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 2,030
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 9,001–9,100 of 56,203
Diseases Classification for Tea Plant Using Concatenated Convolution Neural Network
Krisnandi, D., Pardede, H. F., Yuwana, R. S., Zilvan, V., Heryana, A., Fauziah, F., & Rahadi, V. P. (2019) · CommIT (Communication and Information Technology) Journal · doi:10.21512/commit.v13i2.5886 · Cited by 38
Building recognition using sketch-based representations and spectral graph matching
Chung, Y.-C., Han, T. X., & He, Z. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459444 · Cited by 35
A novel Fourier descriptor based image alignment algorithm for automatic optical inspection
Chen, C., Yeh, C.-W., & Yin, P.-Y. (2008) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2008.11.003 · Cited by 35
PassBYOP: Bring Your Own Picture for Securing Graphical Passwords
Bianchi, A., Oakley, I., & Kim, H. (2015) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2015.2487511 · Cited by 35
Orientation Covariant Aggregation of Local Descriptors with Embeddings
Tolias, G., Furon, T., & Jeǵou, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_25 · Cited by 31
Smets, B., d'Oreye, N. F., Kervyn, M., et al. (2016) · Journal of African Earth Sciences · doi:10.1016/j.jafrearsci.2016.04.010 · Cited by 35
A Novel Keypoint Detector Combining Corners and Blobs for Remote Sensing Image Registration
Ye, Y., Wang, M., Hao, S., & Zhu, Q. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.2980620 · Cited by 35
Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data
Astor, T., Dayananda, S., Nautiyal, S., & Wachendorf, M. (2020) · Agronomy · doi:10.3390/agronomy10101600 · Cited by 40
Max-margin Latent Dirichlet Allocation for Image Classification and Annotation
Wang, Y., & Mori, G. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.112 · Cited by 35
Dikmen, M., Ning, H., Lin, D. J., Cao, L., Le, V., Tsai, S.-F., Lin, K.-H., Li, Z., Yan, S., Huang, T. S., et al. (2008) · Cited by 35
Ground-based cloud classification by learning stable local binary patterns
Wang, Y., Shi, C., Wang, C., et al. (2018) · Atmospheric Research · doi:10.1016/j.atmosres.2018.02.023 · Cited by 35
Automated noninvasive classification of renal cancer on multiphase CT
Linguraru, M. G., Wang, S., Shah, F. A., et al. (2011) · Medical Physics · doi:10.1118/1.3633898 · Cited by 35
On the design of a novel JPEG quantization table for improved feature detection performance
Chao, J., Chen, H., & Steinbach, E. G. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738345 · Cited by 32
Yang, X., Shyu, M., Yu, H., et al. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2865828 · Cited by 35
Bi, H., Zheng, W., Lei, Q., et al. (2020) · Journal of Geophysical Research Solid Earth · doi:10.1029/2020jb019983 · Cited by 35
Robust layer-based boat detection and multi-target-tracking in maritime environments
Krüger, W. A., & Orlov, Z. (2010) · doi:10.1109/wssc.2010.5730254 · Cited by 35
Early Cognitive Vision: Feedback Mechanisms for the Disambiguation of Early Visual Representation
Pugeault, N. (2008) · doi:10.53846/goediss-3447 · Cited by 35
Interest Point Detector and Feature Descriptor Survey
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_6 · Cited by 37
Feature extraction and local Zernike moments based geometric invariant watermarking
Yuan, X., & Pun, C. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1405-0 · Cited by 35
Image Matching for Geomorphic Measurement Based on SIFT and RANSAC Methods
Wang, W., Jun, H., & Yiping, T. (2008) · doi:10.1109/csse.2008.318 · Cited by 35
Review of person re‐identification techniques
Saghafi, M. A., Hussain, A., Zaman, H. B., & Saad, M. H. M. (2014) · IET Computer Vision · doi:10.1049/iet-cvi.2013.0180 · Cited by 42
Fused sparsity and robust estimation for linear models with unknown variance
Chen, Y., & Dalalyan, A. S. (2012) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 35
Deep learning representation using autoencoder for 3D shape retrieval
Zhu, Z., Wang, X., Bai, S., Yao, C., & Bai, X. (2014) · International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · doi:10.1109/spac.2014.6982699 · Cited by 35
Semi-Supervised Image Classification With Self-Paced Cross-Task Networks
Wu, S., Ji, Q., Wang, S., Wong, H.-S., Yu, Z., & Xu, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2758522 · Cited by 36
Automatic orientation and 3D modelling from markerless rock art imagery
Lerma, J. L., Navarro, S., Cabrelles, M., Seguí, A. E., & Hernández‐López, D. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2012.08.002 · Cited by 35
Mahiddine, A., Seinturier, J., Boi, D. P. J.-M., et al. (2012) · International Conference on Virtual Systems and Multimedia (VSMM) · doi:10.1109/vsmm.2012.6365924 · Cited by 35
An Attention Cascade Global–Local Network for Remote Sensing Scene Classification
Shen, J., Yu, T., Yang, H., Wang, R., & Wang, Q. (2022) · Remote Sensing · doi:10.3390/rs14092042 · Cited by 37
Maiwald, F., Lehmann, C. U., & Lazariv, T. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10110748 · Cited by 35
Shen, W., Ma, L., Zhang, X., Li, X., Zhao, Y., Jing, Y., Feng, Y., Tan, X., Sun, F., & Lin, J. (2019) · Science China Life Sciences · doi:10.1007/s11427-019-9820-4 · Cited by 35
Evaluation Platform for Content-Based Image Retrieval Systems
Budíková, P., Batko, M., & Zezula, P. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24469-8_15 · Cited by 36
Fully pipelined FPGA-based architecture for real-time SIFT extraction
Vourvoulakis, J. V., Kalomiros, J. A., & Lygouras, J. N. (2015) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2015.11.013 · Cited by 35
IMPROVE: An innovative application for collaborative mobile mixed reality design review
Santos, P., Stork, A., Gierlinger, T., et al. (2007) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-007-0010-8 · Cited by 35
Matching RGB and Infrared Remote Sensing Images with Densely-Connected Convolutional Neural Networks
Zhu, R., Yu, D., Ji, S., & Lü, M. (2019) · Remote Sensing · doi:10.3390/rs11232836 · Cited by 36
Geometrically Invariant Object-Based Watermarking using SIFT Feature
Pham, V. Q., Miyaki, T., Yamasaki, T., et al. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379868 · Cited by 35
Enforcing similarity constraints with integer programming for better scene text recognition
Smith, D. L., Field, J., & Learned-Miller, E. (2011) · doi:10.1109/cvpr.2011.5995700 · Cited by 35
Automatic classification of medical X‐ray images using a bag of visual words
Zare, M. R., Mueen, A., & Seng, W. C. (2013) · IET Computer Vision · doi:10.1049/iet-cvi.2012.0291 · Cited by 35
They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers
Chang, X., Yu, Y., Yang, Y., & Xing, E. P. (2016) · 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.2016.208 · Cited by 32
Learning salient visual word for scalable mobile image retrieval
Yang, X., Qian, X., & Mei, T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2014.12.017 · Cited by 35
A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition
Keetha, N., Milford, M. J., & Garg, S. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3096751 · Cited by 35
Semantic-Shift for Unsupervised Object Detection
Liu, D., & Chen, T. (2006) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2006.192 · Cited by 35
Real-time concealed object detection and recognition in passive imaging at 250 GHz
Kowalski, M. (2019) · Applied Optics · doi:10.1364/ao.58.003134 · Cited by 35
Grebner, T., Riekenbrauck, R., & Waldschmidt, C. (2023) · IEEE Transactions on Radar Systems · doi:10.1109/trs.2023.3347734 · Cited by 35
FisheyeDet: A Self-Study and Contour-Based Object Detector in Fisheye Images
Li, T., Tong, G., Tang, H., Li, B., & Chen, B. (2020) · IEEE Access · doi:10.1109/access.2020.2987868 · Cited by 36
Hierarchical Forgery Classifier on Multi-Modality Face Forgery Clues
Liu, D., Zheng, Z., Peng, C., Yukai, W. A. N. G., Wang, N., & Gao, X. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3304913 · Cited by 40
Exploring Deep Learning-Based Visual Localization Techniques for UAVs in GPS-Denied Environments
Al-Jarrah, O. Y., Shatnawi, A. S., Shurman, M. M., Ramadan, O. A., & Muhaidat, S. (2024) · IEEE Access · doi:10.1109/access.2024.3440064 · Cited by 37
Bag of multimodal LDA models for concept formation
Nakamura, T., Nagai, T., & Iwahashi, N. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980324 · Cited by 33
Multiple Object Tracking Using SIFT Features and Location Matching
Ha, S.-W., & Moon, Y. H. (2011) · Cited by 35
Document search support by making physical documents transparent in projection-based mixed reality
Iwai, D., & Sato, K. (2010) · Virtual Reality · doi:10.1007/s10055-010-0159-5 · Cited by 35
Leaf Image Classification with Shape Context and SIFT Descriptors
Wang, Z., Lu, B., Chi, Z., et al. (2011) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2011.115 · Cited by 38
Jordt, A. (2014) · Multimedialen Archiv und Publikationsserver der Christian-Albrechts-Universität zu Kiel (Christian-Albrechts-Universität zu Kiel) · Cited by 36
WSUIE: Weakly Supervised Underwater Image Enhancement for Improved Visual Perception
Hong, L., Wang, X., Xiao, Z., Zhang, G., & Liu, J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3105144 · Cited by 35
Accurate Object Recognition with Shape Masks
Marszałek, M., & Schmid, C. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0479-2 · Cited by 36
Liao, C., Tang, H., Liu, Q., et al. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874001 · Cited by 35
Tightly-Coupled Stereo Visual-Inertial Navigation Using Point and Line Features
Kong, X., Wu, W., Zhang, L., & Wang, Y. (2015) · Sensors · doi:10.3390/s150612816 · Cited by 38
Assembly manipulation of small objects by dual‐arm manipulator
Shauri, R. L. A., & Nonami, K. (2011) · Assembly Automation · doi:10.1108/01445151111150604 · Cited by 38
Quadruplet Networks for Sketch-Based Image Retrieval
Seddati, O., Dupont, S., & Mahmoudi, S. (2017) · ACM International Conference on Multimedia Retrieval (ICMR) · doi:10.1145/3078971.3078985 · Cited by 35
Regulation of flagellar motor switching by c-di-GMP phosphodiesterases in Pseudomonas aeruginosa
Xin, L., Zeng, Y., Sheng, S., et al. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.009009 · Cited by 35
Scene Categorization Model Using Deep Visually Sensitive Features
Shi, J., Zhu, H., Yu, S., Wu, W., & Shi, H. W. (2019) · IEEE Access · doi:10.1109/access.2019.2908448 · Cited by 34
The Nature-Inspired BASIS Feature Descriptor for UAV Imagery and Its Hardware Implementation
Fowers, S. G., Lee, D.-J., Ventura, D. A., & Archibald, J. K. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2223631 · Cited by 34
SPARTAN: Developing a Vision System for Future Autonomous Space Exploration Robots
Kostavelis, I., Nalpantidis, L., Boukas, E., Rodrigalvarez, M. A., Stamoulias, I., Lentaris, G., Diamantopoulos, D., Siozios, K., Soudris, D., & Gasteratos, A. C. (2013) · Journal of Field Robotics · doi:10.1002/rob.21484 · Cited by 35
Efficient scale space auto-context for image segmentation and labeling
Jiang, J., & Zhuowen Tu, A. (2009) · 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.2009.5206761 · Cited by 34
EVALUATING THE POTENTIAL OF RTK-UAV FOR AUTOMATIC POINT CLOUD GENERATION IN 3D RAPID MAPPING
Fazeli, H., Samadzadegan, F., & Javan, F. D. (2016) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xli-b6-221-2016 · Cited by 35
Intelligent visual localization of wireless capsule endoscopes enhanced by color information
Dimas, G., Spyrou, E., Iakovidis, D., & Koulaouzidis, A. (2017) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2017.08.029 · Cited by 34
It's Not What It Looks Like: Manipulating Perceptual Hashing based Applications
Hao, Q., Luo, L., Jan, S. T. K., & Wang, G. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484559 · Cited by 43
Liao, W.-S., Chen, K., & Hsu, W. H. (2008) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1390334.1390494 · Cited by 35
A deep metric learning approach for histopathological image retrieval
Yang, P., Zhai, Y., Li, L., Lv, H., Wang, J., Zhu, C., & Jiang, R. (2020) · Methods · doi:10.1016/j.ymeth.2020.05.015 · Cited by 34
DeepIndex for Accurate and Efficient Image Retrieval
Liu, Y., Guo, Y., Wu, S., et al. (2015) · ACM International Conference on Multimedia Retrieval (ICMR) · doi:10.1145/2671188.2749300 · Cited by 35
Automatic feature based image registration using SIFT algorithm
MAHESH, M., & Subramanyam, M. V. (2012) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2012.6396024 · Cited by 35
Unsupervised texture transfer from images to model collections
Wang, T. Y., Su, H., Huang, Q., Huang, J., Guibas, L. J., & Mitra, N. J. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2982404 · Cited by 35
A Pose Proposal and Refinement Network for Better 6D Object Pose Estimation
Trabelsi, A., Chaâbane, M. A., Blanchard, N., & Beveridge, R. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00243 · Cited by 34
Egocentric Audio-Visual Object Localization
Huang, C., Tian, Y., Kumar, A., & Xu, C. (2023) · 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/cvpr52729.2023.02194 · Cited by 37
3D Reconstruction of Space Objects from Multi-Views by a Visible Sensor
Zhang, H., Wei, Q., & Jiang, Z. (2017) · Sensors · doi:10.3390/s17071689 · Cited by 35
Histograms of Categorized Shapes for 3D ear detection
Zhou, J., Cadavid, S., & Abdel-Mottaleb, M. S. (2010) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2010.5634512 · Cited by 35
Inference and Learning with Hierarchical Shape Models
Kokkinos, I., & Yuille, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0398-7 · Cited by 35
Building facade interpretation from uncalibrated wide-baseline image sequences
Mayer, H., & Reznik, S. (2006) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2006.10.007 · Cited by 35
A coarse-to-fine visual geo-localization method for GNSS-denied UAV with oblique-view imagery
Ye, Q., Luo, J., & Lin, Y. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.05.006 · Cited by 35
Telikani, A., Sarkar, A., Du, B., Santoso, F., Shen, J., Yan, J. H., Yong, J., & Yap, E. W. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3530913 · Cited by 43
Brachmann, E., Wynn, J., Chen, S., Cavallari, T., Monszpart, Á., Turmukhambetov, D., & Prisacariu, V. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72992-8_24 · Cited by 38
Contour and texture preservation underwater image restoration via low-rank regularizations
Yu, Q., Hou, G., Zhang, W., Huang, B., & Pan, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125549 · Cited by 38
Creating Panoramic Images for Bladder Fluorescence Endoscopy
Behrens, A. (2008) · Acta Polytechnica · doi:10.14311/1013 · Cited by 35
Towards improving the efficiency of sequence-based SLAM
Liu, Y., & Zhang, H. (2013) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2013.6618095 · Cited by 34
Efficient detection and tracking of moving objects in geo-coordinates
Lin, Y., Yu, Q., & Medioni, G. G. (2010) · Machine Vision and Applications · doi:10.1007/s00138-010-0264-1 · Cited by 35
Heo, Y. S., Lee, K. M., & Lee, S. U. (2009) · 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.2009.5206507 · Cited by 35
Golparvar‐Fard, M., Savarese, S., & Pena-Mora, F. A. (2010) · ASCE Construction Research Congress (CRC) · doi:10.1061/41109(373)6 · Cited by 35
A systematic approach for 2D-image to 3D-range registration in urban environments
Liu, L., & Stamos, I. (2007) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409215 · Cited by 35
Wu, Z., Weise, T., Wang, Y., & Wang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3019956 · Cited by 34
3D Human Pose Estimation from multi-view thermal vision sensors
Lupión, M., Polo-Rodríguez, A., Medina-Quero, J., Sanjuan, J. F., & Ortigosa, P. M. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102154 · Cited by 36
A supervised learning framework for generic object detection in images
Ali, S., & Shah, M. A. (2005) · doi:10.1109/iccv.2005.22 · Cited by 35
Image-based navigation for functional endoscopic sinus surgery using structure from motion
Leonard, S., Reiter, A., Sinha, A., Ishii, M., Taylor, R. H., & Hager, G. D. (2016) · SPIE Proceedings · doi:10.1117/12.2217279 · Cited by 35
Joint Camera Pose Estimation and 3D Human Pose Estimation in a Multi-camera Setup
Puwein, J., Ballan, L., Ziegler, R., & Pollefeys, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16808-1_32 · Cited by 34
Noh, M., & Howat, I. M. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.12.008 · Cited by 36
Prosser-Contreras, M., Atencio, E., Rivera, F. M.-L., & Herrera, R. F. (2020) · Applied Sciences · doi:10.3390/app10248788 · Cited by 35
Ahmad, J., Sajjad, M., Mehmood, I., et al. (2015) · Journal of Real-Time Image Processing · doi:10.1007/s11554-015-0536-0 · Cited by 35
Compound Facial Expression Recognition Based on Highway CNN
Slimani, K., Lekdioui, K., Messoussi, R., & Touahni, R. (2019) · Conference of the Moroccan Classification Society: New Challenges in Data Sciences · doi:10.1145/3314074.3314075 · Cited by 38
Retinal image registration under the assumption of a spherical eye
Hernandez-Matas, C., Zabulis, X., Triantafyllou, Α., Anyfanti, P., & Argyros, A. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.06.006 · Cited by 35
Guided Matching Based on Statistical Optical Flow for Fast and Robust Correspondence Analysis
Maier, J., Humenberger, M., Murschitz, M., Zendel, O., & Vincze, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_7 · Cited by 37
Kim, H., Kim, D. J., & Park, K. (2011) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2011.2161249 · Cited by 35
A Comprehensive Survey on Pose-Invariant Face RecognitionPreprint
Ding, C., & Tao, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04383 · Cited by 35
A Review of Hashing Methods for Multimodal Retrieval
Cao, W., Feng, W., Lin, Q., Cao, G., & He, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2968154 · Cited by 39
Intrinsic and extrinsic active self-calibration of multi-camera systems
Brückner, M., Bajramovic, F., & Denzler, J. (2014) · Machine Vision and Applications · doi:10.1007/s00138-013-0541-x · Cited by 38
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
