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 3,760
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 2,360
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 3,801–3,900 of 56,204
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
Sarlin, P.-E., Dusmanu, M., Schönberger, J. L., Speciale, P., Gruber, L., Larsson, V., Mikšík, O., & Pollefeys, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20071-7_40 · Cited by 97
A Review of Deep Learning-based Human Activity Recognition on Benchmark Video Datasets
Sharma, V., Gupta, M., Pandey, A. K., Mishra, D., & Kumar, A. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2093705 · Cited by 97
Towards Interpretable Face Recognition
Yin, B., Tran, L., Li, H., Shen, X., & Liu, X. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00944 · Cited by 90
Feature based local binary pattern for rotation invariant texture classification
Pan, Z., Li, Z., Fan, H., & Wu, X. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.07.007 · Cited by 89
Abdel-Aty, M. A., Wang, Z., Zheng, O., & Abdelraouf, A. (2023) · Accident Analysis & Prevention · doi:10.1016/j.aap.2023.107191 · Cited by 104
Zheng, X., Sun, X., Fu, K., et al. (2012) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2012.2216499 · Cited by 87
An Analysis and Implementation of the Harris Corner Detector
Sánchez, J., Monzón, N., & Salgado, A. (2018) · Image Processing On Line · doi:10.5201/ipol.2018.229 · Cited by 83
An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images
Qin, R. (2014) · Remote Sensing · doi:10.3390/rs6097911 · Cited by 87
Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery
Gong, Y., Xiao, Z., Tan, X., Sui, H., Xu, C., Duan, H., & Li, D. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2930246 · Cited by 93
Building Multiversal Semantic Maps for Mobile Robot Operation
Ruiz-Sarmiento, J.-R., Galindo, C., & González-Jiménez, J. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.12.016 · Cited by 93
Adaptive hash retrieval with kernel based similarity
Bai, X. P., Yan, C., Yang, H., Bai, L., Zhou, J. W., & Hancock, E. R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.03.020 · Cited by 87
Active learning with multi-label SVM classification
Li, X. Y., & Guo, Y. (2013) · Cited by 87
A note on multi-image denoising
Buades, T., Lou, Y., Morel, J., & Tang, Z. (2009) · International Workshop on Local and Non-Local Approximation in Image Processing (LNLA) · doi:10.1109/lnla.2009.5278408 · Cited by 87
Object Mining Using a Matching Graph on Very Large Image Collections
Philbin, J. F., & Zisserman, A. (2008) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1109/icvgip.2008.103 · Cited by 84
A Novel Neural Network for Remote Sensing Image Matching
Zhu, H., Jiao, L., Ma, W., Liu, F., & Zhao, W. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2888757 · Cited by 86
Kitamura, K., Yamasaki, T., & Aizawa, K. (2009) · ACM Workshop on Multimedia for Cooking and Eating Activities (CEA) · doi:10.1145/1630995.1631001 · Cited by 84
Video Stabilization Using Scale-Invariant Features
Hu, R. J., Shi, R., Shen, I.-F., et al. (2007) · Proceedings · doi:10.1109/iv.2007.119 · Cited by 86
Online visual motion estimation using FastSLAM with SIFT features
Barfoot, T. D. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2005.1545444 · Cited by 86
High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder
Zhou, W., Shao, Z., Diao, C., & Cheng, Q. (2015) · Remote Sensing Letters · doi:10.1080/2150704x.2015.1074756 · Cited by 85
Rank-based pooling for deep convolutional neural networks
Shi, Z., Ye, Y., & Wu, Y. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.07.003 · Cited by 88
Kosov, S., Shirahama, K., Li, C., & Grzegorzek, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.12.021 · Cited by 85
Sewer pipe defect detection via deep learning with local and global feature fusion
Li, D., Xie, Q., Yu, Z., Wu, Q., Zhou, J., & Wang, J. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103823 · Cited by 87
Visual servoing for humanoid grasping and manipulation tasks
Vahrenkamp, N., Wieland, S., Azad, P., Gonzalez, D., Asfour, T., & Dillmann, R. (2008) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2008.4755985 · Cited by 84
Vehicle localization using mono-camera and geo-referenced traffic signs
Qu, X., Soheilian, B., & Paparoditis, N. (2015) · 2015 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2015.7225751 · Cited by 85
Autonomous flight in unstructured and unknown indoor environments
Bachrach, A. (2009) · Cited by 87
Person Reidentification by Minimum Classification Error-Based KISS Metric Learning
Tao, D.-P., Jin, L., Wang, Y., & Li, X.-L. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2323992 · Cited by 95
Large-scale image classification with trace-norm regularization
Harchaoui, Z., Douze, M., Paulin, M., Dudı́k, M., & Malick, J. (2012) · 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.2012.6248078 · Cited by 87
Feature Co-occurrence Maps: Appearance-based localisation throughout the day
Johns, E., & Yang, G. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631024 · Cited by 83
Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
Li, Y., Hu, W., Dong, H., & Zhang, X. (2019) · Applied Sciences · doi:10.3390/app9061128 · Cited by 94
Cosmological reconstruction from galaxy light: neural network based light-matter connection
Modi, C., Feng, Y., & Seljak, U. (2018) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2018/10/028 · Cited by 86
“In addition, we use Gaussian difference (GD) which is an approximate blob detection technique used in computer vision [27] to identify peaks in”
MethodIBFDS: Intelligent bone fracture detection system
Dimililer, K. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.11.237 · Cited by 86
Noviyanto, A., & Arymurthy, A. M. (2013) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2013.09.002 · Cited by 86
Pinz, A. J. (2005) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000003 · Cited by 84
Zhou, Y., Zhang, L., Cao, Y., & Wu, Z. (2018) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2875503 · Cited by 86
Image based detection of geometric changes in urban environments
Taneja, A., Ballan, L., & Pollefeys, M. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126515 · Cited by 84
Tobiasz, A., Markiewicz, J. S., Łapiński, S., Nikel, J., Kot, P., & Muradov, M. (2019) · Sustainability · doi:10.3390/su11247046 · Cited by 94
Multimodal Classification of Moderated Online Pro-Eating Disorder Content
Chancellor, S., Kalantidis, Y., Pater, J. A., Choudhury, M. D., & Shamma, D. A. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025985 · Cited by 97
A Benchmark Dataset to Study the Representation of Food Images
Farinella, G. M., Allegra, D., & Stanco, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16199-0_41 · Cited by 85
Implementation of ultra-light UAV systems for cultural heritage documentation
Bakırman, T., Bayram, B., Akpınar, B., et al. (2020) · Journal of Cultural Heritage · doi:10.1016/j.culher.2020.01.006 · Cited by 86
Image Feature Representation of the Subband Power Distribution for Robust Sound Event Classification
Dennis, J. W., Tran, H. D., & Chng, E. S. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2012.2226160 · Cited by 86
Real-time bag of words, approximately
Uijlings, J., Smeulders, A. W. M., & Scha, R. J. H. (2009) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1646396.1646405 · Cited by 87
Computer vision based crowd disaster avoidance system: A survey
Yogameena, B., & Nagananthini, C. (2017) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2017.02.021 · Cited by 90
Image classification by combining local and global features
Kabbai, L., Abdellaoui, M., & Douik, A. (2018) · The Visual Computer · doi:10.1007/s00371-018-1503-0 · Cited by 85
Ham, Y., & Golparvar‐Fard, M. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.02.054 · Cited by 87
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
Zheng, L., Wang, S., Zhou, W., et al. (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.2014.252 · Cited by 86
Towards cross-category knowledge propagation for learning visual concepts
Qi, G.-J., Aggarwal, C. C., Rui, Y., Tian, Q. C., Chang, S., & Huang, T. S. (2011) · 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.2011.5995312 · Cited by 85
Large-Scale Structure from Motion with Semantic Constraints of Aerial Images
Chen, Y., Wang, Y., Lu, P., Chen, Y., & Wang, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03398-9_30 · Cited by 96
PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
Truong, P., Danelljan, M., Timofte, R., & Gool, L. V. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3249225 · Cited by 96
Scale-Aware Trident Networks for Object DetectionPreprint
Li, Y., Chen, Y., Wang, N., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.01892 · Cited by 86
Sub-linear Indexing for Large Scale Object Recognition
Obdržálek, Š., & Matas, J. (2005) · British Machine Vision Conference (BMVC) · doi:10.5244/c.19.3 · Cited by 85
Xie, L., Wang, J., Zhang, B., & Tian, Q. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2408566 · Cited by 81
TIB-Net: Drone Detection Network With Tiny Iterative Backbone
Sun, H., Yang, J. J., Shen, J., Liang, D., Ning-Zhong, L., & Zhou, H. (2020) · IEEE Access · doi:10.1109/access.2020.3009518 · Cited by 93
Hough Pyramid Matching: Speeded-Up Geometry Re-ranking for Large Scale Image Retrieval
Avrithis, Y., & Tolias, G. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0659-3 · Cited by 83
Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks
Zhong, J., Leí, T., & Yao, G. (2017) · Sensors · doi:10.3390/s17122720 · Cited by 86
AMNet: Memorability Estimation with Attention
Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (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.2018.00666 · Cited by 89
Cross-Domain Color Facial Expression Recognition Using Transductive Transfer Subspace Learning
Zheng, W., Zong, Y., Zhou, X., et al. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2563432 · Cited by 86
A fast SIFT based method for copy move forgery detection
Alberry, H. A., Hegazy, A. A., & Salama, G. I. (2018) · Future Computing and Informatics Journal · doi:10.1016/j.fcij.2018.03.001 · Cited by 86
Transferring Visual Prior for Online Object Tracking
Wang, Q., Chen, F., Yang, J., Xu, W., & Yang, M. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2190085 · Cited by 88
Ströbel, B., Schmelzle, S., Blüthgen, N., & Heethoff, M. (2018) · ZooKeys · doi:10.3897/zookeys.759.24584 · Cited by 85
Correlation Verification for Image Retrieval
Lee, S., Seong, H., Lee, S., & Kim, E. (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/cvpr52688.2022.00530 · Cited by 90
Wan, F., Bohr, S. S.-R., Kłodzińska, S. N., Jumaa, H., Huang, Z., Nylander, T., Thygesen, M. B., Sørensen, K. K., Jensen, K. J., Sternberg, C., et al. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b19644 · Cited by 87
Boosted human re-identification using Riemannian manifolds
Bąk, S., Corvée, E., Brémond, F., & Thonnat, M. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.08.008 · Cited by 86
3D model retrieval using hybrid features and class information
Li, B., & Johan, H. (2011) · Multimedia Tools and Applications · doi:10.1007/s11042-011-0873-3 · Cited by 83
Direct Visual Odometry in Low Light Using Binary Descriptors
Alismail, H., Kaess, M., Brett Browning, A., & Lucey, S. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2635686 · Cited by 81
Parameter-Free Weighted Multi-View Projected Clustering with Structured Graph Learning
Wang, R., Nie, F., Wang, Z., Hu, H., & Li, X. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2913377 · Cited by 85
Abid, M. R., Petriu, E. M., & Amjadian, E. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2014.2351331 · Cited by 85
OmniGlue: Generalizable Feature Matching with Foundation Model Guidance
Jiang, H., Karpur, A., Cao, B., Huang, Q., & Araújo, A. D. M. (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/cvpr52733.2024.01878 · Cited by 109
“The past decade has witnessed the evolution from hand-crafted [3, 29] to learning-based image features [10, 36, 38, 51, 55].”
BackgroundMethodInfluentialPhotogrammetric processing of low‐altitude images acquired by unpiloted aerial vehicles
Zhang, Y., Xiong, J., & Hao, L. (2011) · The Photogrammetric Record · doi:10.1111/j.1477-9730.2011.00641.x · Cited by 84
BOLD Features to Detect Texture-less Objects
Tombari, F., Franchi, A., & Di, L. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.160 · Cited by 85
Anderson, A. J., Bruni, E., Lopopolo, A., Poesio, M., & Baroni, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.093 · Cited by 77
Semantic-Aware Co-indexing for Image Retrieval
Zhang, S. S., Yang, M., Wang, X., Lin, Y., & Tian, Q. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.210 · Cited by 86
Rey-Otero, I., & Delbracio, M. (2014) · Image Processing On Line · doi:10.5201/ipol.2014.82 · Cited by 89
Globally Optimal Consensus Set Maximization through Rotation Search
Bazin, J., Seo, Y., & Pollefeys, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37444-9_42 · Cited by 85
Guo, X., Hu, J., Chen, J., Deng, F., & Lam, T. L. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3058935 · Cited by 86
PWStableNet: Learning Pixel-Wise Warping Maps for Video Stabilization
Zhao, M., & Ling, Q. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2963380 · Cited by 87
A high-precision photogrammetric recording system for small artifacts
Sapirstein, P. N. (2017) · Journal of Cultural Heritage · doi:10.1016/j.culher.2017.10.011 · Cited by 85
AdaFusion: Visual-LiDAR Fusion With Adaptive Weights for Place Recognition
Lai, H., Yin, P., & Scherer, S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3210880 · Cited by 86
Chen, W., Yang, B., Li, J., & Wang, J. (2020) · IEEE Access · doi:10.1109/access.2020.3027794 · Cited by 95
Chen, W., Luo, J., Zhang, F., & Tian, Z. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17949-4 · Cited by 103
Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification
Jianping Fan, B., Zhou, N., Peng, J., & Gao, L. C. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2457337 · Cited by 86
DeepDeform: Learning Non-Rigid RGB-D Reconstruction With Semi-Supervised Data
Božič, A., Zollhöfer, M., Theobalt, C., & NieBner, M. (2020) · 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/cvpr42600.2020.00703 · Cited by 87
Distributed metric calibration of ad hoc camera networks
Devarajan, D., Radke, R. J., & Chung, H. (2006) · ACM Transactions on Sensor Networks · doi:10.1145/1167935.1167939 · Cited by 83
Deep ChArUco: Dark ChArUco Marker Pose Estimation
Hu, D., DeTone, D., & Malisiewicz, T. (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.2019.00863 · Cited by 91
Unifying visual saliency with HOG feature learning for traffic sign detection
Xie, Y., Liu, L., Li, C., et al. (2009) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2009.5164247 · Cited by 85
Traffic sign recognition using group sparse coding
Liu, H., Liu, Y., & Sun, F. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.010 · Cited by 85
A Comprehensive Review on 3D Object Detection and 6D Pose Estimation With Deep Learning
Hoque, S., Arafat, M. Y., Xu, S., Maiti, A., & Wei, Y.-C. (2021) · IEEE Access · doi:10.1109/access.2021.3114399 · Cited by 91
Large Foundation Model Empowered Discriminative Underwater Image Enhancement
Wang, H., Köser, K., & Ren, P. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3525962 · Cited by 94
Aldomà, A., Tombari, F., Prankl, J., et al. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630859 · Cited by 85
Comparison of OpenCV's feature detectors and feature matchers
Noble, F. K. (2016) · International Conference on Mechatronics and Machine Vision in Practice (M2VIP) · doi:10.1109/m2vip.2016.7827292 · Cited by 85
Query Adaptive Similarity for Large Scale Object Retrieval
Qin, D., Wengert, C., & Gool, L. V. (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.2013.211 · Cited by 79
Single scattering by realistic, inhomogeneous mineral dust particles with stereogrammetric shapes
Lindqvist, H., Jokinen, O. T., Kandler, K., et al. (2014) · Atmospheric chemistry and physics · doi:10.5194/acp-14-143-2014 · Cited by 85
Leveraging Context to Support Automated Food Recognition in Restaurants
Bettadapura, V., Thomaz, E., Parnami, A., Abowd, G. D., & Essa, I. A. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.83 · Cited by 86
Latent Pyramidal Regions for Recognizing Scenes
Sadeghi, F., & Tappen, M. F. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33715-4_17 · Cited by 84
Color texture image retrieval based on Gaussian copula models of Gabor wavelets
Li, C., Huang, Y., & Zhu, L. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.030 · Cited by 86
Deep Image Retrieval: Learning global representations for image searchPreprint
Gordo, A., Almazán, J., Revaud, J., & Larlus, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.01325 · Cited by 76
RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots
Iocchi, L., Holz, D., Ruiz-del-Solar, J., Sugiura, K., & van der Zant, T. (2015) · Artificial Intelligence · doi:10.1016/j.artint.2015.08.002 · Cited by 86
Multi-View and 3D Deformable Part Models
Pepik, B., Stark, M. M., Gehler, P., & Schiele, B. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408347 · Cited by 82
Wang, J., Lu, C., & Jiang, W. (2018) · Sensors · doi:10.3390/s18092851 · Cited by 89
3D textureless object detection and tracking: An edge-based approach
Choi, C., & Christensen, H. I. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6386065 · Cited by 83
Huang, J., Dai, A., Guibas, L. J., & Nießner, M. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130824 · Cited by 79
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
