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 9,401–9,500 of 56,203
Automatic matching of construction onsite resources under camera views
Zhang, B., Zhu, Z., Hammad, A., & Aly, W. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.03.011 · Cited by 34
Optimal Illumination and Color Consistency for Optical Remote-Sensing Image Mosaicking
Li, J., Hu, Q., & Ai, M. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2743209 · Cited by 33
Armstrong, L. F., Lacelle, D., Fraser, R. H., et al. (2018) · Arctic Science · doi:10.1139/as-2018-0016 · Cited by 33
Recognition and Tracking of 3D Objects
Wiedemann, C., Ulrich, M., & Steger, C. T. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69321-5_14 · Cited by 33
Human Motion Tracking by Temporal-Spatial Local Gaussian Process Experts
Zhao, X., Fu, Y., & Liu, Y. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2076820 · Cited by 33
Perception and probabilistic anchoring for dynamic world state logging
Blodow, N., Jain, D., Márton, Z.-C., & Beetz, M. (2010) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2010.5686341 · Cited by 33
An Unordered Image Stitching Method Based on Binary Tree and Estimated Overlapping Area
Qu, Z., Li, J., Bao, K.-H., & Si, Z. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2993134 · Cited by 32
On writer identification for Arabic historical manuscripts
Asi, A., Abdalhaleem, A., Fecker, D., et al. (2017) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-017-0289-3 · Cited by 33
Image retrieval based on gradient-structures histogram
Yuan, B., & Liu, G. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04657-0 · Cited by 33
SuperMatching: Feature Matching Using Supersymmetric Geometric Constraints
Cheng, Z., Chen, Y., Martin, R. R., Lai, Y., & Wang, A. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.15 · Cited by 33
RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching
Parihar, U. S., Gujarathi, A., Mehta, K., Tourani, S., Garg, S., Milford, M. J., & Krishna, K. M. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636619 · Cited by 37
Vision-Aided Inertial Navigation for Precise Planetary Landing: Analysis and Experiments
Mourikis, A. I., Trawny, N., Roumeliotis, S. I., Johnson, A., & Matthies, L. H. (2007) · Robotics: Science and Systems · doi:10.15607/rss.2007.iii.019 · Cited by 33
3DSEM++: Adaptive and intelligent 3D SEM surface reconstruction
Tafti, A. P., Holz, J. D., Baghaie, A., Owen, H. A., He, M. M., & Yu, Z. (2016) · Micron · doi:10.1016/j.micron.2016.05.004 · Cited by 35
Environment Perception Technologies for Power Transmission Line Inspection Robots
Chen, M., Tian, Y., Xing, S., Li, Z., Li, E., Liang, Z., & Guo, R. (2021) · Journal of Sensors · doi:10.1155/2021/5559231 · Cited by 35
3D Plant Modelling via Hyperspectral Imaging
Liang, J., Zia, A., Zhou, J. W., et al. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.29 · Cited by 33
SceneSqueezer: Learning to Compress Scene for Camera Relocalization
Yang, L., Shrestha, R., Li, W., Liu, S., Zhang, G., Cui, Z., & Tan, P. (2022) · 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/cvpr52688.2022.00808 · Cited by 34
Weakly Supervised Multilabel Clustering and its Applications in Computer Vision
Xia, Y., Nie, L., Zhang, L., Yang, Y., Hong, R., & Li, X. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2501385 · Cited by 33
Baumgartl, H., & Buettner, R. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.069 · Cited by 33
Hu, K., Niu, X., Liu, S., Zhang, Y., Cao, C., Xiao, F., Yang, W., & Gao, X. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.02.018 · Cited by 33
Learning a sparse codebook of facial and body microexpressions for emotion recognition
Song, Y., Morency, L., & Davis, R. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2522851 · Cited by 35
Absolute Triangulation Algorithms for Space Exploration
Henry, S., & Christian, J. A. (2022) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g006989 · Cited by 34
Object classification by fusing SVMs and Gaussian mixtures
Deselaers, T., Heigold, G., & Ney, H. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.02.002 · Cited by 33
Action Recognition by Multiple Features and Hyper-Sphere Multi-class SVM
Liu, J., Yang, J., Zhang, Y., et al. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.912 · Cited by 33
Chen, H., Hu, C., Lee, F., Lin, C., Yao, W., Lü, C., & Chen, Q. (2021) · Sensors · doi:10.3390/s21093094 · Cited by 35
Removal of specular reflections from image sequences using feature correspondences
Shah, S. M. Z. A., Marshall, S., & Murray, P. (2017) · Machine Vision and Applications · doi:10.1007/s00138-017-0826-6 · Cited by 33
The method of insulator recognition based on deep learning
Liu, Y., Yong, J., Liu, L., Zhao, J., & Li, Z. (2016) · International Conference on Applied Robotics for the Power Industry (CARPI) · doi:10.1109/carpi.2016.7745630 · Cited by 36
Improving the robustness in feature detection by local contrast enhancement
Vonikakis, V., Chrysostomou, D., Kouskouridas, R., & Gasteratos, A. C. (2012) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2012.6295482 · Cited by 34
From Bag-of-Visual-Words to Bag-of-Visual-Phrases Using n-Grams
Pedrosa, G. V., & Traina, A. J. M. (2013) · doi:10.1109/sibgrapi.2013.49 · Cited by 33
Multi-Relation Attention Network for Image Patch Matching
Quan, D., Wang, S., Li, Y., Yang, B., Huyan, N., Chanussot, J., Hou, B., & Jiao, L. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3101414 · Cited by 33
Vision-Based GNSS-Free Localization for UAVs in the Wild
Gurgu, M.-M., Queralta, J. P., & Westerlund, T. (2022) · International Conference on Mechanical Engineering and Robotics Research (ICMERR) · doi:10.1109/icmerr56497.2022.10097798 · Cited by 40
Visual recognition of aircraft mechanical parts for smart maintenance
Cusano, C., & Napoletano, P. (2017) · Computers in Industry · doi:10.1016/j.compind.2017.01.001 · Cited by 33
Mobile augmented reality for books on a shelf
Chen, D. M., Tsai, S. S., Hsu, C.-H., Singh, J., & Girod, B. (2011) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2011.6012171 · Cited by 34
G3Reg: Pyramid Graph-Based Global Registration Using Gaussian Ellipsoid Model
Qiao, Z., Yu, Z., Jiang, B., et al. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3394519 · Cited by 33
A swarm of wheeled and aerial robots for environmental monitoring
Carpentiero, M., Gugliermetti, L., Sabatini, M., et al. (2017) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2017.8000073 · Cited by 33
Document seal detection using GHT and character proximity graphs
Roy, P. P., Pal, U., & Lladós, J. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.12.004 · Cited by 33
SafeFac: Video-based smart safety monitoring for preventing industrial work accidents
Ahn, J., Park, J.-Y., Lee, S. S., Lee, K.-H., Do, H., & Ko, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119397 · Cited by 40
Computing the Uncertainty of the 8 point Algorithm for Fundamental Matrix Estimation
Sur, F., Noury, N., & Berger, M. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.96 · Cited by 33
Dynamic trees for unsupervised segmentation and matching of image regions
Todorović, S., & Nechyba, M. C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.219 · Cited by 33
Mosaic gradient histogram for object tracking in DoFP infrared polarization imaging
Qiao, X., Zhao, Y., Chen, L., Kong, S. G., & Chan, J. C.-W. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.10.006 · Cited by 33
A vehicle real-time detection algorithm based on YOLOv2 framework
Zhang, Z., Wang, H., Zhang, J., & Yang, W. (2018) · doi:10.1117/12.2309844 · Cited by 33
Robust Face Alignment Using a Mixture of Invariant Experts
Tuzel, O., Marks, T. K., & Tambe, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_50 · Cited by 30
MEB-YOLO: An Efficient Vehicle Detection Method in Complex Traffic Road Scenes
Song, Y., Hong, S., Hu, C., He, P., Tao, L., Tie, Z., & Ding, C. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.038910 · Cited by 36
Learning for mismatch removal via graph attention networks
Jiang, X., Wang, Y., Fan, A., & Ma, J. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.06.009 · Cited by 33
Ardizzone, E., Bruno, A., & Mazzola, G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41181-6_78 · Cited by 33
Improving Computer Vision-Based Perception for Collaborative Indoor Navigation
Ruotsalainen, L., Morrison, A. J., Mäkelä, M., Rantanen, J., & Sokolova, N. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3106257 · Cited by 32
A direct approach for object detection with catadioptric omnidirectional cameras
Çınaroğlu, İ., & Baştanlar, Y. (2015) · Signal Image and Video Processing · doi:10.1007/s11760-015-0768-2 · Cited by 33
Matching Features Correctly through Semantic Understanding
Kobyshev, N., Riemenschneider, H., & Gool, L. V. (2014) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2014.15 · Cited by 32
Defocus Blur-Invariant Scale-Space Feature Extractions
Saad, E., & Hirakawa, K. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2555702 · Cited by 33
Monitoring the execution of robot plans using semantic knowledge
Bouguerra, A., Karlsson, L., & Saffiotti, A. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.08.003 · Cited by 33
Mendoza, J., & Pedrini, H. (2019) · Computational Intelligence · doi:10.1111/coin.12241 · Cited by 33
Zelinsky, G. J., Peng, Y., Berg, A., & Samaras, D. (2013) · Journal of Vision · doi:10.1167/13.3.30 · Cited by 33
UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review
Wang, K., Kooistra, L., Pan, R., Wang, W., & Valente, J. R. P. (2024) · Journal of Field Robotics · doi:10.1002/rob.22325 · Cited by 38
“Currently, there are many different feature points (such as oriented fast and rotated brief [ORB] points, Rublee et al., 2011; scale ‐ invariant feature transform points, Lowe, 2004; speeded up robust features points, Bay et al., 2006) which can be used in visual odometry.”
BackgroundAbdullah, A., Veltkamp, R. C., & Wiering, M. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178743 · Cited by 32
Robust edge-based 3D object tracking with direction-based pose validation
Wang, B., Zhong, F., & Qin, X. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6727-5 · Cited by 35
Multiple Human Association and Tracking from Egocentric and Complementary Top Views
Han, R., Feng, W., Zhang, Y., Zhao, J., & Wang, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3070562 · Cited by 35
Automatic Pain Detection on Horse and Donkey Faces
Hummel, H. I., Pessanha, F., Salah, A. A., van Loon, J. P. A. M., & Veltkamp, R. C. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00114 · Cited by 30
Gao, S., Chia, L.-T., Tsang, I. W.-H., & Ren, Z. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2299516 · Cited by 33
Gu, Y., Wang, C., & Li, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3211202 · Cited by 36
An Effective Approach to 3D Deformable Surface Tracking
Zhu, J., Hoi, S. C. H., Xu, Z., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88690-7_57 · Cited by 33
A scalable and flexible framework for smart video surveillance
Nazaré, A., & Schwartz, W. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.10.014 · Cited by 33
Multi-Weighted Co-Occurrence Descriptor Encoding for Vein Recognition
Wang, G., Sun, C., & Sowmya, A. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2922331 · Cited by 33
Joint Spectral Correspondence for Disparate Image Matching
Bansal, M., & Daniilidis, K. (2013) · 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.2013.361 · Cited by 34
Yun, Y., & Gu, I. Y. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.12.002 · Cited by 33
Image classification based on complex wavelet structural similarity
Rehman, A., Gao, Y., Wang, J., & Wang, Z. (2012) · Signal Processing Image Communication · doi:10.1016/j.image.2012.07.004 · Cited by 32
Tag refinement in an image folksonomy using visual similarity and tag co-occurrence statistics
Lee, S., Neve, W. D., & Ro, Y. M. (2010) · Signal Processing Image Communication · doi:10.1016/j.image.2010.10.002 · Cited by 32
Halfway through the semantic gap: Prosemantic features for image retrieval
Ciocca, G., Cusano, C., Santini, S., & Schettini, R. (2011) · Information Sciences · doi:10.1016/j.ins.2011.06.025 · Cited by 33
Rotation-Invariant Siamese Network for Low-Altitude Remote-Sensing Image Registration
Liu, Y., Gong, X., Chen, J., Chen, S., & Yang, Y. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3024776 · Cited by 33
Rapid contour detection for image classification
Rasche, C. (2017) · IET Image Processing · doi:10.1049/iet-ipr.2017.1066 · Cited by 33
Latent topics-based relevance feedback for video retrieval
Fernández-Beltrán, R., & Pla, F. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.09.007 · Cited by 33
Real-time object pose recognition and tracking with an imprecisely calibrated moving RGB-D camera
Pauwels, K., Ivan, V., Ros, E., et al. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942936 · Cited by 33
Study the accuracy of digital close range photogrammetry technique software as a measuring tool
Fawzy, H. E.-D. (2018) · Alexandria Engineering Journal · doi:10.1016/j.aej.2018.04.004 · Cited by 33
Computer assisted plant identification system for Android
Priyankara, H. A. C., & Withanage, D. (2015) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon.2015.7112336 · Cited by 34
A hierarchical multiview registration framework of TLS point clouds based on loop constraint
Wu, H., Yan, L., Xie, H., Wei, P., & Dai, J. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.11.004 · Cited by 35
Strip-based registration of serially acquired optical coherence tomography angiography
Heisler, M., Lee, S., Mammo, Z. N., et al. (2017) · Journal of Biomedical Optics · doi:10.1117/1.jbo.22.3.036007 · Cited by 33
Spatial extensions to bag of visual words
Viitaniemi, V., & Laaksonen, J. T. (2009) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1646396.1646441 · Cited by 34
3D face recognition: A comprehensive survey in 2022
Jing, Y., Lu, X., & Gao, S. (2023) · Computational Visual Media · doi:10.1007/s41095-022-0317-1 · Cited by 48
HiPPo: hierarchical POMDPs for planning information processing and sensing actions on a robot
Sridharan, M., Wyatt, J., & Dearden, R. (2008) · Cited by 32
Industry and Object Recognition: Applications, Applied Research and Challenges
Hirano, Y., García, C., Sukthankar, R., & Hoogs, A. J. (2006) · Lecture notes in computer science · doi:10.1007/11957959_3 · Cited by 34
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Hoffmann, M., Hoopes, A., Greve, D. N., Fischl, B. R., & Dalca, A. V. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00197 · Cited by 41
Khong, N. Z.-J., Zeng, Y., Lai, S.-K., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-81621-w · Cited by 33
Yu, L., Yang, E., Yang, B., Fei, Z., & Niu, C. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3279458 · Cited by 34
Detection and Classification of Moving Vehicle From Video Using Multiple Spatio-Temporal Features
Wang, Y., Ban, X.-J., Wang, H., Wu, D., Wang, H., Yang, S.-W., Liu, S.-N., & Lai, J. (2019) · IEEE Access · doi:10.1109/access.2019.2923199 · Cited by 22
Asymmetry-Based Quality Assessment of Face Images
Zhang, G., & Wang, Y. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10520-3_47 · Cited by 33
RGB-D Object SLAM Using Quadrics for Indoor Environments
Liao, Z., Wang, W., Qi, X., & Zhang, X. (2020) · Sensors · doi:10.3390/s20185150 · Cited by 34
Visual odometry learning for unmanned aerial vehicles
Guizilini, V., & Ramos, F. T. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979706 · Cited by 32
Indexing visual features: Real-time loop closure detection using a tree structure
Liu, Y., & Zhang, H. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6224741 · Cited by 32
Transfusive image manipulation
Yücer, K., Jacobson, A., Hornung, A., & Sorkine‐Hornung, O. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366195 · Cited by 31
Park, M., Tran, D. Q., Bak, J., Kulinan, A. S., & Park, S. (2023) · Journal of Safety Research · doi:10.1016/j.jsr.2023.08.018 · Cited by 37
Spatio-temporal features for robust content-based video copy detection
Willems, G., Tuytelaars, T., & Gool, L. V. (2008) · Multimedia Information Retrieval · doi:10.1145/1460096.1460143 · Cited by 34
Fast and Efficient Method for Large-Scale Aerial Image Stitching
Pham, N. T., Park, S., & Park, C. (2021) · IEEE Access · doi:10.1109/access.2021.3111203 · Cited by 33
Huang, C.-R., Chen, Y.-T., Chen, W., Cheng, H., & Sheu, B. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2466460 · Cited by 33
Generalized Max PoolingPreprint
Murray, N., & Perronnin, F. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.0312 · Cited by 33
Leaving Flatland: Toward real-time 3D navigation
Morisset, B., Rusu, R. B., Sundaresan, A., Hauser, K. K., Agrawal, M., Latombe, J., & Beetz, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152715 · Cited by 33
Bundle adjustment from sonar images and SLAM application for seafloor mapping
Shin, Y.-S., Lee, Y., Choi, H., & Kim, A. (2015) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans.2015.7401963 · Cited by 32
6D scan registration using depth-interpolated local image features
Andreasson, H., & Lilienthal, A. J. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2009.09.011 · Cited by 33
3D stereo estimation and fully automated learning of eye-hand coordination in humanoid robots
Fanello, S., Pattacini, U., Gori, I., et al. (2014) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2014.7041491 · Cited by 33
Foveated Nonlocal Self-Similarity
Foi, A., & Boracchi, G. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0898-1 · Cited by 33
Distinguishing the Indistinguishable: Exploring Structural Ambiguities via Geodesic Context
Yan, Q., Yang, L., Zhang, L., & Xiao, C. (2017) · 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.2017.24 · Cited by 33
Part-based annotation-free fine-grained classification of images of retail products
Santra, B., Shaw, A. K., & Mukherjee, D. P. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108257 · Cited by 35
Locate, Size and Count: Accurately Resolving People in Dense Crowds via DetectionPreprint
Sam, D. B., Peri, S. V., Sundararaman, M. N., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07538 · Cited by 33
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
