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 11,401–11,500 of 56,200
Representations and Techniques for 3D Object Recognition and Scene Interpretation
Hoiem, D., & Savarese, S. (2011) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00370ed1v01y201107aim015 · Cited by 26
A Context-Aware Locality Measure for Inlier Pool Enrichment in Stepwise Image Registration
Zhang, S., Zhao, W., Hao, X., Yang, Y., & Guan, C. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2961480 · Cited by 27
Integrated Localization and Recognition for Inshore Ships in Large Scene Remote Sensing Images
Li, W., Fu, K., Sun, H., et al. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2688357 · Cited by 27
Invariant features-based automated registration and montage for wide-field OCT angiography
Wang, J., Camino, A., Hua, X., et al. (2018) · Biomedical Optics Express · doi:10.1364/boe.10.000120 · Cited by 27
Bio‐Metamaterials for Mechano‐Regulation of Mesenchymal Stem Cells
Munding, N., Fladung, M., Chen, Y., Hippler, M., Ho, A. D., Wegener, M., Bastmeyer, M., & Tanaka, M. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202301133 · Cited by 26
Combining Conformal Deformation and Cook–Torrance Shading for 3-D Reconstruction in Laparoscopy
Malti, A., & Bartoli, A. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300237 · Cited by 27
Episodic Training for Domain GeneralizationPreprint
Li, D., Zhang, J., Yang, Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00113 · Cited by 27
Kushwaha, A., Khare, A., & Khare, M. (2021) · International Journal of Image and Graphics · doi:10.1142/s0219467822500097 · Cited by 27
HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing
Kurihara, J., Takahashi, Y., Sakamoto, Y., et al. (2018) · Sensors · doi:10.3390/s18020619 · Cited by 27
Automatic fight detection in surveillance videos
Fu, E. Y., Leong, H. V., Ngai, G., & Chan, S. C. F. (2017) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-02-2017-0018 · Cited by 33
A Deep Learning Framework for Joint Image Restoration and Recognition
Chen, R., Mihaylova, L. S., Zhu, H., & Bouaynaya, N. C. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01222-x · Cited by 28
Designing User Friendly Augmented Work Environments
Lahlou, S. (2009) · Computer supported cooperative work · doi:10.1007/978-1-84800-098-8 · Cited by 27
Active multi-view object recognition: A unifying view on online feature selection and view planning
Potthast, C., Breitenmoser, A., Sha, F., et al. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.06.013 · Cited by 27
Miri, M. S., Abràmoff, M. D., Kwon, Y. H., & Garvin, M. K. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.005252 · Cited by 27
MCNet: Rethinking the Core Ingredients for Accurate and Efficient Homography Estimation
Zhu, H., Cao, S., Hu, J., Zuo, S., Yu, B., Ying, J., Li, J., & Shen, H. (2024) · 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/cvpr52733.2024.02450 · Cited by 27
“We conduct comparison including our MCNet, RHWF [4], IHN [3], MHN+DLKFM [34], MHN [11], DHN+DLKFM [34], DHN [6], CLKN [5], SIFT+MAGSAC [2], SIFT+RANSAC [15], and LK [1].”
MethodConstellations and the Unsupervised Learning of Graphs
Bonev, B., Escolano, F. J., Lozano, M. Á., et al. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72903-7_31 · Cited by 27
Discovering panoramas in web videos
Liu, F., Hu, Y. H., & Gleicher, M. L. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459404 · Cited by 26
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data
Mahadevan, V., Wong, C. W., Pereira, J. C., et al. (2011) · Cited by 26
Image-Based Modeling by Joint Segmentation
Quan, L., Wang, J., Tan, P., et al. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0044-1 · Cited by 26
Object tracking using learned feature manifolds
Guo, Y., Chen, Y., Tang, F., Li, A., Luo, W., & Liu, M. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.09.007 · Cited by 25
Universal multi-dimensional scaling
Agarwal, A. K., Phillips, J. M., & Venkatasubramanian, S. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835948 · Cited by 28
Global Multi-modal 2D/3D Registration via Local Descriptors Learning
Markova, V., Ronchetti, M., Wein, W., Zettinig, O., & Prevost, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16446-0_26 · Cited by 26
A SIFT descriptor based method for global disparity vector estimation in multiview video coding
Liu, K.-H., Liu, T.-J., & Liu, H. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5583884 · Cited by 26
LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR
Bartoccioni, F., Zablocki, É., Pérez, P., Cord, M., & Alahari, K. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103601 · Cited by 32
An Instance Segmentation approach to Food Calorie Estimation using Mask R-CNN
Poply, P., & Jothi, J. A. A. (2020) · doi:10.1145/3432291.3432295 · Cited by 26
A novel background subtraction method based on color invariants
Zhou, H., Chen, Y., & Feng, R. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.07.008 · Cited by 25
3D lung image retrieval using localized features
Depeursinge, A., Zrimec, T., Busayarat, S., & Müller, H. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877943 · Cited by 26
Yue, H., Sun, X., Wu, F., et al. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.52 · Cited by 26
Automated Vision-Based Diagnosis of Cassava Mosaic Disease.
Aduwo, J. R., Mwebaze, E., & Quinn, J. A. (2010) · Cited by 26
ELM embedded discriminative dictionary learning for image classification
Zeng, Y., Li, Y., Chen, J., Jia, X., & Huang, G.-B. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.11.015 · Cited by 25
Agostinho, S., Gomes, J. P., & Bue, A. D. (2022) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-022-01133-8 · Cited by 31
A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method
Kamencay, P., Jarina, R., Benčo, M., Hudec, R., Zachariášová, M., & Hlubík, J. (2013) · Brno University of Technology Digital Library (Brno University of Technology) · Cited by 26
Feature Selective Networks for Object Detection
Zhai, Y., Fu, J., Lu, Y., & Li, H. (2018) · 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.2018.00435 · Cited by 26
Maeda, Y., Fukushima, N., & Matsuo, H. (2018) · Applied Sciences · doi:10.3390/app8081235 · Cited by 27
A comparison of feature detectors and descriptors for object class matching
Hietanen, A., Lankinen, J., Kämäräinen, J., Buch, A. G., & Krüger, N. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.08.106 · Cited by 27
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
Liu, J., Sun, W., Yang, H., Zeng, Z., Liu, C., Zheng, J., Liu, X., Rahmani, H., Sebe, N., & Mian, A. S. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02646-6 · Cited by 31
Hybrid multi-layer deep CNN/aggregator feature for image classification
Kulkarni, P., Zepeda, J., Jurie, F., Pérez, P., & Chevallier, L. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178196 · Cited by 26
Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary PatternsPreprint
Zheng, Y., Shen, C., Hartley, R. I., et al. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1009.0892 · Cited by 26
Local Feature Based Geometric-Resistant Image Information Hiding
Gao, X., Deng, C. D., Li, X., & Tao, D. (2010) · Cognitive Computation · doi:10.1007/s12559-010-9033-8 · Cited by 26
Churchill, D. G., & Vardy, A. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4651166 · Cited by 26
Energy Based Multi-model Fitting & Matching for 3D Reconstruction
Isack, H. N., & Boykov, Y. (2014) · 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.2014.150 · Cited by 26
Outdoor Localization Using Stereo Vision Under Various Illumination Conditions
Kiyoshi, I. R. I. E., Yoshida, T., & Tomono, M. (2012) · Advanced Robotics · doi:10.1163/156855311x614608 · Cited by 25
Machine Learning and SIFT Approach for Indonesian Food Image Recognition
Giovany, S., Putra, A. M., Hariawan, A. S., et al. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.10.020 · Cited by 26
Aurora image search with contextual CNN feature
Yang, X., Gao, X., Song, B., & Yang, D. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.11.059 · Cited by 26
A Local Feature based on Lagrangian Measures for Violent Video Classification
Senst, T., Eiselein, V., & Sikora, T. (2015) · doi:10.1049/ic.2015.0104 · Cited by 26
Multiview Face Detection and Registration Requiring Minimal Manual Intervention
Anvar, S. M. H., Yau, W., & Teoh, E. K. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.37 · Cited by 26
Tseng, K.-K., Li, Y., Hsu, C., et al. (2013) · BioMed Research International · doi:10.1155/2013/687607 · Cited by 26
Deep learning features exception for cross-season visual place recognition
Kenshimov, C., Bampis, L., Amirgaliyev, B., Arslanov, M., & Gasteratos, A. C. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.10.028 · Cited by 24
Mercer, J. J., & Westbrook, C. J. (2016) · Journal of Geophysical Research Biogeosciences · doi:10.1002/2016jg003478 · Cited by 26
Zhu, Q., Sun, Y., Guan, Q., Wang, L., & Lin, W. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3170335 · Cited by 26
3D ear recognition using global and local features
Ganapathi, I. I., & Prakash, S. (2018) · IET Biometrics · doi:10.1049/iet-bmt.2017.0212 · Cited by 26
Rodríguez‐Ruiz, J., Medina‐Pérez, M. A., Monroy, R., et al. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.018 · Cited by 26
Pathology-oriented multiplexing enables integrative disease mapping
Kuehl, M. B., Okabayashi, Y., Wong, M. N., Gernhold, L., Gut, G., Kaiser, N., Schwerk, M., Gräfe, S. K., Ma, F. Y., Tanevski, J., et al. (2025) · Nature · doi:10.1038/s41586-025-09225-2 · Cited by 34
“After correcting for this offset, SIFT-based homography detection 86 was performed and images from subsequent cycles were warped to match the reference from the first cycle.”
Robust line segment matching across views via ranking the line-point graph
Wei, D., Zhang, Y., Liu, X., et al. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.11.002 · Cited by 26
Vehicle type classification from laser scanner profiles: A benchmark of feature descriptors
Sandhawalia, H., Rodríguez-Serrano, J. A., Poirier, H., et al. (2013) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2013.6728283 · Cited by 26
Cashmere/wool identification based on bag-of-words and spatial pyramid match
Lü, K., Zhong, Y., Li, D., et al. (2017) · Textile Research Journal · doi:10.1177/0040517517723027 · Cited by 26
MAV visual SLAM with plane constraint
Lee, G. H., Fraundorfer, F., & Pollefeys, M. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980365 · Cited by 28
Safa, A., Verbelen, T., Ocket, I., Bourdoux, A., Hichem Sahli, B., Catthoor, F., & Gielen, G. E. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160681 · Cited by 33
A Survey of Deep Learning Approaches for OCR and Document UnderstandingPreprint
Subramani, N., Matton, A., Greaves, M. W., & Lam, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13534 · Cited by 112
Mahmoudi, S. A., Mahmoudi, S. A., Belarbi, M. A., et al. (2020) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5699 · Cited by 26
Luppichini, M., Favalli, M., Isola, I., et al. (2019) · Remote Sensing · doi:10.3390/rs11131630 · Cited by 26
Learning Local RGB-to-CAD Correspondences for Object Pose Estimation
Georgakis, G., Karanam, S., Wu, Z., & Košecká, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00906 · Cited by 27
Maximum Detector Response Markers for SIFT and SURF
Schweiger, F., Zeisl, B., Georgel, P. F., et al. (2009) · Cited by 26
An ASIFT-Based Local Registration Method for Satellite Imagery
Wang, X., Li, Y., Wei, H., et al. (2015) · Remote Sensing · doi:10.3390/rs70607044 · Cited by 26
The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing
Argaw, D. M., Heilbron, F. C., Lee, J., Woodson, M., & Kweon, I. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_12 · Cited by 38
Logo detection based on spatial-spectral saliency and partial spatial context
Gao, K., Lin, S., Zhang, Y., et al. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202500 · Cited by 26
Chowdhury, S. A., Kowsar, M. M. S., & Deb, K. (2017) · ICT Express · doi:10.1016/j.icte.2017.11.016 · Cited by 26
Zhi, X., Yan, J., Hang, Y., & Wang, S. (2016) · Journal of Real-Time Image Processing · doi:10.1007/s11554-016-0594-y · Cited by 26
Towards Accurate and Robust Multi-Modal Medical Image Registration Using Contrastive Metric Learning
Hu, J., Sun, S., Yang, X., Zhou, S., Wang, X., Fu, Y., Zhou, J., Yin, Y., Cao, K., Song, Q., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2938858 · Cited by 27
Anomaly Detection in Moving-Camera Video Sequences Using Principal Subspace Analysis
Thomaz, L. A., Jardim, E., da Silva, A. F., da Silva, E. A. B., Netto, S. L., & Krim, H. (2017) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2017.2758379 · Cited by 26
Image classification with multi-view multi-instance metric learning
Tang, J., Li, D., & Tian, Y.-J. (2021) · Expert systems with applications · doi:10.1016/j.eswa.2021.116117 · Cited by 26
Wind turbine tower detection using feature descriptors and deep learning
Abedini, F., Bahaghighat, M., & Shoyan, M. (2020) · Facta universitatis - series Electronics and Energetics · doi:10.2298/fuee2001133a · Cited by 25
Ensemble Partitioning for Unsupervised Image Categorization
Dai, D., Prasad, M., Leistner, C., & Gool, L. V. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33712-3_35 · Cited by 25
SHOG - Spherical HOG Descriptors for Rotation Invariant 3D Object Detection
Skibbe, H., Reisert, M., & Burkhardt, H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23123-0_15 · Cited by 26
Copy move forgery detection based on keypoint and patch match
Liu, K., Lu, W., Lin, C., Huang, X., Liu, X., Yeung, Y., & Xue, Y. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07930-5 · Cited by 26
Robust Stereo-Match Algorithm for Infrared Markers in Image-Guided Optical Tracking System
Lin, Q., Yang, R., Zhang, Z., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2869433 · Cited by 26
Singaraju, D., & Vidal, R. (2011) · doi:10.1109/cvpr.2011.5995469 · Cited by 26
Latent visual context learning for web image applications
Zhou, W., Tian, Q. C., Lu, Y., Yang, L., & Li, H. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.08.016 · Cited by 26
Graphic logo detection with deep region-based convolutional networks
Li, Y., Shi, Q., Deng, J., et al. (2017) · IEEE Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2017.8305065 · Cited by 26
Joint CRF and Locality-Consistent Dictionary Learning for Semantic Segmentation
Li, Y., Guo, Y., Guo, J. F., et al. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2867720 · Cited by 26
Lin, R. F.-Y., Ou, C., Tseng, K.-K., et al. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121067 · Cited by 26
Evaluation of feature detection algorithms for structure from motion
Govender, N. (2009) · Cited by 26
Cirujeda, P., Cid, Y. D., Mateo, X., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0820-2 · Cited by 26
Gaussian Process Gauss-Newton for 3D laser-based Visual Odometry
Tong, C. H., & Barfoot, T. D. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631321 · Cited by 26
Registration of the rotation axis in X-ray tomography
Yang, Y., Yang, F., Hingerl, F. F., et al. (2015) · Journal of Synchrotron Radiation · doi:10.1107/s160057751402726x · Cited by 26
Video-based localization without 3D mapping for the visually impaired
Liu, J. J., Phillips, C. J., & Daniilidis, K. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543581 · Cited by 26
Touchless palmprint recognition based on 3D Gabor template and block feature refinement
Liang, X., Li, Z., Fan, D., Li, J., Jia, W., & Zhang, D. D. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108855 · Cited by 28
Rethinking the sGLOH Descriptor
Bellavia, F., & Colombo, C. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2697849 · Cited by 26
Unmanned Solar Airplanes: Design and Algorithms for Efficient and Robust Autonomous Operation
Leutenegger, S. (2014) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010255301 · Cited by 27
Multi-Instance Ensemble Learning With Discriminative Bags
Yang, M., Zhang, Y.-X., Wang, X., et al. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3125040 · Cited by 26
Gradient Feature-Oriented 3-D Domain Adaptation for Hyperspectral Image Classification
Jia, S., Liu, X., Xu, M. H., et al. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3073699 · Cited by 26
Deep multimodal fusion for ground-based cloud classification in weather station networks
Liu, S., & Mei, L. (2018) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-018-1062-0 · Cited by 26
Finding local leaf vein patterns for legume characterization and classification
Larese, M. G., & Granitto, P. M. (2015) · Machine Vision and Applications · doi:10.1007/s00138-015-0732-8 · Cited by 26
3D Models from the Black Box: Investigating the Current State of Image-Based Modeling
Nguyen, H., Wuensche, B. C., Delmas, P. J., et al. (2012) · Cited by 26
Lippolis, G., Edsjö, A., Helczynski, L., et al. (2013) · BMC Cancer · doi:10.1186/1471-2407-13-408 · Cited by 26
Chen, L., Bo, K., Lee, F., & Chen, Q. (2020) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2020.08425 · Cited by 26
CMOS-3D Smart Imager Architectures for Feature Detection
Suárez, M. P., Brea, V. M., Fernández‐Berni, J., Carmona‐Galán, R., Liñán, G., Cabello, D., & Rodriguez-Vazquez, A. (2012) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2012.2223552 · Cited by 26
Liu, A.-A., Tang, J., Nie, W., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2686705 · Cited by 26
EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera
Huang, Z., Sun, L., Zhao, C., Li, S., & Su, S. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00536 · Cited by 30
Maiwald, F., Bruschke, J., Lehmann, C. U., & Niebling, F. (2019) · Virtual Archaeology Review · doi:10.4995/var.2019.11867 · Cited by 28
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
