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 41,501–41,600 of 56,200
Local feature-based image matching: a comprehensive review and robustness evaluation
Bian, H., Chen, Q., Zhang, H., Qin, L., Cui, H., & Wang, X. (2025) · The Visual Computer · doi:10.1007/s00371-025-04278-w · Cited by 3
“(4 ) SuperPoint [140] + CAPS [123] + MNN and SIFT [76] + CAPS [123] + MNN are chosen to examine the robustness disparities between handcrafted detectors and deep learning-based detectors using the same descriptor.”
InfluentialGraph-based Correlated Topic Model for Motion Patterns Analysis in Crowded Scenes from Tracklets.
Alghamdi, M., & Gotoh, Y. (2018) · Cited by 1
Vote based correspondence for 3D point-set registration
Kulkarni, N. A., & Kumar, S. (2012) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/2425333.2425355 · Cited by 1
Identification of geometrically consistent interest points for 3D scene reconstruction
Nicosevici, T., García, R. R., & Gracias, N. (2008) · Instrumentation viewpoint · doi:10.5821/iwp.2007.6.15846 · Cited by 1
Image composition using F-transform
Vajgl, M., Hurtík, P., Perfilieva, I., et al. (2014) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzz-ieee.2014.6891761 · Cited by 1
Deep Neural Networks Optimization Based On Deconvolutional Networks
Liu, Z., Zhang, C., Li, C., Ding, S., Liu, S., & Dong, Y. X. (2018) · International Conference on Graphics and Signal Processing (ICGSP) · doi:10.1145/3282286.3282299 · Cited by 1
Image-Based Motion Detection: Using the Concept of Weighted Directional Descriptors
Zadicario, E., Rudich, S., Hamarneh, G., et al. (2010) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2009.935727 · Cited by 1
Literature Survey on Recent Methods for 2D to 3D Video Conversion
Phan, R., Rzeszutek, R., & Androutsos, D. (2012) · CRC Press eBooks · doi:10.1201/b11716-34 · Cited by 1
Affine-Gradient Based Local Binary Pattern Descriptor for Texture ClassifficationPreprint
Hao, Y., Li, S., Mo, H., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.06871 · Cited by 1
Magallon, B. J., Amado-Dasallas, J., Jiao, B. J., et al. (2017) · doi:10.1117/12.2278177 · Cited by 1
Evaluation of Interest Point Detectors for Non-planar, Transparent Scenes
Papalazarou, C., Rongen, P. M. J., & de With, P. H. N. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04697-1_1 · Cited by 1
FROM VISUAL SALIENCY TO VIDEO BEHAVIOUR UNDERSTANDING
Hung, H. (2007) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1
AICO, Artificial Intelligent COach
Marco, V. S., Ukita, N., Kobayashi, N., et al. (2020) · Journal of WSCG · doi:10.24132/jwscg.2020.28.4 · Cited by 1
Vision-based recognition for robot localization
Xingjian, Z. (2010) · doi:10.1109/iccda.2010.5540820 · Cited by 1
How to Exploit Marker Orientation for SIFT Image Matching
Park, G.-M., Hwang, H.-L., & Cho, H.-G. (2012) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2012.147 · Cited by 1
Is visual similarity sufficient for semantic object recognition
Sluzek, A. S., & Paradowski, M. (2012) · Cited by 1
Robust skin microrelief depth estimation using a mobile stereo system
Moon, C., Lee, O., & Choi, M. (2022) · Skin Research and Technology · doi:10.1111/srt.13204
Learning a family of detectors
Sclaroff, S., & Yuan, Q. (2010) · Cited by 1
Rotational invariant biologically inspired object recognition
karimi, H. S., & Mohammadi, K. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1621 · Cited by 1
UAIC's Participation at Wikipedia Retrieval @ ImageCLEF 2011.
Boroş, E., Ginsca, A., & Iftene, A. (2011) · Cited by 1
Gao, Y., Dong, L., Yang, P., et al. (2014) · Optics Express · doi:10.1364/oe.22.008298 · Cited by 1
Leveraging cross-view geo-localization with ensemble learning and temporal awareness
Ghanem, A., Abdelhay, A., Salah, N. E., Eldeen, A. N., Elhenawy, M. M. Z., Masoud, M., Hassan, A. M., & Hassan, A. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283672 · Cited by 1
Lang, S. R., Luerssen, M. H., & Powers, D. M. W. (2019) · MATEC Web of Conferences · doi:10.1051/matecconf/201927702032 · Cited by 1
Deep Cross-Modal Alignment in Audio-Visual Speech Recognition
Sterpu, G. (2021) · Trinity's Access to Research Output (TARA) (Trinity College Dublin) · Cited by 1
An Approach to the Compact and Efficient Visual Codebook Based on SIFT Descriptor
Wang, Z., Liu, G., Qian, X., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15702-8_42 · Cited by 1
MMI-FM: Multimodal Interaction and Fusion Mechanism for Video Anomaly Detection
Wang, B., Wang, C., Li, J., et al. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06975-4 · Cited by 1
“Early manual feature methods primarily use low-level features such as histogram of optical flow [ 19], spatio-temporal gradient [20, 21], SIFT [22] and dynamic texture [23].”
Henrich, V., Vogel, A., Baader, M., Franke, J. E., & Kühl, A. (2024) · International Electric Drives Production Conference · doi:10.1109/edpc63771.2024.10932861 · Cited by 1
“Techniques include keypoint detection methods [14] and tracking algorithms such as the Kalman filter [15], which predicts the position of an object over time.”
AN ADVANCED FACE DETECTION AND RECOGNITION
Chakraborty, A. (2020) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v11i4.6632 · Cited by 1
Li, M., Xiong, J., Deng, R., Yao, T., Tyree, R. N., Hiremath, G. S., & Huo, Y. (2025) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3047341 · Cited by 1
“Classic methods like Scale-Invariant Feature Trans-form (SIFT), 11 which detects and describes local features by identifying key points and computing gradients, Corresponding author: Yuankai Huo: E-mail: yuankai.huo@vanderbilt.edu a r X i v : 2410 .”
MethodDeep Learning-Based Descriptors for Object Instance Search
Lin, J., Morère, O., Veillard, A., et al. (2019) · Springer eBooks · doi:10.1007/978-981-10-5152-4_8 · Cited by 1
KRF-SLAM: A Robust AI Slam Based On Keypoint Resampling And Fusion
Wong, W. M., Lim, C., Lee, C.-D., et al. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191192 · Cited by 1
Watermark Image Recognition from Local Scale-Invariant Feature
Madane, A. R. (2009) · doi:10.1109/uksim.2009.63 · Cited by 1
A multi-level sketch-based interface for decorative pattern exploration
Chen, Y., Fu, H., & Au, K. C. (2016) · SIGGRAPH Asia Technical Briefs · doi:10.1145/3005358.3005362 · Cited by 1
Graph Embedding Based on Nodes Attributes Representatives and a Graph of Words Representation
Gibert, J., & Valveny, E. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14980-1_21 · Cited by 1
Machine Learning for Measuring and Analyzing Online Social Communications
Bronk, C., Lendasse, A., Lindner, P., et al. (2021) · ESANN 2021 proceedings · doi:10.14428/esann/2021.es2021-3 · Cited by 1
An efficient hybrid approach for vehicle detection and tracking
El-Alami, A., Nadir, Y., Amhaimar, L., et al. (2023) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom59760.2023.10322924 · Cited by 1
Spectral representation for matching and recognition
Haseeb, M. (2013) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · Cited by 1
Hu, R., Liu, S., Wang, J., Mi, X., Liu, Y., Hao, P., & Shi, X. (2023) · International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) · doi:10.1109/ichci58871.2023.10277762 · Cited by 1
Distributable Consistent Multi-Object MatchingPreprint
Hu, N., Huang, Q., Thibert, B., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07191 · Cited by 1
Difference of wavelet SIFT based mobile robot navigation
Jun, S., Kim, Y.-O., & Lee, J. (2009) · International Conference on Control and Automation · doi:10.1109/icca.2009.5410318 · Cited by 1
Fast Nearest Neighbor Search with Transformed Residual Quantization
Yuan, J., & Liu, X. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0175 · Cited by 1
Detection of the Vanishing Line of the Ocean Surface from Pairs of Scale-Invariant Keypoints
Fefilatyev, S., Shreve, M., & Goldgof, D. B. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41181-6_17 · Cited by 1
Telefonica Research at TRECVID 2011 Content-Based Copy Detection.
Anguera, X., Adamek, T., Xu, D., et al. (2011) · Cited by 1
Real‐Time Stabilization of an Eight‐Rotor UAV Using Stereo Vision and Optical Flow
Romero, H., Salazar, S., & Gómez, J. (2013) · doi:10.1002/9781118599938.ch12 · Cited by 1
Wearable multispectral imaging and telemetry at edge
Cai, Y., & Siegel, M. (2023) · Electronic Imaging · doi:10.2352/ei.2023.35.7.image-278 · Cited by 1
A deep-learning approach to grain boundary detection in backscattered electron images
Zhao, P., Sur, F., Gey, N., et al. (2026) · Computational Materials Science · doi:10.1016/j.commatsci.2026.114622 · Cited by 1
Unsupervised feature selection and category formation for mobile robot vision
Madokoro, H., Tsukada, M., & Sato, K. (2011) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033238 · Cited by 1
Construction of a Local Attraction Map According to Social Visual Attention
Ide, I., Wang, J., Noda, M., et al. (2012) · Smart innovation, systems and technologies · doi:10.1007/978-3-642-29934-6_15 · Cited by 1
Pedestrian Recognition in Aerial Video Using Saliency and Multi-Features
Liu, C., Xu, F., Wang, X., et al. (2014) · International Journal of Computer and Electrical Engineering · doi:10.7763/ijcee.2014.v6.844 · Cited by 1
MTF-Net: A mediator transformer-based fusion network with MOE for 6D object pose estimation
Jiang, Z., An, T., Tong, Z., Li, Z., Du, Y., Xie, T., Wang, K., Zhao, L., & Li, R. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114674 · Cited by 3
MPNet: A Multiprocess Convolutional Neural Network for Animal Classification
Jiang, B., Huang, W., Huang, Y., et al. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-2540-4_42 · Cited by 1
Face recognition based on multi-scale local features
Geng, C. (2012) · doi:10.32657/10356/50725 · Cited by 1
Research on the feature set construction method for spherical stereo vision
Zhu, J., Wan, L., Röning, J., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083386 · Cited by 1
CESPED: A benchmark for supervised particle pose estimation in cryo-EM
Sánchez-García, R., Saur, M., Vargas, J., et al. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.023245 · Cited by 1
Efficient image retrieval using multi neural hash codes and bloom filters
Chakrabarti, S. (2020) · ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY · doi:10.33130/ajct.2020v06i03.004 · Cited by 1
Visual Odometry and 3D Mapping in Indoor Environments
Wu, Q., Li, Q., & Cheng, N. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.336-338.348 · Cited by 1
Two-Fold and Symmetric Repeatability Rates for Comparing Keypoint燚etectors
Rube, I. E. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.031602 · Cited by 1
Automatic Product Images Classification With Two Layers of SVM Classifier
Jia, S., Kong, X., & Hong, M. (2012) · Lecture notes in electrical engineering · doi:10.1007/978-94-007-2169-2_25 · Cited by 1
Super resolution based on simultaneous registration and reconstruction
Yuan, Z. J., Yan, P., Li, S., et al. (2008) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2008.4795671 · Cited by 1
Qi, 贺. H., Jinyuan, 张. Z., Dongmei, 黄. H., et al. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop220432 · Cited by 1
A topology synthesizing approach for classification of visual information
Dong, L., & Izquierdo, E. (2008) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2008.4564971 · Cited by 1
Sampling Based Average Classifier Fusion
Hou, J., Liu, W., & Karimi, H. R. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/369613 · Cited by 1
Dai, D., Quan, Q., & Cai, K.-Y. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801926 · Cited by 1
“SURF [3], SIFT [4]) to extract key points and establish the correspondence to recover the pose.”
Csca-unet: a lightweight network for rapid underwater image enhancement
Wang, W., Liao, W., & Xu, Z. C. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07848-w · Cited by 1
“To better evaluate the effectiveness of our proposed method, we used SIFT[34] feature point detection and Canny[35] edge detection to assess the enhanced results.”
Cao, S., Mu, D., Zhang, Y., et al. (2026) · npj Heritage Science · doi:10.1038/s40494-026-02371-4 · Cited by 1
Research on 3D Reconstruction Algorithm of Citrus Seedlings Based on Multi-View Geometry
Wang, H., Liu, Q., Xiao, M., et al. (2023) · doi:10.1109/scset58950.2023.00069 · Cited by 1
Bayesian object extraction from uncalibrated image pairs
Koo, H. I., Lee, S. H., & Cho, N. I. (2007) · Signal Processing Image Communication · doi:10.1016/j.image.2007.07.003 · Cited by 1
Face Recognition using Invariant Feature Vectors and Ensemble of Classifiers
Vinay, A., Gupta, A., Garg, H., et al. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-3600-3_74 · Cited by 1
Image Description and Matching Scheme Using Synthetic Features for Recommendation Service
Yang, W.-K. (2011) · ETRI Journal · doi:10.4218/etrij.11.1510.0023 · Cited by 1
2D-3D semantic segmentation using cardinality as higher-order loss
Namin, S. R., Álvarez, J. M., & Petersson, L. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900222 · Cited by 1
q-Gaussian Mixture Models Based on Non-extensive Statistics for Image and Video Semantic Indexing
Inoue, N., & Shinoda, K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37444-9_39 · Cited by 1
Improved deep depth estimation for environments with sparse visual cues
Joswig, N., Autiosalo, J., & Ruotsalainen, L. (2023) · Machine Vision and Applications · doi:10.1007/s00138-022-01364-0 · Cited by 1
Research on Adversarial Attack Technology for Object Detection in Physical World Based on Vision
Liu, H., Wang, F., & Du, J. (2022) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1109/cacml55074.2022.00112 · Cited by 1
Self-paced Learning for Weakly Supervised Evidence Discovery in Multimedia Event SearchPreprint
Liu, M., Jiang, L., Shan, S., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.03748 · Cited by 1
Visual Categorization Based on Learning Contextual Probabilistic Latent Component Tree
Atsumi, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33269-2_53 · Cited by 1
Video Frame Interpolation by Image Morphing Including Fully Automatic Correspondence Setting
HASEYAMA, M., Takizawa, M., & Yamamoto, T. (2009) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e92.d.2163 · Cited by 1
An Efficient Copy-Move Forgery Detection Technique Using Nature-Inspired Optimization Algorithm
Gupta, A., & Chawla, I. (2019) · Studies in computational intelligence · doi:10.1007/978-981-15-1362-6_7 · Cited by 1
Tumor Net (T-Net) for Classification of multi Tumors in Brain MR Images
Anil Kumar, B. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/74922020 · Cited by 1
Real-Time Template Matching Based on Local Stable Pixels
Zhu, X., Hu, X. G., Liu, C., et al. (2020) · International Conference on Advances in Computer Technology, Information Science and Communications (CTISC) · doi:10.1109/ctisc49998.2020.00028 · Cited by 1
Research on 3D Reconstruction Algorithm of small Crops based on multi-view Geometry
Wang, H., Xiao, M., Luo, Q., et al. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2832/1/012015 · Cited by 1
Research on adaptive SIFT algorithm in image matching of pipeline inner surface visual measurement
Li, Z., Zhang, L., Wang, L., et al. (2017) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi.2017.8265799 · Cited by 1
Hashing-based large-scale medical image retrieval for computer-aided diagnosis
Zhang, X., & Zhang, S. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804076-8.00008-6 · Cited by 1
Fusion 3D object tracking method based on region and point cloud registration
Jin, Y., Zhang, J., Liu, Y., et al. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.4.043048 · Cited by 1
“With the development of feature point descriptor algorithms 7 – 9 possessing local invariance, it is widely used in the field of image feature tracking.”
Fast Feature Extraction Using Approximations to Derivatives with Summed-Area Images
Wyatt, P. P., & Nakai, H. (2006) · Lecture notes in computer science · doi:10.1007/11612032_78 · Cited by 1
Enhanced stereo vision SLAM for outdoor heavy machine rotation sensing
Lin, L.-H. (2010) · cIRcle (University of British Columbia) · doi:10.14288/1.0071017 · Cited by 1
Learning Category-Specific Sharable and Exemplary Visual Elements for Image Classification
Xie, Y., & Song, T. (2020) · IEEE Access · doi:10.1109/access.2020.2982591 · Cited by 1
A Minimal Solution Estimating the Position of Cameras with Unknown Focal Length with IMU Assistance
Yan, K., Yu, Z., Song, C., Zhang, H., & Chen, D. (2024) · Drones · doi:10.3390/drones8090423 · Cited by 1
Mohammed, A. D. (2019) · i-manager’s Journal on Image Processing · doi:10.26634/jip.5.3.15689 · Cited by 1
Multiple view anomaly detection in images from UAS structure inspection using CNNs
Debus, P., Benz, C., & Rodehorst, V. (2019) · Report · doi:10.2749/newyork.2019.1984 · Cited by 1
Zhang, D. D., Xu, Y. D., & Zuo, W. (2016) · Springer eBooks · doi:10.1007/978-981-10-2056-8_10 · Cited by 1
Research on moving human and vehicle's recognition algorithm
Zhu, J., Zhou, J., & Zhang, B. (2010) · doi:10.1109/iccda.2010.5540698 · Cited by 1
Real-Time Video Face Recognition for Embedded Devices
Costache, G., Mangapuram, S., Drîmbărean, A., et al. (2011) · InTech eBooks · doi:10.5772/19159 · Cited by 1
Text extraction from natural scene: Methodology and application
Yi, C. (2014) · CUNY Academic Works (City University of New York) · Cited by 1
Detection of Defects in Plastic Cup Production Using YOLO Method
Adhisaputra, S., & Harefa, J. (2024) · International Conference on Data Science and Its Applications (ICoDSA) · doi:10.1109/icodsa62899.2024.10651829 · Cited by 2
“Low-level visual descriptors such as SIFT [17], HOG [18] and Haar-like [19] are usually used.”
MethodMulti-Resolution Estimation of Optical Flow on Vehicle Tracking under Unpredictable Environments
Do, V. D., & Woo, D.-M. (2016) · Advanced science and technology letters · doi:10.14257/astl.2016.129.07 · Cited by 1
Khan, A., Khan, M. U. K., Bilal, M., et al. (2016) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-46097-0_3 · Cited by 1
Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm Using GPUs
Kommera, P. R., Muknahallipatna, S., & McInroy, J. E. (2023) · Engineering · doi:10.4236/eng.2023.1510046 · Cited by 1
Low-resolution vision for autonomous mobile robots
Birchfield, S. T., & Murali, V. N. (2011) · 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
