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 19,201–19,300 of 56,201
Texture representations using subspace embeddings
Yang, X., & Tian, Y. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.03.009 · Cited by 12
Sun, Q., Diao, M., Li, Y., et al. (2017) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-11-2016-0280 · Cited by 12
Video2Action: Reducing Human Interactions in Action Annotation of App Tutorial Videos
Feng, S., Chen, C., & Xing, Z. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606778 · Cited by 12
Self-Supervised SAR Image Registration With SAR-Superpoint and Transformation Aggregation
Zou, B., Li, H., & Zhang, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3231904 · Cited by 13
Singh, A., & Ohgami, R. S. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_56_18 · Cited by 12
Removal and injection of keypoints for SIFT-based copy-move counter-forensics
Amerini, I., Barni, M., Caldelli, R., et al. (2013) · EURASIP Journal on Information Security · doi:10.1186/1687-417x-2013-8 · Cited by 12
Jadidi, H., Ravanshadnia, M., Hosseinalipour, M., et al. (2015) · Visualization in Engineering · doi:10.1186/s40327-014-0016-9 · Cited by 12
Self-Similarity and Symmetry With SIFT for Multi-Modal Image Registration
Lv, G. (2019) · IEEE Access · doi:10.1109/access.2019.2912199 · Cited by 11
Campos, Y., Sossa, H., & Pájares, G. (2016) · Precision Agriculture · doi:10.1007/s11119-016-9483-4 · Cited by 12
Feature Detectors and Descriptors Generations with Numerous Images and Video Applications
Dey, N., Ashour, A. S., & Hassanien, A. E. (2016) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-5225-1025-3.ch003 · Cited by 12
Sgorbissa, A., & Verda, D. (2013) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2013.06.006 · Cited by 12
Feature Fusion Based Deep Spatiotemporal Model for Violence Detection in Videos
Asad, M., Yang, Z., Khan, Z., Yang, J., & He, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36708-4_33 · Cited by 14
Correspondence Propagation with Weak Priors
Wang, H., Yan, S., Liu, J., et al. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2006602 · Cited by 12
Hybrid visual natural landmark–based localization for indoor mobile robots
Zhang, X., Zhu, S., Wang, Z., et al. (2018) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881418810143 · Cited by 12
Invariant Gabor-based interest points detector under geometric transformation
Yussof, W. N. J. H. W., & Hitam, M. S. (2013) · Digital Signal Processing · doi:10.1016/j.dsp.2013.10.011 · Cited by 12
Intelligent unmanned anti-theft system using network camera
Kim, J., Yeom, D. H., Joo, Y. H., et al. (2010) · International Journal of Control Automation and Systems · doi:10.1007/s12555-010-0505-0 · Cited by 12
Order Preserving Sparse Coding
Ni, B., Moulin, P., & Yan, S. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2362935 · Cited by 12
Liang, P., Shen, D., Blasch, E., et al. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2015967 · Cited by 12
Efficient Feature Distribution for Object Matching in Visual-Sensor Networks
Sulić, V., Perš, J., Kristan, M., et al. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2133330 · Cited by 12
Rani, K., Supriya, P., & Sarath, T. V. (2017) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2017.8282671 · Cited by 12
Detail-Preserving Mesh Unfolding for Nonrigid Shape Retrieval
Sahillioğlu, Y., & Kavan, L. (2016) · ACM Transactions on Graphics · doi:10.1145/2893477 · Cited by 12
An experimental tattoo de-identification system for privacy protection in still images
Marčetić, D., Ribarič, S., Štruc, V., et al. (2014) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2014.6859766 · Cited by 12
The Study of Preprocessing and Postprocessing Techniques of Image Stitching
Vaidya, O., & Gandhe, S. T. (2018) · 2018 International Conference On Advances in Communication and Computing Technology (ICACCT) · doi:10.1109/icacct.2018.8529642 · Cited by 12
MDN-VO: Estimating Visual Odometry with Confidence
Kaygusuz, N., Méndez, O., & Bowden, R. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636827 · Cited by 15
Bouma, H., de Boer, M., Kruithof, M. C., ter Haar, F. B., Fischer, N. M., Hagendoorn, L., Joosten, B., & Raaijmakers, S. A. (2017) · Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies · doi:10.1117/12.2277970 · Cited by 12
Tang, H., Kang, F., Geng, Z., et al. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103683 · Cited by 12
A semantic region descriptor for local feature based image categorization
Li, T., & Kweon, I.-S. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4517864 · Cited by 12
A learning-based logo recognition algorithm using SIFT and efficient correspondence matching
Xia, L., Qi, F., & Zhou, Q. (2008) · International Conference on Information and Automation · doi:10.1109/icinfa.2008.4608292 · Cited by 12
Fundus Image Registration Technique Based on Local Feature of Retinal Vessels
Ramli, R., Hasikin, K. B., Idris, M. Y. I., et al. (2021) · Applied Sciences · doi:10.3390/app112311201 · Cited by 12
MedRegNet: unsupervised multimodal retinal-image registration with GANs and ranking loss
Santarossa, M., Kilic, A., von der Burchard, C., et al. (2022) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2607653 · Cited by 12
Research of shoeprint image stream retrival algorithm with scale-invariance feature transform
Li, Z., Wei, C., Li, Y., et al. (2011) · doi:10.1109/icmt.2011.6002147 · Cited by 12
Feng, Y., Welsh, D., McDonald, K., et al. (2015) · Acta Oncologica · doi:10.3109/0284186x.2015.1063782 · Cited by 12
Detection and Analysis of Sow Targets Based on Image Vision
Lei, K., Zong, C., Yang, T., Peng, S., Zhu, P., Wang, H., Teng, G., & Du, X. (2022) · Agriculture · doi:10.3390/agriculture12010073 · Cited by 13
Object Counting using KAZE Features Under Different Lighting Conditions for Inventory Management
Sharma, T., Jain, A., Verma, N. K., et al. (2019) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr47015.2019.9174578 · Cited by 12
Martedi, S. E., Uchiyama, H., Enriquez, G., Saitô, H., Miyashita, T., & Hara, T. (2010) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2010.5643552 · Cited by 12
A robust watermark authentication technique based on Weber’s descriptor
Walia, E., & Suneja, A. (2012) · Signal Image and Video Processing · doi:10.1007/s11760-012-0312-6 · Cited by 12
3D Morphology Measurement for Blastocyst Evaluation From “All Angles”
Shan, G., Dai, C., Liu, H., et al. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3232068 · Cited by 12
Traffic sign recognition by Bags of features
Ohgushi, K., & Hamada, N. (2009) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2009.5395921 · Cited by 12
Light Field Stitching for Parallax Tolerance
Wang, P., Jin, X., Li, C., et al. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451032 · Cited by 12
Wang, X., Zhang, K., Zhang, X., Li, S., & Yan, J. (2021) · Infrared Physics & Technology · doi:10.1016/j.infrared.2021.103790 · Cited by 12
Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data
Liu, Y., Jin, R., Pantofaru, C., et al. (2007) · 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.2007.383270 · Cited by 12
Training Data Extraction and Object Detection in Surveillance Scenario
Wilkowski, A., Stefańczyk, M., & Kasprzak, W. (2020) · Sensors · doi:10.3390/s20092689 · Cited by 14
Nakamura, T., Nagai, T., & Iwahashi, N. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385502 · Cited by 11
Self-Supervised Feature Learning for Long-Term Metric Visual Localization
Chen, Y., & Barfoot, T. D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3227866 · Cited by 13
Embedded double matching of local descriptors for a fast automatic recognition of real-world objects
Alqaisi, T., Gledhill, D., & Olszewska, J. I. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467377 · Cited by 12
CNN-SIFT Consecutive Searching and Matching for Wine Label Retrieval
Li, X., Yang, J., & Ma, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26763-6_24 · Cited by 12
Decoupling photometry and geometry in dense variational camera calibration
Aubry, M., Kolev, K., Goldlüecke, B., & Cremers, D. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126396 · Cited by 12
Feature Combination via Clustering
Hou, J., Gao, H., & Li, X. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2645883 · Cited by 11
Cross domain 2D-3D descriptor matching for unconstrained 6-DOF pose estimation
Nadeem, U., Bennamoun, M., Togneri, R. B., Sohel, F. A., Rekavandi, A. M., & Boussaïd, F. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109655 · Cited by 13
Siozios, K., Diamantopoulos, D., Kostavelis, I., et al. (2011) · doi:10.1109/recosoc.2011.5981524 · Cited by 12
Optimal texture image reconstruction method for improvement of SAR image matching
Ghannadi, M. A., Saadatseresht, M., Izadi, M., et al. (2020) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2020.0058 · Cited by 12
A Scale Invariant Local Image Descriptor for Visual Homing
Vardy, A., & Oppacher, F. (2005) · Lecture notes in computer science · doi:10.1007/11521082_22 · Cited by 12
MDMASNet: A dual-task interactive semi-supervised remote sensing image segmentation method
Zhang, L., Yang, Z., Zhou, G., et al. (2023) · Signal Processing · doi:10.1016/j.sigpro.2023.109152 · Cited by 12
Interactive active contour with kernel descriptor
Li, H., Gong, M., Miao, Q., et al. (2018) · Information Sciences · doi:10.1016/j.ins.2018.03.016 · Cited by 12
Wang, M., Xie, G., Zhang, Z., Wang, Y., Xiang, S., & Pi, Y. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3170488 · Cited by 12
Äniruddha Acharya, K., & Babu, R. V. (2013) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2013.6776198 · Cited by 12
Online structural learning with dense samples and a weighting kernel
Yu, X., & Yu, Q. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.05.017 · Cited by 12
Multimodal Perception Integrating Point Cloud and Light Field for Ship Autonomous Driving
Cong, R., Sheng, H., Zhao, M., Yang, D., Wang, T., Chen, R., & Shen, J. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3381652 · Cited by 15
“3, we further extract SIFT [45] within each matched local object region among view images for fine-grained key point matching.”
MethodHPointLoc: Point-Based Indoor Place Recognition Using Synthetic RGB-D Images
Yudin, D. A., Solomentsev, Y., Musaev, R., Staroverov, A., & Panov, A. I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30111-7_40 · Cited by 12
3D Data Acquisition and Registration Using Two Opposing Kinects
Soleimani, V., Mirmehdi, M., Damen, D., et al. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.21 · Cited by 12
Anomaly Detection of Trackside Equipment Based on GPS and Image Matching
Gao, L., Jiu, Y., Wei, X., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2966783 · Cited by 12
Biomedical CBIR using “bag of keypoints” in a modified inverted index
Rahman, M. M., Antani, S., & Thoma, G. R. (2011) · doi:10.1109/cbms.2011.5999136 · Cited by 12
Remote inspection with multi-copters, radiological sensors and SLAM techniques
Carvalho, H., Vale, A., Marques, R., Ventura, R., Brouwer, Y., & Gonçalves, B. (2018) · EPJ Web of Conferences · doi:10.1051/epjconf/201817007014 · Cited by 12
Global-Scale Location Prediction for Social Images Using Geo-Visual Ranking
Li, X., Larson, M., & Hanjalić, A. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2413351 · Cited by 12
Classification approach for automatic laparoscopic video database organization
Twinanda, A. P., Marescaux, J., de Mathelin, M., et al. (2015) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1183-4 · Cited by 12
Semi-automatic Image Annotation
Moehrmann, J., & Heidemann, G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40246-3_33 · Cited by 12
Multi-scale local pattern co-occurrence matrix for textural image classification
Sun, X., Wang, J., Chen, R., et al. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252374 · Cited by 12
Feature matching error analysis and modeling for consistent estimation in vision‐aided navigation
Yang, C., Vadlamani, A., Soloviev, A., Veth, M. J., & Taylor, C. N. (2018) · NAVIGATION Journal of the Institute of Navigation · doi:10.1002/navi.265 · Cited by 12
Towards real time 3D tracking and reconstruction on a GPU using Monte Carlo simulations
Sánchez, J. R., Álvarez, H., & Borro, D. (2010) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2010.5643568 · Cited by 12
Global Context Descriptors for SURF and MSER Feature Descriptors
Carmichael, G., Laganière, R., & Bose, P. K. (2010) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2010.47 · Cited by 12
Lightweight Yolov4 Target Detection Algorithm Fused with ECA Mechanism
Wang, C., Zhou, Y., & Li, J. (2022) · Processes · doi:10.3390/pr10071285 · Cited by 12
Practical, Fast and Robust Point Cloud Registration for Scene Stitching and Object Localization
Sun, L. (2022) · IEEE Access · doi:10.1109/access.2021.3140105 · Cited by 12
Local Binary Pattern Circuit Generator With Adjustable Parameters for Feature Extraction
Hu, M., Ng, K. S., Chen, P., et al. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2758387 · Cited by 12
Ontology Based Object Learning and Recognition
Maillot, N. E., & THONNAT, M. (2005) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 12
Fast image stitching method for handling dynamic object problems in Panoramic Images
Abdukholikov, M., & Whangbo, T. K. (2017) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2017.11.013 · Cited by 12
Learning Spatial-context-aware Global Visual Feature Representation for Instance Image Retrieval
Zhang, Z., Wang, L., Zhou, L., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01033 · Cited by 12
Object Matching in Distributed Video Surveillance Systems by LDA-Based Appearance Descriptors
Presti, L. L., Sclaroff, S., & Cascia, M. L. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04146-4_59 · Cited by 12
Devassy, B. M., Yayilgan, S. Y., & Hardeberg, J. Y. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-01054-6_10 · Cited by 12
May, S., Koch, P., Koch, R., et al. (2014) · Eurographics · doi:10.2312/vmv.20141281 · Cited by 12
Domain-adaptive modules for stereo matching network
Ling, Z., Yang, K., Li, J., Zhang, Y., Gao, X., Luo, L., & Xie, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.004 · Cited by 11
Object detection using shape features
Gupta, S., & Singh, Y. J. (2014) · IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) · doi:10.1109/iccic.2014.7238445 · Cited by 12
Image-based localization using LSTMs for structured feature correlationPreprint
Walch, F., Hazırbaş, C., Leal-Taixé, L., Sattler, T., Hilsenbeck, S., & Cremers, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07890 · Cited by 21
Panoramic view creation using invariant momentsand SURF features
Karthik, R., AnnisFathima, A., & Vaidehi, V. (2013) · International Conference on Recent Trends in Information Technology · doi:10.1109/icrtit.2013.6844233 · Cited by 12
Distribution Entropy Boosted VLAD for Image Retrieval
Zhou, Q., Wang, C., Liu, P., Li, Q., Wang, Y., & Chen, S. (2016) · Entropy · doi:10.3390/e18080311 · Cited by 10
Mohan, A., Kaseb, A. S., Lu, Y.-H., et al. (2016) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2016.0040 · Cited by 12
Multi-Layered Conceptual Spatial Mapping for Autonomous Mobile Robots.
Zender, H., & Kruijff, G.-J. M. (2007) · Cited by 12
A four-frames differencing technique for moving objects detection in wide area surveillance
Abdelli, A., & Choi, H. (2017) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2017.7881701 · Cited by 12
LAPMv2: An improved tool for underwater large-area photo-mosaicking
Marcon, Y. (2014) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans.2014.7003185 · Cited by 12
Markerless Pose Tracking for Augmented Reality
Yuan, C. (2006) · Lecture notes in computer science · doi:10.1007/11919476_72 · Cited by 12
AMW-YOLOv8n: Road Scene Object Detection Based on an Improved YOLOv8
Wu, D., Fang, C., Zheng, X., et al. (2024) · Electronics · doi:10.3390/electronics13204121 · Cited by 12
Traffic Flow Monitoring in Crowded Cities.
Quinn, J. A., & Nakibuule, R. (2010) · Cited by 12
A Low Complexity Interest Point Detector
Chen, J., Duan, L., Gao, F., et al. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2354237 · Cited by 12
Quadtree decomposition based extended vector space model for image retrieval
Ramanathan, V., Mishra, S., & Mitra, P. (2011) · Proceedings · doi:10.1109/wacv.2011.5711495 · Cited by 12
MessyTable: Instance Association in Multiple Camera Views
Cai, Z., Zhang, J., Ren, D., Yu, C., Zhao, H., Yi, S., Yeo, C. K., & Loy, C. C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_1 · Cited by 13
Inostroza, F., Díaz, S., Cárdenas, J., et al. (2017) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2017.09.006 · Cited by 12
Robust local feature descriptor for multisource remote sensing image registration
Chen, S., Li, X., Yang, H., et al. (2018) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.12.015005 · Cited by 12
Liu, T., & Liu, Y. (2021) · Journal of Advanced Transportation · doi:10.1155/2021/8153474 · Cited by 13
AUTOMATIC GEO-REGISTRATION FOR PORT SURVEILLANCE
Cao, X., Wu, L. Y., Rasheed, Z., et al. (2010) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001410008093 · Cited by 12
Designing mobile augmented reality art applications
Coulton, P., Smith, R., Murphy, E., et al. (2014) · doi:10.1145/2676467.2676490 · Cited by 12
Xu, C., Kuipers, B. J., & Murarka, A. (2009) · doi:10.1109/iccvw.2009.5457639 · Cited by 12
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