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 10,701–10,800 of 56,200
Khan, S. D., & Basalamah, S. M. (2023) · Remote Sensing · doi:10.3390/rs15082208 · Cited by 29
Loop-Closure Detection Using Local Relative Orientation Matching
Ma, J., Ye, X., Zhou, H., Mei, X., & Fan, F. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3074520 · Cited by 29
Creating map-based storyboards for browsing tour videos
Pongnumkul, S., Wang, J., & Cohen, M. (2008) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1449715.1449720 · Cited by 29
Improved ORB Algorithm Using Three-Patch Method and Local Gray Difference
Ma, C., Hu, X., Xiao, J., Du, H., & Zhang, G. (2020) · Sensors · doi:10.3390/s20040975 · Cited by 29
Boosting image classification through semantic attention filtering strategies
Fidalgo, E., Alegre, E., González‐Castro, V., et al. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.06.033 · Cited by 29
TrTr-CMR: Cross-Modal Reasoning Dual Transformer for Remote Sensing Image Captioning
Wu, Y., Li, L., Jiao, L., Liu, F., Liu, X., & Yang, S. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3475633 · Cited by 34
Content-based image retrieval using a mobile device as a novel interface
Hare, J. S., & Lewis, P. H. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.588979 · Cited by 29
Determining Map Quality through an Image Similarity Metric
Varsadan, I., Birk, A., & Pfingsthorn, M. F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02921-9_31 · Cited by 29
Deep learning-guided surface characterization for autonomous hydrogen lithography
Rashidi, M., Croshaw, J., Mastel, K., Tamura, M., Hosseinzadeh, H., & Wolkow, R. A. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab6d5e · Cited by 29
K-Means Clustering Guided Generative Adversarial Networks for SAR-Optical Image Matching
Du, W.-L., Zhou, Y. Z., Zhao, J., & Tian, X. (2020) · IEEE Access · doi:10.1109/access.2020.3042213 · Cited by 29
Single-Camera Closed-Form Real-Time Needle Tracking for Ultrasound-Guided Needle Insertion
Najafi, M., Abolmaesumi, P., & Rohling, R. N. (2015) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2015.05.016 · Cited by 29
HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNs
Han, X., Lei, J., & Chen, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_1 · Cited by 29
Detection of Waste Containers Using Computer Vision
Valente, M., Silva, H., Caldeira, J. M. L. P., et al. (2019) · Applied System Innovation · doi:10.3390/asi2010011 · Cited by 29
Acquisition system for dense lightfield of large scenes
Ziegler, M. D., Veld, R. O. H., Keinert, J., et al. (2017) · 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video · doi:10.1109/3dtv.2017.8280412 · Cited by 29
Multiple Plane Detection in Image Pairs Using J-Linkage
Fouhey, D. F., Scharstein, D., & Briggs, A. J. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.91 · Cited by 29
A Survey of FPGA-Based Vision Systems for Autonomous Cars
Castells‐Rufas, D., Ngo, V., Borrego-Carazo, J., Codina, M., Sanchez, C., Gil, D., & Carrabina, J. (2022) · IEEE Access · doi:10.1109/access.2022.3230282 · Cited by 34
Amiri, M., & Rabiee, H. R. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2009.12.014 · Cited by 29
A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection
Hamidi, M., Haziti, M. E., Cherifi, H., & Hassouni, M. E. (2021) · Journal of Imaging · doi:10.3390/jimaging7100218 · Cited by 30
Boosting image retrieval through aggregating search results based on visual annotations
Olivares, X., Ciaramita, M., & van Zwol, R. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459386 · Cited by 29
A Fast 2D Shape Recovery Approach by Fusing Features and Appearance
Zhu, J., Lyu, M. R.-T., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.151 · Cited by 29
Automatic landmark location with a Combined Active Shape Model
Zhou, D., Petrovska‐Delacrétaz, D., & Dorizzi, B. (2009) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2009.5339037 · Cited by 29
High speed human action recognition using a photonic reservoir computer
Picco, E., Antonik, P., & Massar, S. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.06.014 · Cited by 30
Efficiently searching for similar images
Grauman, K. L. (2010) · Communications of the ACM · doi:10.1145/1743546.1743570 · Cited by 28
Kolzenburg, S., Favalli, M., Fornaciai, A., et al. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2587798 · Cited by 29
Retinal image registration through simultaneous camera pose and eye shape estimation
Hernandez-Matas, C., Zabulis, X., & Argyros, A. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591421 · Cited by 28
Hu, M. Q., Yang, C., Zheng, Y., Wang, X., He, L., & Ren, F. (2019) · IEEE Access · doi:10.1109/access.2019.2936976 · Cited by 29
Convolutional neural net bagging for online visual tracking
Li, H., Li, Y., & Porikli, F. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.07.002 · Cited by 28
Dual graph regularized compact feature representation for unsupervised feature selection
Li, S., Tang, C., Liu, X., Liu, Y., & Chen, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.060 · Cited by 29
Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram
Al-Aidid, S., & Pamungkas, D. S. (2018) · Jurnal Rekayasa Elektrika · doi:10.17529/jre.v14i1.9799 · Cited by 29
Trajectory-Based Visual Localization in Underwater Surveying Missions
Burguera, A., Bonin‐Font, F., & Oliver, G. (2015) · Sensors · doi:10.3390/s150101708 · Cited by 29
Capformer: Pure Transformer for Remote Sensing Image Caption
Wang, J., Chen, Z., Ma, A., & Zhong, Y. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9883199 · Cited by 30
Elhashash, M., & Qin, R. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.03.018 · Cited by 30
Machine Vision System for Automatic Weeding Strategy using Image Processing Technique
Ghazali, K. H., Mustafa, M. M., & Hussain, A. (2008) · Cited by 29
Decelle, J., Kayal, E., Bigeard, E., et al. (2022) · The ISME Journal · doi:10.1038/s41396-022-01274-z · Cited by 29
Robust 3/6 DoF self-localization system with selective map update for mobile robot platforms
Costa, C. M., Sobreira, H., Sousa, A. J., et al. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2015.09.030 · Cited by 29
Relative velocity measurement using low cost single camera-based stereo vision system
Murmu, N., Chakraborty, B., & Nandi, D. (2019) · Measurement · doi:10.1016/j.measurement.2019.04.006 · Cited by 29
Evaluating 3D spatial pyramids for classifying 3D shapes
López-Sastre, R. J., García-Fuertes, A., Redondo-Cabrera, C., et al. (2013) · Computers & Graphics · doi:10.1016/j.cag.2013.04.003 · Cited by 29
Liu, A.-A., Lu, Y., Chen, M., et al. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2721438 · Cited by 29
Vector-Valued Multi-View Semi-Supervsed Learning for Multi-Label Image Classification
Luo, Y., Tao, D., Xu, C., et al. (2013) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v27i1.8589 · Cited by 29
Semantic Labeling of Places with Mobile Robots
Mozos, Ó. M. (2010) · Springer tracts in advanced robotics · doi:10.1007/978-3-642-11210-2 · Cited by 30
Efficient Person Search: An Anchor-Free Approach
Yan, Y., Li, J., Qin, J., Zheng, P., Liao, S., & Yang, X. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01772-3 · Cited by 34
Computerized paleography: Tools for historical manuscripts
Wolf, L., Potikha, L., Dershowitz, N., Shweka, R., & Choueka, Y. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116481 · Cited by 32
Joint Attention in Driver-Pedestrian Interaction: from Theory to PracticePreprint
Rasouli, A., & Tsotsos, J. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02522 · Cited by 41
Discriminative Semantic Subspace Analysis for Relevance Feedback
Zhang, L., Shum, H. P. H., & Shao, L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2516947 · Cited by 29
Adversarial Similarity Metric Learning for Kinship Verification
Wei, Z., Xu, M., Geng, L., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2929939 · Cited by 29
Egg size classification on Android mobile devices using image processing and machine learning
Waranusast, R., Intayod, P., & Makhod, D. (2016) · ICT International Student Project Conference (ICT-ISPC) · doi:10.1109/ict-ispc.2016.7519263 · Cited by 29
Local region detector + CNN based landmarks for practical place recognition in changing environments
Neubert, P., & Protzel, P. (2015) · European Conference on Mobile Robots · doi:10.1109/ecmr.2015.7324051 · Cited by 29
Intensity Rank Estimation of Facial Expressions Based on a Single Image
Chang, K.-Y., Chen, C., & Hung, Y. (2013) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2013.538 · Cited by 29
On-line multi-modal speaker diarization
Noulas, A. K., & Kröse, B. (2007) · International Conference on Multimodal Interfaces · doi:10.1145/1322192.1322254 · Cited by 29
Multi-level attention model for person re-identification
Yan, Y., Ni, B., Liu, J., & Yang, X. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.08.024 · Cited by 27
Research on improved algorithm for helmet detection based on YOLOv5
Shan, C., Liu, H., & Yu, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-45383-x · Cited by 33
Robust FEC-CNN: A High Accuracy Facial Landmark Detection System
He, Z., Zhang, J., Kan, M., et al. (2017) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.255 · Cited by 29
Finger knuckle print recognition based on SURF algorithm
Zhu, L. (2011) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2011.6019781 · Cited by 29
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation
Gupta, K., Petersson, L., & Hartley, R. I. (2019) · doi:10.1109/iccvw.2019.00337 · Cited by 29
Khan, Z. H., & Gu, I. Y. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2013.2237900 · Cited by 29
A passive camera based determination of a non-cooperative and unknown satellite's pose and shape
Volpe, R., Palmerini, G. B., & Sabatini, M. (2018) · Acta Astronautica · doi:10.1016/j.actaastro.2018.06.061 · Cited by 29
Zhao, Q., Ma, Y., Lyu, S., & Chen, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3126770 · Cited by 31
Fast Object Retrieval Using Direct Spatial Matching
Zhong, Z., Zhu, J., & Hoi, S. C. H. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2446201 · Cited by 29
Detect and identify earth rock embankment leakage based on UAV visible and infrared images
Su, H., Ma, J., Zhou, R., et al. (2022) · Infrared Physics & Technology · doi:10.1016/j.infrared.2022.104105 · Cited by 29
Scene classification using multi-resolution low-level feature combination
Zhou, L. J., Zhou, Z., & Hu, D. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.06.023 · Cited by 29
Object detection by color histogram-based fuzzy classifier with support vector learning
Juang, C.-F., Sun, W.-K., & Chen, G. (2009) · Neurocomputing · doi:10.1016/j.neucom.2008.11.016 · Cited by 32
Down Syndrome Face Recognition: A Review
Agbolade, O., Nazri, A., Yaakob, R., Ghani, A., & Cheah, Y. K. (2020) · Symmetry · doi:10.3390/sym12071182 · Cited by 29
Farsi/Arabic optical font recognition using SIFT features
Zahedi, M., & Eslami, S. (2011) · Procedia Computer Science · doi:10.1016/j.procs.2010.12.173 · Cited by 29
A multipurpose autonomous robot for target recognition in unknown environments
Avola, D., Foresti, G. L., Cinque, L., et al. (2016) · 2022 IEEE 20th International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin.2016.7819262 · Cited by 29
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose EstimationPreprint
Jain, A., Tompson, J., LeCun, Y., & Bregler, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.7963 · Cited by 34
Multi-View Learning for Vehicle Re-Identification
Lin, W., Li, Y., Yang, X., Peng, P., & Xing, J. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00148 · Cited by 35
A Comparative Analysis of RootSIFT and SIFT Methods for Drowsy Features Extraction
Vijayan, V., & Pushpalatha, K. P. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.04.046 · Cited by 28
3SD-Net: SAR Small Ship Detection Neural Network
Ma, Y., Guan, D., Deng, Y., Yuan, W., & Wei, M. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3454308 · Cited by 35
BigDataBench: A Scalable and Unified Big Data and AI Benchmark SuitePreprint
Gao, W., Zhan, J., Wang, L., Luo, C., Zheng, D., Wen, X., Ren, R., Zheng, C., He, X., Ye, H., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08254 · Cited by 43
Min, J., Cai, M., Gou, C., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07466-0 · Cited by 28
Wide-Baseline Multi-Camera Calibration using Person Re-Identification
Xu, Y. L., Li, Y.-J., Weng, X., & Kitani, K. (2021) · 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/cvpr46437.2021.01293 · Cited by 31
A Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces
Özcan, B., Schwermann, R., & Blankenbach, J. (2020) · Materials · doi:10.3390/ma14010158 · Cited by 28
3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation
Zhang, L., Ding, Z., Li, H., Shen, Y., & Lu, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100120 · Cited by 28
Traffic Sign Detection and Recognition Using Multi-Scale Fusion and Prime Sample Attention
Cao, J., Zhang, J., & Huang, W. F. (2020) · IEEE Access · doi:10.1109/access.2020.3047414 · Cited by 30
Vector Semantic Representations as Descriptors for Visual Place Recognition
Neubert, P., Schubert, S., Schlegel, K., & Protzel, P. (2021) · Robotics: Science and Systems · doi:10.15607/rss.2021.xvii.083 · Cited by 29
Representing Topics Using Images
Αλέτρας, Ν., & Stevenson, M. (2013) · Cited by 28
Akkad, N. E., Merras, M., Baataoui, A., Saaidi, A., & Satori, K. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5012-3 · Cited by 28
Hanslovsky, P., Bogovic, J., & Saalfeld, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw794 · Cited by 25
Learning query-dependent prefilters for scalable image retrieval
Torresani, L., Szummer, M., & Fitzgibbon, A. W. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206582 · Cited by 28
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and RetrievalPreprint
Weyand, T., Araújo, A. D. M., Cao, B., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01804 · Cited by 28
Real-time Object Detection and Tracking in Mixed Reality using Microsoft HoloLens
Farasin, A., Peciarolo, F., Grangetto, M., Gianaria, E., & Garza, P. (2020) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0008877901650172 · Cited by 31
Underwater self-supervised monocular depth estimation and its application in image enhancement
Wang, J., Ye, X., Liu, Y., Mei, X., & Hou, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105846 · Cited by 29
STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
Zhou, Q., & Li, X. S. (2019) · Applied Sciences · doi:10.3390/app9235187 · Cited by 28
Temporal aggregation for large-scale query-by-image video retrieval
Araújo, A. D. M., Chaves, J., Angst, R., et al. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351054 · Cited by 28
Hybrid Teams: Flexible Collaboration Between Humans, Robots and Virtual Agents
Schwartz, T., Zinnikus, I., Krieger, H., Bürckert, C., Folz, J., Kiefer, B., Hevesi, P., Lüth, C., Pirkl, G., Spieldenner, T., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45889-2_10 · Cited by 29
Large-Scale, Metric Structure From Motion for Unordered Light Fields
Nousias, S., Lourakis, M., & Bergeles, C. (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.2019.00341 · Cited by 28
Vision-Based Human Detection Techniques: A Descriptive Review
Sumit, S. S., Rambli, D. R. A., & Mirjalili, S. (2021) · IEEE Access · doi:10.1109/access.2021.3063028 · Cited by 29
Fathian, K., Khosoussi, K., Tian, Y., Lusk, P. C., & How, J. P. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3002432 · Cited by 29
A Survey of Architecture and Function of the Primary Visual Cortex (V1)
Ng, J., Bharath, A. A., & Li Zhaoping, A. (2006) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2007/97961 · Cited by 28
Soni, B., Das, P. K., & Thounaojam, D. M. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2018.5576 · Cited by 28
Chandaria, J., Thomas, G. A., & Stricker, D. (2007) · Journal of Real-Time Image Processing · doi:10.1007/s11554-007-0043-z · Cited by 28
Cross-age face verification by coordinating with cross-face age verification
Du, L., & Ling, H. (2015) · 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.2015.7298846 · Cited by 28
Distributable Consistent Multi-object Matching
Hu, N., Qi-xing Huang, A., Thibert, B., & Guibas, L. J. (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.2018.00261 · Cited by 27
Learning Semantic-Aware Local Features for Long Term Visual Localization
Fan, B., Zhou, J., Feng, W., et al. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3187565 · Cited by 28
Broll, W. (2013) · eXamen.press · doi:10.1007/978-3-642-28903-3_8 · Cited by 28
de Barros, W. F., Nascimento, E. R., Barbosa, W., et al. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.06.018 · Cited by 28
iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial Systems
Chen, S., Li, X., Li, S., Zhou, Y., & Yang, X. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3532506 · Cited by 30
“In SfM, by assuming a pure-rotation motion, SIFT [70]-based feature matching between arbitrary two images is performed in the covisibility region (if it exists), which can be found by relative rotation based on priori world-frame rotations, see Fig.”
Task Specific Local Region Matching
Babenko, B., Dollár, P., & Belongie, S. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408848 · Cited by 28
Soccer players identification based on visual local features
Ballan, L., Bertini, M., Bimbo, A. D., et al. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282321 · Cited by 28
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
