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 17,201–17,300 of 56,201
Egocentric Hand Detection Via Dynamic Region Growing
Huang, S., Wang, W., He, S., & Lau, R. W. H. (2017) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3152129 · Cited by 15
An Improved Approach for Real-Time Taillight Intention Detection by Intelligent Vehicles
Tong, B., Chen, W., Li, C., Du, L., Xiao, Z., & Zhang, D. (2022) · Machines · doi:10.3390/machines10080626 · Cited by 14
Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention
Thomas, A., Ferrari, V., Leibe, B., Tuytelaars, T., & Gool, L. V. (2009) · The International Journal of Robotics Research · doi:10.1177/0278364909340444 · Cited by 15
Aerial Image Series Quality Assessment
Zhang, H., Li, B., Zhang, J., & Xu, F. (2014) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/17/1/012183 · Cited by 15
A COMPARISON BETWEEN ACTIVE AND PASSIVE TECHNIQUES FOR UNDERWATER 3D APPLICATIONS
Bianco, G., Gallo, A., Bruno, F., et al. (2012) · 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-xxxviii-5-w16-357-2011 · Cited by 15
Hsieh, C.-C., Cheng, W.-H., Chang, C.-H., Chuang, Y., & Wu, J. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459415 · Cited by 15
Chen, L., Jin, S., & Xia, Z. (2021) · Sensors · doi:10.3390/s21010310 · Cited by 17
Semantic classification by covariance descriptors within a randomized forest
Kluckner, S., & Bischof, H. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457638 · Cited by 16
Biomedical Image Enhancement Using Wavelets
Khatkar, K., & Kumar, D. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.04.128 · Cited by 15
Semantically Guided Geo-location and Modeling in Urban Environments
Singh, G., & Košecká, J. (2016) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-25781-5_6 · Cited by 15
Phan, T.-B., Trinh, D. H., Lamarque, D., et al. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8802948 · Cited by 15
Characterization of Color Images with Multiscale Monogenic Maxima
Soulard, R., & Carré, P. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2760303 · Cited by 16
Distributed collaborative inspections through smart infrastructure metaverse
Al‐Sabbag, Z. A., Yeum, C. M., & Narasimhan, S. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105503 · Cited by 15
An approach for facial expression classification
Ali, A. M., Ibrahim, A. K., & Zhuang, H. (2017) · International Journal of Biometrics · doi:10.1504/ijbm.2017.10006477 · Cited by 15
Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification
Li, H., & Hua, G. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2695183 · Cited by 15
SRP‐AKAZE: an improved accelerated KAZE algorithm based on sparse random projection
Li, D., Xu, Q., Yu, W., et al. (2020) · IET Computer Vision · doi:10.1049/iet-cvi.2019.0622 · Cited by 15
Wang, J., Zeng, Z., Chen, B., Wang, Y., Liao, D., Li, G., Wang, Y., & Xia, S. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02009-7 · Cited by 18
“Traditional UCMH approaches learned to transform hard-craft features (Lowe, 2004) into binary codes by solving linear problems, e.g. matrix factorization (Zhou et al., 2014; Ding et al., 2016; Hu et al., 2019) and spectral decomposition (Kumar and Udupa, 2011; Song et al., 2013).”
Efficient 3D Object Recognition from Cluttered Point Cloud
Wei, L., Cheng, H., & Zhang, X. (2021) · Sensors · doi:10.3390/s21175850 · Cited by 17
The Influence of Camera Calibration on Nearshore Bathymetry Estimation from UAV Videos
Simarro, G., Calvete, D., Plomaritis, T. A., et al. (2021) · Remote Sensing · doi:10.3390/rs13010150 · Cited by 15
Interactive tool for image annotation using a semi-supervised and hierarchical approach
Chiang, C.-C. (2012) · Computer Standards & Interfaces · doi:10.1016/j.csi.2012.05.002 · Cited by 15
A sparse curvature-based detector of affine invariant blobs
Ferraz, L., & Binefa, X. (2011) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2011.12.002 · Cited by 15
Carneiro, G. A., Cunha, A. M. T. D. S., Aubry, T., & Sousa, J. J. (2024) · AgriEngineering · doi:10.3390/agriengineering6040277 · Cited by 16
Puthenputhussery, A., Liu, Q., & Liu, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_37 · Cited by 15
A Harris Corner Detector Implementation in SoC-FPGA for Visual SLAM
Schulz, V. H., Bombardelli, F. G., & Todt, E. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-47247-8_4 · Cited by 15
Linear distance metric learning for large-scale generic image recognition
中山, 英. (2011) · Medical Entomology and Zoology · Cited by 15
A Multi-Resolution Approach for an Automated Fusion of Different Low-Cost 3D Sensors
Dupuis, J., Paulus, S., Behmann, J., et al. (2014) · Sensors · doi:10.3390/s140407563 · Cited by 15
Visual Mouse: SIFT Detection and PCA Recognition
Zhou, H., Xie, L., & Fang, X. (2007) · International Conference on Computational Intelligence and Security Workshops (CISW) · doi:10.1109/cisw.2007.4425487 · Cited by 15
Dong, X., Kim, W. Y., & Lee, K. (2021) · Journal of Biosystems Engineering · doi:10.1007/s42853-021-00120-y · Cited by 15
Counting People Based on Linear, Weighted, and Local Random Forests
Farhood, H., He, X., Jia, W., et al. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227421 · Cited by 15
A Survey of Landmark Recognition Using the Bag-of-Words Framework
Bhattacharya, P., & Gavrilova, M. L. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-31745-3_13 · Cited by 15
Robust Isometric Non-Rigid Structure-From-Motion
Parashar, S., Pizarro, D., & Bartoli, A. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3089923 · Cited by 15
Adaptive Gaze Control for Object Detection
de Croon, G., Postma, E. O., & van den Herik, H. J. (2011) · Cognitive Computation · doi:10.1007/s12559-010-9093-9 · Cited by 15
3D Scene Reconstruction with an Un-calibrated Light Field Camera
Zhang, Q., Li, H., Wang, X., et al. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01516-1 · Cited by 15
An assessment of methods for aligning two-dimensional microscope sections to create image volumes
Beare, R. J., Richards, K., Murphy, S. J., Petrou, S., & Reutens, D. C. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.01.012 · Cited by 15
Verykokou, S., & Ioannidis, C. (2016) · The Photogrammetric Record · doi:10.1111/phor.12156 · Cited by 15
Image Recognition by Deep Learning
Fujiyoshi, H., & Yamashita, T. (2017) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.35.180 · Cited by 15
Towards robust cascaded regression for face alignment in the wild
Qu, C., Gao, H., Monari, E., et al. (2015) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301348 · Cited by 15
Image-Based Camera Localization Algorithm for Smartphone Cameras Based on Reference Objects
Jabborov, F., & Cho, J. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07487-9 · Cited by 15
Paul, S. K., Nicolescu, M., & Nicolescu, M. N. (2023) · Sensors · doi:10.3390/s23135798 · Cited by 16
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and SteganographyPreprint
Wang, Z., Byrnes, O., Hu, W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09287 · Cited by 15
Huang, Y., Qiao, X., Ren, P., Liu, L., Pu, C., & Chen, J. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00149 · Cited by 16
Stacked Siamese Neural Network (SSiNN) on Neural Codes for Content-Based Image Retrieval
Gopu, V. R. M. K., & Madhavi, D. (2023) · IEEE Access · doi:10.1109/access.2023.3298216 · Cited by 15
Seppälä, O., Pohjonen, A. S., Mendonça, J., et al. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.111956 · Cited by 15
Su, M., Ma, Y., Zhang, X., Wang, Y., & Zhang, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178090 · Cited by 15
Zamzami, N., & Bouguila, N. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027539 · Cited by 15
Pain intensity estimation by a self-taught selection of histograms of topographical features
Florea, C., Florea, L., Butnaru, R., Bandrabur, A., & Vertan, C. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.08.014 · Cited by 14
Guo, S., Wang, D., Feng, Z., & Guo, W. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3116040 · Cited by 15
Ahn, J., Yasukawa, S., Sonoda, T., et al. (2017) · Journal of Marine Science and Technology · doi:10.1007/s00773-017-0448-8 · Cited by 15
Environment-Adaptable Printed-Circuit Board Positioning Using Deep Reinforcement Learning
Solórzano, C. A. P., & Tsai, D. (2022) · IEEE Transactions on Components Packaging and Manufacturing Technology · doi:10.1109/tcpmt.2022.3142033 · Cited by 15
Interactive tracking of insect posture
Shen, M., Li, C., Huang, W., Szyszka, P., Shirahama, K., Grzegorzek, M., Merhof, D., & Deußen, O. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.05.011 · Cited by 15
MCRNet: Underwater image enhancement using multi-color space residual network
Qin, N., Wu, J., Liu, X., Lin, Z., & Wang, Z. (2024) · Biomimetic Intelligence and Robotics · doi:10.1016/j.birob.2024.100169 · Cited by 17
Distortion, rotation and scale invariant recognition of hollow Hindi characters
Kumar, M., Kumar, M., Jindal, M. K., et al. (2022) · Sadhana · doi:10.1007/s12046-022-01847-w · Cited by 15
Strain field measurements around notches using SIFT features and meshless methods
Gonzáles, G. L. G., & Meggiolaro, M. A. (2015) · Applied Optics · doi:10.1364/ao.54.004520 · Cited by 15
Performance Evaluation of SIFT-Based Descriptors for Object Recognition
Yue-hua, T., Skubic, M., Han, T., -, Y. X., & Chi, X. (2013) · Cited by 16
Calibrating Pan-Tilt Cameras with Telephoto Lenses
Huang, X., Gao, J., & Yang, R. (2007) · Springer eBooks · doi:10.1007/978-3-540-76386-4_11 · Cited by 15
Convolutional Neural Networks for joint object detection and pose estimation: A comparative studyPreprint
Massa, F., Aubry, M., & Marlet, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7190 · Cited by 20
State-of-the-art and trends of autonomous driving technology
Dingyi, Y., Wang, H., & Kaiming, Y. (2018) · IEEE International Symposium on Innovation and Entrepreneurship (TEMS-ISIE) · doi:10.1109/tems-isie.2018.8478449 · Cited by 18
Incorporating visual adjectives for image classification
Xie, L., Wang, J., Zhang, B., & Tian, Q. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.12.008 · Cited by 15
Kong, X. (2021) · Remote Sensing · doi:10.3390/rs13163152 · Cited by 15
SVD-SIFT for web near-duplicate image detection
Liu, H., Lu, H., & Xue, X. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5650235 · Cited by 15
Zhu, Y., Yang, T., Wang, M., et al. (2022) · Remote Sensing · doi:10.3390/rs14020342 · Cited by 15
Learning Residual Elastic Warps for Image Stitching under Dirichlet Boundary Condition
Kim, M., Lee, Y., Han, W. K., et al. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00397 · Cited by 15
A novel remote sensing image retrieval method based on visual salient point features
Wang, X., Shao, Z., Zhou, X., & Liu, J. (2014) · Sensor Review · doi:10.1108/sr-03-2013-640 · Cited by 15
Monocular Template-Based Tracking of Inextensible Deformable Surfaces under L 2-Norm
Shen, S., Shi, W., & Liu, Y. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12304-7_21 · Cited by 15
Three-dimensional object recognition system for enhancing the intelligence of a KUKA robot
Xie, S. Q., Cheng, D., Wong, S., et al. (2007) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-007-1112-y · Cited by 15
Recognition of boards using wood fingerprints based on a fusion of feature detection methods
Pahlberg, T., Hagman, O., & Thurley, M. J. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2014.12.014 · Cited by 16
Does Diversity Affect User Satisfaction in Image Search
Wu, Z., Zhou, K., Liu, Y., et al. (2019) · ACM Transactions on Information Systems · doi:10.1145/3320118 · Cited by 15
Head Detection and Tracking Using OpenCV
Alcantara, G. K. L., Evangelista, I. D. J., Malinao, J. V. B., et al. (2018) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem.2018.8666374 · Cited by 15
Abnormal Scene Change Detection from a Moving Camera Using Bags of Patches and Spider-Web Map
Hsieh, J.-W., Chuang, C.-H., Alghyaline, S., et al. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2381257 · Cited by 15
An Active Patch Model for Real World Texture and Appearance Classification
Mao, J., Zhu, J., & Yuille, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10578-9_10 · Cited by 15
Analysis of Mine Change Using 3D Spatial Information Based on Drone Image
Kim, D.-P., Kim, S.-B., & Back, K.-S. (2022) · Sustainability · doi:10.3390/su14063433 · Cited by 15
A power-efficient real-time architecture for SURF feature extraction
Wilson, C., Zicari, P., Craciun, S., et al. (2014) · International Conference on ReConFigurable Computing and FPGAs (ReConFig) · doi:10.1109/reconfig.2014.7032492 · Cited by 15
Joint Map and Symmetry Synchronization
Sun, Y., Liang, Z., Huang, X., & Qi-xing Huang, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_16 · Cited by 12
Markatopoulou, F., Mezaris, V., & Patras, I. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351108 · Cited by 15
A Survey of the Yolo Series of Object Detection Algorithms
Xu, J., Li, J., Zhou, Z.-R., et al. (2024) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip64812.2024.10873779 · Cited by 15
Tóth, B., Tóth, M. J., Papp, D., & Szücs, G. (2016) · CLEF (Working Notes) · Cited by 14
Efficient Rotation-Discriminative Template Matching
Marimón, D., & Ebrahimi, T. (2007) · Springer eBooks · doi:10.1007/978-3-540-76725-1_24 · Cited by 15
Atmosphere-aware photoclinometry for pixel-wise 3D topographic mapping of Mars
Liu, W. C., & Wu, B. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.09.017 · Cited by 15
IBM Research Australia at LifeCLEF2014: Plant Identification Task.
Chen, Q., Abedini, M., Garnavi, R., & Liang, X. (2014) · CLEF (Working Notes) · Cited by 15
Comparing Local Feature Descriptors in pLSA-Based Image Models
Hörster, E., Greif, T., Lienhart, R. W., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69321-5_45 · Cited by 15
A bi-directional evaluation-based approach for image retargeting quality assessment
Oliveira, S. A. F., Alves, S. S. A., Gomes, J. P. P., et al. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.11.011 · Cited by 15
Twitter Food Photo Mining and Analysis for One Hundred Kinds of Foods
Yanai, ., & Kawano, Y. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13168-9_3 · Cited by 15
Global Propagation of Affine Invariant Features for Robust Matching
Cui, C., & Ngan, K. N. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2246521 · Cited by 15
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
Spencer, J., Bowden, R., & Hadfield, S. (2019) · 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.2019.00636 · Cited by 16
A robust RGB-D SLAM system for 3D environment with planar surfaces
Su, P.-C., Shen, J., & Cheung, S.-C. S. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738057 · Cited by 15
Attention-based multimodal image matching
Moreshet, A., & Keller, Y. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103949 · Cited by 15
Landmarks inside the shape: Shape matching using image descriptors
Güler, R. A., Tari, S. Z., & Ünal, G. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.07.013 · Cited by 15
Searching for repeated video sequences
Can, T., & Duygulu, P. (2007) · Multimedia Information Retrieval · doi:10.1145/1290082.1290112 · Cited by 15
Grewal, M., Wiersma, J., Westerveld, H. G., et al. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.1.014007 · Cited by 15
A new scale invariant feature detector and modified SURF descriptor
Huang, H., Lu, L., Yan, B., & Chen, J. (2010) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2010.5583377 · Cited by 15
Performance Analysis of Hand-Crafted Features and CNN Toward Real-Time Crop Disease Identification
Tiwari, V., Agrahari, A., & Srivastava, S. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-7078-0_48 · Cited by 15
Anomaly region detection and localization in metal surface inspection
Vaikundam, S., Hung, T.-Y., & Chia, L. T. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532459 · Cited by 15
On the impact of learning hierarchical representations for visual recognition in robotics
Ciliberto, C., Fanello, S., Santoro, M., Natale, L., Giorgio Metta, B., & Rosasco, L. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696893 · Cited by 14
Zhang, H., Wu, S., Luo, X., Huang, Y., & Li, H. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.105962 · Cited by 20
A blueprint for integrated eye-controlled environments
Bonino, D., Castellina, E., Corno, F., et al. (2009) · Universal Access in the Information Society · doi:10.1007/s10209-009-0145-4 · Cited by 15
Laminography as a tool for imaging large-size samples with high resolution
Nikitin, V., Wildenberg, G. A., Mittone, A., et al. (2024) · Journal of Synchrotron Radiation · doi:10.1107/s1600577524002923 · Cited by 15
Toward Personalized Modeling: Incremental and Ensemble Alignment for Sequential Faces in the Wild
Peng, X., Zhang, S., Yu, Y., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-0996-8 · Cited by 15
Efficient Feature-Based Nonrigid Registration of Multiphase Liver CT Volumes
Kwon, D., Yun, I. D., Lee, K. H., et al. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.36 · Cited by 15
Keypoint matching by outlier pruning with consensus constraint
Liu, Y., Feng, R., & Zhang, H. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139965 · Cited by 14
Face Detection and Segmentation with Generalized Intersection over Union Based on Mask R-CNN
Lin, K., Zhao, H., Lv, J., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39431-8_11 · Cited by 15
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
