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 20,901–21,000 of 56,201
Image retargeting using nonparametric semantic segmentation
Razzaghi, P., & Samavi, S. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2249-y · Cited by 11
Bag of Visual Words: A Soft Clustering Based Exposition
Garg, V., Vempati, S., & Jawahar, C. V. (2011) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2011.15 · Cited by 13
On improving the accuracy of self-calibrated stereo digital image correlation system
Thiruselvam, N. I., & Subramanian, S. J. (2020) · Measurement Science and Technology · doi:10.1088/1361-6501/abae3b · Cited by 11
Qiu, T., Liang, X., Du, Q., et al. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10020068 · Cited by 11
Object identification using mobile devices
Juang, L., Wu, M.-N., & Weng, Z.-Z. (2014) · Measurement · doi:10.1016/j.measurement.2014.01.029 · Cited by 11
Components for Object Detection and Identification
Heisele, B., Riskov, I., & Morgenstern, C. (2006) · Lecture notes in computer science · doi:10.1007/11957959_12 · Cited by 11
Thorax disease diagnosis using deep convolutional neural network
Chen, J., Qi, X., Tervonen, O. A., Sílven, O., Zhao, G., & Pietikäinen, M. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591186 · Cited by 11
VFM: Visual Feedback Model for Robust Object Recognition
Wang, C., & Huang, K. (2015) · Journal of Computer Science and Technology · doi:10.1007/s11390-015-1526-1 · Cited by 12
DSAC - Differentiable RANSAC for Camera LocalizationPreprint
Brachmann, E., Krull, A., Nowozin, S., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05705 · Cited by 11
Indoor Positioning Using PnP Problem on Mobile Phone Images
Kubíčková, H., Jedlička, K., Fiala, R., et al. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9060368 · Cited by 11
Integration of an unmanned vehicle and its application to real-time gas detection and monitoring
Peng, C.-C., & Hsu, C.-Y. (2015) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-tw.2015.7216921 · Cited by 11
Feature Fusion for Efficient Object Classification Using Deep and Shallow Learning
Janani, T. S., & Ramanan, A. (2017) · International Journal of Machine Learning and Computing · doi:10.18178/ijmlc.2017.7.5.633 · Cited by 11
Fully Convolutional Network and Region Proposal for Instance Identification with Egocentric Vision
Portaz, M., Kohl, M., Quénot, G., et al. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.281 · Cited by 11
Real-time side scan image generation and registration framework for AUV route following
King, P., Vardy, A., Vandrish, P., et al. (2012) · IEEE/OES Autonomous Underwater Vehicles · doi:10.1109/auv.2012.6380758 · Cited by 11
Deep Learning–Based Scan-to-BIM Automation and Object Scope Expansion Using a Low-Cost 3D Scan Data
Ma, J. W., Jung, J., & Leite, F. L. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5751 · Cited by 12
Modeling spatial and semantic cues for large-scale near-duplicated image retrieval
Zhang, S. S., Tian, Q. C., Hua, G., Zhou, W., Huang, Q., Li, H., & Gao, W. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.11.003 · Cited by 11
Incremental threshold learning for classifier selection
Pang, Y., Deng, J., & Yuan, Y. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.01.012 · Cited by 11
Maldonado-Ramírez, A., & Torres-Méndez, L. A. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793550 · Cited by 11
Robust unambiguous parametrization of the essential manifold
Subbarao, R., Genç, Y., & Meer, P. (2008) · 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.2008.4587689 · Cited by 11
Arnold, S., & Yamazaki, K. (2017) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2017.8078906 · Cited by 11
Deep Hierarchical Representation from Classifying Logo-405
Hou, S., Lin, J., Zhou, S., et al. (2017) · Complexity · doi:10.1155/2017/3169149 · Cited by 11
Qiao, G., Mi, H., Feng, T., et al. (2016) · Remote Sensing · doi:10.3390/rs8050396 · Cited by 11
Chen, W., Ke, X., & Meng, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-20387-x · Cited by 18
Morales, A., Sánchez-Aparicio, L. J., González‐Aguilera, D., et al. (2017) · Remote Sensing · doi:10.3390/rs9121219 · Cited by 11
A Genetic Programming Method for Scale-Invariant Texture Classification
Ghazouani, H., Barhoumi, W., & Antit, Y. (2020) · Proceedings of the international neural networks society · doi:10.1007/978-3-030-48791-1_47 · Cited by 11
Zumaya, C. C., Catalano, I., & Queralta, J. P. (2024) · Remote Sensing · doi:10.3390/rs16162974 · Cited by 11
Evaluation of SIFT and SURF features in the songket recognition
Willy, D., Noviyanto, A., & Arymurthy, A. M. (2013) · International Conference on Advanced Computer Science and Information Systems · doi:10.1109/icacsis.2013.6761607 · Cited by 11
Robot Localisation Using Natural Landmarks
Anderson, P., Yusmanthia, Y., Hengst, B., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39250-4_12 · Cited by 11
Video matching using DC-image and local features
Bekhet, S., Ahmed, A. A., & Hunter, A. (2013) · Edge Hill University Research Information Repository (Edge Hill University) · Cited by 11
Person re-identification based on deep spatio-temporal features and transfer learning
Wang, S., Zhang, C., Duan, L., et al. (2016) · doi:10.1109/ijcnn.2016.7727397 · Cited by 11
Multiscale and Multidirectional Biomedical Texture Analysis
Depeursinge, A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812133-7.00002-8 · Cited by 19
Vision based global localisation using a 3D environmental model created by a laser range scanner
Ho, N., & Jarvis, R. A. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4650601 · Cited by 11
Image Registration Method for Satellite Image Sensing using Feature based Techniques
Pisupati, S. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/82912020 · Cited by 11
A Review: Personal Identification Based on Palm Vein Infrared Pattern
Soh, S. C., Ibrahim, M. Z., & Yakno, M. (2018) · Journal of Telecommunication Electronic and Computer Engineering (JTEC) · Cited by 11
SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
Wu, X., Zhao, H., Li, S., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19769-7_34 · Cited by 13
Real-Time Human Detection and Tracking Using Quadcopter
George, R. P., & Venugopal, P. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-8575-8_29 · Cited by 11
Similarity metric learning for face verification using sigmoid decision function
Hou, X., Ding, S., Ma, L., et al. (2015) · The Visual Computer · doi:10.1007/s00371-015-1079-x · Cited by 11
Efficient coarse-to-fine spectral rectification for hyperspectral image
Xie, W., Li, Y., Zhou, W., et al. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.11.038 · Cited by 11
Wang, H., Qi, J., Lei, Y.-F., et al. (2021) · Remote Sensing · doi:10.3390/rs13081507 · Cited by 11
Weighted average pooling of deep features for tattoo identification
Nicolás-Díaz, M., Morales-González, A., & Méndez-Vázquez, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12516-9 · Cited by 10
Wang, Y., Pan, Z., & Li, R. (2019) · IEEE Access · doi:10.1109/access.2019.2900730 · Cited by 11
A novel Genetic Algorithms and SURF-Based approach for image retargeting
Oliveira, S. A. F., Neto, A. R. R., & Bezerra, F. N. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.09.015 · Cited by 11
Wang, S., Zhu, Z., Ma, T., & Fan, J. (2024) · Journal of Testing and Evaluation · doi:10.1520/jte20230485 · Cited by 12
“One such technique is the scale-invariant feature transformation (SIFT) algo-rithm, which allows for fast and accurate stereo matching in these scenarios 146;147 .”
MethodRIFormer+: Rethinking Rotation-Invariant Feature Learning in Transformer
Song, C., Zhang, Y., Ma, M., & Mei, S. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3607728 · Cited by 19
Automatic Matching of Multispectral Images Based on Nonlinear Diffusion of Image Structures
Li, R., Zhao, H., Zhang, X., et al. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3043379 · Cited by 10
DFAS-YOLO: Dual Feature-Aware Sampling for Small-Object Detection in Remote Sensing Images
Liu, X., Zhou, S., Ma, J., Sun, Y., Zhang, J., & Zuo, H. (2025) · Remote Sensing · doi:10.3390/rs17203476 · Cited by 14
Accelerating Image-Domain-Warping Virtual View Synthesis on GPGPU
Wang, R., Luo, J., Jiang, X., et al. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2654120 · Cited by 10
A Novel Face Inpainting Approach Based on Guided Deep Learning
Salem, N. M., Mahdi, H. M. K., & Abbas, H. M. (2021) · International Conference on Communications, Signal Processing, and their Applications (ICCSPA) · doi:10.1109/iccspa49915.2021.9385734 · Cited by 10
Local Binary Patterns as Texture Descriptors for User Attitude Recognition
Eisa, M., Elgamal, A. F., Ghoneim, R., et al. (2010) · Cited by 10
A Local Feature Descriptor Based on SIFT for 3D Pollen Image Recognition
Wang, Z., Bao, W., Lin, D., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2948088 · Cited by 10
Monteiro, S. T., Nieto, J. I., Murphy, R. J., et al. (2013) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2013.6722997 · Cited by 10
Robust and accurate estimation of multiple motions for whole-image super-resolution
Tanaka, M., Yaguchi, Y., & Okutomi, M. (2008) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2008.4711838 · Cited by 10
A novel region-based expansion rate obstacle detection method for MAVs using a fisheye camera
Badrloo, S., Varshosaz, M., Pirasteh, S., et al. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102739 · Cited by 10
Scale-Adaptive KCF Mixed with Deep Feature for Pedestrian Tracking
Zhou, Y., Yang, W., & Shen, Y. (2021) · Electronics · doi:10.3390/electronics10050536 · Cited by 10
PhotoSketch: a photocentric urban 3D modeling system
Wolberg, G., & Zokai, S. (2017) · The Visual Computer · doi:10.1007/s00371-017-1365-x · Cited by 10
Vietnamese Food Recognition System Using Convolutional Neural Networks Based Features
Ung, H. T., Dang, T., Thai, V.-P., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63007-2_37 · Cited by 10
Simultaneous Localization and Mapping Related Datasets: A Comprehensive SurveyPreprint
Liu, Y., Fu, Y., Chen, F., Goossens, B., Tao, W., & Zhao, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04036 · Cited by 24
Simultaneous color consistency and depth map estimation for radiometrically varying stereo images
Heo, Y. S., Lee, K. M., & Lee, S. U. (2009) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459396 · Cited by 10
Swathi, R., & Srinivas, A. (2019) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-019-01063-w · Cited by 10
Latent‐lSVM classification of very high‐dimensional and large‐scale multi‐class datasets
Do, T., & Poulet, F. (2017) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4224 · Cited by 11
Ott, F., Heublein, L., Rügamer, D., Bischl, B., & Mutschler, C. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104256 · Cited by 12
A Stronger Stitching Algorithm for Fisheye Images Based on Deblurring and Registration
Hao, J., Xie, J., Zhang, J., et al. (2023) · IEEE Sensors Letters · doi:10.1109/lsens.2023.3320060 · Cited by 10
Branching Path Following for Graph Matching
Wang, T., Ling, H., Lang, C., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_32 · Cited by 10
SSR-Net: A Spatial Structural Relation Network for Vehicle Re-identification
Xu, Z., Wei, L., Lang, C., Feng, S., Wang, T., Borş, A. G., & Liu, H. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3578578 · Cited by 10
Deep Learning in Object Detection
Pang, Y., & Cao, J. (2019) · Springer eBooks · doi:10.1007/978-981-10-5152-4_2 · Cited by 12
Cooperative anchoring : sharing information about objects in multi-robot systems
LeBlanc, K. A. (2010) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 10
Saikia, S., Fernández-Robles, L., Alegre, E., & Fidalgo, E. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05955-2 · Cited by 9
Improving Search and Rescue Using Contextual Information
Calisi, D., Iocchi, L., Nardi, D., Randelli, G., & Ziparo, V. A. (2009) · Advanced Robotics · doi:10.1163/156855309x452539 · Cited by 10
Aminzadeh, A., & Atashgah, M. A. A. (2018) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2018.170075 · Cited by 10
High-order information for robust iris recognition under less controlled conditions
Yang, G., Zeng, H., Li, P., & Lei Zhang, A. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351665 · Cited by 10
A geometric correction method for projected images using SIFT feature points
Takahashi, T., Norihito, N., Aoki, T., et al. (2008) · doi:10.1145/1394622.1394628 · Cited by 10
Super-Resolution Mosaicing of Unmanned Aircraft System (UAS) Surveillance Video Frames
Ghosh, D., Kaabouch, N., & Semke, W. H. (2013) · Cited by 10
Song, F. D., Li, M., Yang, Y., et al. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1516051 · Cited by 10
Does Removing Pooling Layers from Convolutional Neural Networks Improve Results?
dos Santos, C. F. G., Moreira, T. P., Colombo, D., & Papa, J. P. (2020) · SN Computer Science · doi:10.1007/s42979-020-00295-9 · Cited by 11
Bai, Y., Yang, K., Yu, W., Ma, W., & Zhao, T. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.4740 · Cited by 11
3D HMM-based Facial Expression Recognition using Histogram of Oriented Optical Flow
Kung, S. H., Zohdy, M. A., & Bouchaffra, D. (2015) · Transactions on Machine Learning and Artificial Intelligence · doi:10.14738/tmlai.36.1661 · Cited by 10
[Poster] MOBIL: A moments based local binary descriptor
Bellarbi, A., Otmane, S., Zenati, N., et al. (2014) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2014.6948435 · Cited by 10
Wang, X., Zhan, Z., & Heipke, C. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-1-w1-191-2017 · Cited by 10
Visual features for ego-centric activity recognition
Tadesse, G. A., & Cavallaro, A. (2018) · doi:10.1145/3211960.3211978 · Cited by 10
A simple yet effective image stitching with computational suture zone
Zhang, J., Gao, Y., Xu, Y. T., et al. (2022) · The Visual Computer · doi:10.1007/s00371-022-02637-5 · Cited by 10
Image retrieval using unsupervised prompt learning and regional attention
Zhang, B.-J., Liu, G., & Li, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122913 · Cited by 11
Local similarity refinement of shape‐preserved warping for parallax‐tolerant image stitching
Li, W., Jin, C., Liu, M., et al. (2017) · IET Image Processing · doi:10.1049/iet-ipr.2017.0037 · Cited by 10
Hand posture recognition using SURF with adaptive boosting
Yao, Y., & Li, C. (2012) · Warwick Research Archive Portal (University of Warwick) · Cited by 10
Low Complexity Image Quality Measures for Dietary Assessment Using Mobile Devices
Xu, C., Khanna, N., Boushey, C. J., & Delp, E. J. (2011) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2011.64 · Cited by 10
Auto-adaptive harris corner detection algorithm based on block processing
Shen, S., Zhang, X., & Heng, W. (2010) · International Symposium on Signals, Systems and Electronics · doi:10.1109/issse.2010.5638297 · Cited by 10
From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration
Qian, Z., Han, R., Feng, W., & Wang, S. (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/cvpr52733.2024.00088 · Cited by 10
A Multi-strategy Region Proposal Network
Chen, Y., Li, Y., Wang, G., & Xu, Q. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.06.043 · Cited by 11
Rwt-Slam: Robust Visual Slam for Weakly Textured EnvironmentsPreprint
Peng, Q., Xiang, Z., Zhao, X., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4514612 · Cited by 10
3D Reconstruction Challenges Using Structure-From-Motion
Koutsoudis, A., Ioannakis, G., Arnaoutoglou, F., et al. (2020) · Advances in religious and cultural studies (ARCS) book series · doi:10.4018/978-1-7998-2871-6.ch007 · Cited by 10
A hardware design of optimized ORB algorithm with reduced hardware cost
Lee, K.-Y., Byun, K., & ETRI (2013) · Advanced Science and Technology Letters · doi:10.14257/astl.2013.43.11 · Cited by 10
Overview of the MPEG CDVS Standard
Duan, L., Huang, T., & Gao, W. (2015) · DCC · doi:10.1109/dcc.2015.72 · Cited by 10
A New Approach to Robust Estimation of Parametric Structures
Yang, X. S., Meer, P., & Meer, J. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2994190 · Cited by 10
Fast feature matching based on r ‐nearest k ‐means searching
Wang, K., Zhu, N., Cheng, Y., et al. (2018) · CAAI Transactions on Intelligence Technology · doi:10.1049/trit.2018.1041 · Cited by 10
A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation
Zhang, Z., Shen, Z., Liu, J., Shu, J., & Zhang, H. (2023) · Sensors · doi:10.3390/s24010003 · Cited by 12
Measurement of Soil Tillage Using UAV High-Resolution 3D Data
Rebelo, C., & Nascimento, J. (2021) · Remote Sensing · doi:10.3390/rs13214336 · Cited by 10
Learnable Graph Matching: A Practical Paradigm for Data Association
He, J., Huang, Z., Wang, N., & Zhang, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3362401 · Cited by 10
Chang, S., Kopp, M., Ghamisi, P., & Du, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3424532 · Cited by 13
Counting people from above: Airborne video based crowd analysisPreprint
Perko, R., Schnabel, T., Fritz, G., Almér, A., & Paletta, L. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.6213 · Cited by 10
Frame Stitching in Indoor Environment Using Drone Captured Images
Ramaswamy, A., Gubbi, J., Raj, R., et al. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451671 · Cited by 10
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
