M., Wang, R., Wen, F., & Hu, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-61286-x · Cited by 2
“Researchers have also explored local features such as scale-invariant feature transform (SIFT) 17 points or spatio-temporal interest points (STIP) 18 in the spatial domain to describe action information without correlation.”
Markiewicz, J. S. (2025) · Reports on Geodesy and Geoinformatics · doi:10.2478/rgg-2025-0006 · Cited by 1
“The choice of this normisassociatedwiththefundamentalalgorithmforkeypointde-tection and matching, which is based on the SIFT algorithm (Lowe, 2004).”
InfluentialDeep Transfer Learning for Scene Recognition Using Resnet50Preprint
Boro, S., & Sathya, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4507993 · Cited by 1
User-drawn sketch-based 3D object retrieval using distributed compressive sensing
Yoon, S. M., Hahn, K.-S., & Yoon, G.-J. (2013) · doi:10.1109/ictc.2013.6675454 · Cited by 1
Deep Convolutional Neural Network Based Object Detection Inference Acceleration Using FPGA
Tesema, S. N. (2022) · theses.fr (ABES) · doi:10.70675/4a3f6c51z0434z4b5az92fdz56300190f00b · Cited by 1
Cross-Codebook Image Classification
Liao, S., Li, X., & Du, X. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03731-8_46 · Cited by 1
How Many Frames Does Facial Expression Recognition Require?
Yu, K., Wang, Z., Guan, G., et al. (2012) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2012.56 · Cited by 1
Liu, L., He, C., Yao, C., et al. (2025) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-025-00448-0 · Cited by 1
“Previous research mainly used SIFT feature extraction techniques combined with RANSAC feature matching techniques to compute the matching relationship between two images and employed homography matrices to warp the images for frame alignment.”
InfluentialAction scene detection from motion and events
Sorschag, R., & Hörhan, M. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116507 · Cited by 1
Joint Utilization of Appearance and Geometry for Scene Logo Retrieval
Soysal, M., & Alatan, A. A. (2010) · Lecture notes in electrical engineering · doi:10.1007/978-90-481-9794-1_58 · Cited by 1
An Improved Image Stitching Method Based on Vision-Inertial Fusion
Liu, T., Chen, B., Wang, B., et al. (2024) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc60896.2024.10560768 · Cited by 1
“In the early 20th century, Lowe [9] proposed the SIFT, establishing the dominant position of the feature-point-based stitching algorithm.”
BackgroundContextual modeling on auxiliary points for robust image reranking
Li, Y., Kong, X., Fu, H., et al. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-018-7403-7 · Cited by 1
Performance Evaluation of Point-Based Image Descriptors
Sundaram, S., Lee, Y., Kim, Y., et al. (2014) · doi:10.1109/icisa.2014.6847445 · Cited by 1
An Improved SIFT Algorithm for Monocular Vision Positioning
Niu, X., Pu, J., & Zhang, C. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/612/3/032124 · Cited by 1
Automatic Representation and Classifier Optimization for Image-based Object Recognition
Bürger, F., & Pauli, J. (2015) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0005359005420550 · Cited by 1
Remote sensing image registration based on fusion of local features and self-similar features
Song, Z., & Li, Z. (2022) · doi:10.1109/iciibms55689.2022.9971708 · Cited by 1
One-click scanning of large-size documents using mobile phone camera
Liu, S., Jiang, B., & Yang, Y. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2242825 · Cited by 1
An Adaptive Features Fusion Convolutional Neural Network for Multi-Class Agriculture Pest Detection
Qasim, M., Shah, S. M. A., Safi, Q. G. K., et al. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.065060 · Cited by 1
Brain MR image registration based on an Improved CNN Model
Wang, Q., Liu, S. T., Ba, X., et al. (2021) · doi:10.1145/3510513.3510524 · Cited by 1
A Novel Framework for Robust Annotation and Retrieval in Video Sequences
Anjulan, A., & Canagarajah, N. C. (2006) · Lecture notes in computer science · doi:10.1007/11788034_19 · Cited by 1
Extracting Corresponding Point Based on Texture Synthesis for Nearly Flat Textureless Object Surface
Mao, M., Hao, K., & Ding, Y. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/594956 · Cited by 1
ADVANTAGES OF USING SIFT FOR BRAIN TUMOR DETECTION
Pani, A., Shende, P., Dhumal, M., et al. (2015) · International Journal of Students Research in Technology & Management · Cited by 1
Diagnosis Expert System for Oesophagus Cancer in Early Stage
Ji, B., Song, R. F., Xu, F., et al. (2012) · International Conference on Computer Science and Service System · doi:10.1109/csss.2012.530 · Cited by 1
Image Registration SIFT Algorithm Based on Adaptive Adjustment of Grayscale Weight
Pan, Y., Wang, Y., Xu, H. F., et al. (2019) · International Conference on Optical Communications and Networks (ICOCN) · doi:10.1109/icocn.2019.8933825 · Cited by 1
Detecting objects in image collections using bipartite graph matching
Xu, P., Chen, R., & Ning, Y. (2012) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2012.6223420 · Cited by 1
Remote Sensing Image Registration Using Fast Visual Saliency and Improved SIFT
Khalili, F., Razzazi, F., & Hosseini, S. A. (2024) · doi:10.1109/icee63041.2024.10667849 · Cited by 1
Parameterized Scheduling of Topological Patterns in Signal Processing Dataflow Graphs
Wang, L.-H., Shen, C.-C., Wu, S., et al. (2012) · Journal of Signal Processing Systems · doi:10.1007/s11265-012-0719-x · Cited by 1
Alzahrani, A. (2015) · doi:10.22215/etd/2015-10636 · Cited by 1
Cao, J., Zeng, Z., Lao, H., et al. (2024) · Electronics · doi:10.3390/electronics13245003 · Cited by 1
Schreuder, T., Hendriks, E. A., & Redert, A. (2010) · doi:10.1145/1877791.1877804 · Cited by 1
Increase the Image Search Results by Using Flickr Tags
Chan, S.-B., & Yamana, H. (2011) · Cited by 1
Unsupervised Object Annotation through Context Analysis
Riad, A. M., Elminir, H. K., & Abd-Elghany, S. (2013) · International Journal of Applied Information Systems · doi:10.5120/ijais12-450787 · Cited by 1
Identity Verification Based on Facial Pose Pool and Bag of Words Model
Chu, W., & Guan, Y. (2017) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2017.p0448 · Cited by 1
Improved binary feature matching through fusion of hamming distance and fragile bit weight
Zhuang, D., Zhang, D., Li, J., et al. (2013) · doi:10.1145/2505483.2505492 · Cited by 1
Learning spatial prior with automatically labeled landmarks
Wu, J., Zhou, Z., Zhou, L. J., et al. (2008) · doi:10.1109/iske.2008.4731111 · Cited by 1
A 2D Georeferenced Map Aided Visual-Inertial System for Precise UAV LocalizationPreprint
Mao, J., Zhang, L., He, X., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05851 · Cited by 1
Soft assignment vs hard assignment coding for bag of visual words
Chougrad, H., Zouaki, H., & Alheyane, O. (2015) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1109/sita.2015.7358422 · Cited by 2
Depth Map Estimation from Multi-View Images with Nerf-Based Refinement
Ito, S., Miura, K., Ito, K., et al. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222229 · Cited by 1
Pengenalan Penggunaan Helm Proyek Berstandar Pada Citra Foto Berdasarkan SIFT Dengan SVM
Devella, S., & Wijaya, A. K. (2022) · JITTER Jurnal Ilmiah Teknologi dan Komputer · doi:10.24843/jtrti.2022.v03.i02.p09 · Cited by 1
Frontal face landmark displacement across age
Angulu, R., Tapamo, J., & Adewumi, A. O. (2017) · doi:10.1109/robomech.2017.8261119 · Cited by 1
Qu, D., Jin, J., Liu, Y., et al. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131964 · Cited by 1
A Novel SFP Extracting/Tracking Framework for Rigid Microstructure Measurement
Liu, S., Fang, T., & Chen, Z. (2011) · Mathematical Problems in Engineering · doi:10.1155/2012/356454 · Cited by 1
Dual Attention Based Image Pyramid Network for Object Detection
Dong, X., Feng, L., Bai, H., & Zhao, Y. (2021) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2021.12.010 · Cited by 1
Learning Token-Based Representation for Image Retrieval
Wu, H., Wang, M., Zhou, W., et al. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20173 · Cited by 1
Extended Affinity Propagation: Global Discovery and Local InsightsPreprint
Khan, R. A., Amjad, R. A., & Kleinsteuber, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.04459 · Cited by 1
Wei, X., Cui, L., Liu, C., et al. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3432824 · Cited by 1
General detection model in cooperative multirobot localization
Odakura, V. V. V. A., Bianchi, R. A. C., & Costa, A. H. R. (2009) · Journal of the Brazilian Computer Society · doi:10.1590/s0104-65002009000300004 · Cited by 1
Group-based sparse coding dictionary learning for object recognition
Zhao, Y., Li, J., & Zhong, Z. (2014) · IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) · doi:10.1109/wartia.2014.6976263 · 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
