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 3,401–3,500 of 56,204
Review on condition monitoring techniques for water pipelines
Latif, J., Shakir, M. Z., Edwards, N., et al. (2022) · Measurement · doi:10.1016/j.measurement.2022.110895 · Cited by 97
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
Sminchisescu, C., Kanaujia, A., & Metaxas, D. (2006) · 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.2006.169 · Cited by 97
Xetal-II: A 107 GOPS, 600 mW Massively Parallel Processor for Video Scene Analysis
Abbo, A. A., Kleihorst, R. P., Choudhary, V., et al. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2007.909328 · Cited by 96
Summarizing tourist destinations by mining user-generated travelogues and photos
Pang, Y., Hao, Q., Yuan, Y., et al. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.10.010 · Cited by 96
Discriminative human action segmentation and recognition using semi-Markov model
Shi, Q., Wang, L., Cheng, L., & Smola, A. J. (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.2008.4587557 · Cited by 94
Robust Deformable and Occluded Object Tracking With Dynamic Graph
Cai, Z., Wen, L., Lei, Z., et al. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2364919 · Cited by 96
Instance-level Image Retrieval using Reranking Transformers
Tan, F., Yuan, J., & Ordóñez, V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01189 · Cited by 112
Xue, Y., Peizhe, S., Jia, F., et al. (2021) · Underground Space · doi:10.1016/j.undsp.2021.08.004 · Cited by 96
Functional Object Class Detection Based on Learned Affordance Cues
Stark, M. M., Lies, P., Zillich, M., Wyatt, J., & Schiele, B. (2008) · Springer eBooks · doi:10.1007/978-3-540-79547-6_42 · Cited by 95
Mocanu, B., Țapu, R., & Zaharia, T. B. (2016) · Sensors · doi:10.3390/s16111807 · Cited by 102
A Model-Based Sequence Similarity with Application to Handwritten Word Spotting
Rodríguez-Serrano, J. A., & Perronnin, F. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.25 · Cited by 94
Capitain, C. C., & Weller, P. (2021) · Molecules · doi:10.3390/molecules26185457 · Cited by 97
Ballesteros, R., Álvarez, J. F. O., Hernández‐López, D., & Moreno, M. Á. (2014) · Precision Agriculture · doi:10.1007/s11119-014-9355-8 · Cited by 95
Bhuiyan, M. A. B., Abdullah, H. M., Arman, S. E., Rahman, S. S., & Mahmud, K. A. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100214 · Cited by 113
Multi-Label Dictionary Learning for Image Annotation
Jing, X., Wu, F., Li, Z., Hu, R., & Zhang, D. D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2549459 · Cited by 93
Deep Self-Evolution Clustering
Chang, J., Meng, G., Wang, L., Xiang, S., & Pan, C. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889949 · Cited by 99
LemurFaceID: a face recognition system to facilitate individual identification of lemurs
Crouse, D., Jacobs, R. L., Richardson, Z., Klum, S., Jain, A. K., Baden, A. L., & Tecot, S. R. (2017) · BMC Zoology · doi:10.1186/s40850-016-0011-9 · Cited by 96
Incremental natural language processing for HRI
Brick, T. R., & Scheutz, M. J. (2007) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1228716.1228752 · Cited by 99
Taking a Deeper Look at Co-Salient Object Detection
Fan, D.-P., Zheng, L. L., Ji, G.-P., Zhang, D., Fu, H., & Cheng, 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/cvpr42600.2020.00299 · Cited by 93
Joint 2D-3D temporally consistent semantic segmentation of street scenes
Floros, G., & Leibe, B. (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.2012.6248007 · Cited by 93
Improving monocular visual SLAM in dynamic environments: an optical-flow-based approach
Cheng, J., Sun, Y., & Meng, M. Q. ‐. (2019) · Advanced Robotics · doi:10.1080/01691864.2019.1610060 · Cited by 95
Mouats, T., Aouf, N., Sappa, A., Aguilera, C., & Toledo, R. (2015) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2014.2354731 · Cited by 82
Quaternionic Local Ranking Binary Pattern: A Local Descriptor of Color Images
Lan, R., Zhou, Y., & Tang, Y. Y. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2507404 · Cited by 95
Twenty Questions with Noise: Bayes Optimal Policies for Entropy Loss
Jedynak, B., Frazier, P. I., & Sznitman, R. (2012) · Journal of Applied Probability · doi:10.1239/jap/1331216837 · Cited by 95
Inferring terrestrial photosynthetic light use efficiency of temperate ecosystems from space
Hilker, T., Coops, N. C., Hall, F. G., et al. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2011jg001692 · Cited by 96
Large-Scale Video Retrieval Using Image Queries
Araújo, A. D. M., & Girod, B. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2667710 · Cited by 94
OD-LBP: Orthogonal difference-local binary pattern for Face Recognition
Karanwal, S., & Diwakar, M. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102948 · Cited by 96
Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’
Chen, T., Sampath, V., May, M. C., Shan, S., Jorg, O. J., Martín, J. J. A., Stamer, F., Fantoni, G., Tosello, G., & Calaon, M. (2023) · Applied Sciences · doi:10.3390/app13031903 · Cited by 110
A pattern recognition strategy for visual grape bunch detection in vineyards
Pérez-Zavala, R., Torres‐Torriti, M., Cheein, F. A., et al. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.05.019 · Cited by 96
Yang, M., & Liao, W.-C. (2014) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2014.2307297 · Cited by 96
Target Detection Method of UAV Aerial Imagery Based on Improved YOLOv5
Luo, X., Wu, Y., & Wang, F. (2022) · Remote Sensing · doi:10.3390/rs14195063 · Cited by 103
3D Tree Dimensionality Assessment Using Photogrammetry and Small Unmanned Aerial Vehicles
Gatziolis, D., Lienard, J. F., Vogs, A., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137765 · Cited by 96
SVD-matching using SIFT features
Delponte, E., Isgrò, F., Odone, F., & Verri, A. (2006) · Graphical Models · doi:10.1016/j.gmod.2006.07.002 · Cited by 96
Learning a Confidence Measure for Optical Flow
Aodha, O. M., Humayun, A., Pollefeys, M., & Brostow, G. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.171 · Cited by 93
Scale invariant feature approach for insect monitoring
Solís-Sánchez, L. O., Castañeda-Miranda, R., García-Escalante, J. J., Torres‐Pacheco, I., Guevara-González, R. G., Castañeda-Miranda, C. L., & Alaniz-Lumbreras, P. D. (2010) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2010.10.001 · Cited by 95
Comparison of 3D interest point detectors and descriptors for point cloud fusion
Hänsch, R., Weber, T., & Hellwich, O. (2014) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-3-57-2014 · Cited by 96
Modeling and Classifying Breast Tissue Density in Mammograms
Bosch, A., Muñoz, X., Oliver, A., & Martı́, J. (2006) · 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.2006.188 · Cited by 96
Hong, R., Tang, J., Tan, H., et al. (2011) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2043612.2043613 · Cited by 96
Scalable Face Image Retrieval with Identity-Based Quantization and Multireference Reranking
Wu, Z., Ke, Q., Sun, J. Z., & Shum, H. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.111 · Cited by 98
Transform coding of image feature descriptors
Chandrasekhar, V., Gabriel Takacs, A., Chen, D. M., Tsai, S. S., Singh, J., & Girod, B. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.805982 · Cited by 96
Data Mining and Knowledge Discovery in Real Life Applications
Ponce, J., & Karahoca, A. (2009) · doi:10.5772/97 · Cited by 96
Zhou, P., Cheng, G., Liu, Z., Bu, S., & Hu, X. (2015) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-015-0370-3 · Cited by 96
Correspondence labelling for wide-timeframe free-form surface matching
Starck, J., & Hilton, A. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409108 · Cited by 95
CG2Real: Improving the Realism of Computer Generated Images Using a Large Collection of Photographs
Johnson, M. K., Dale, K. M., Avidan, S., Pfister, H., Freeman, W. T., & Matusik, W. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.233 · Cited by 91
Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
Ho, T. K., & Gwak, J. (2019) · Applied Sciences · doi:10.3390/app9194130 · Cited by 96
Visual Quality Assessment for Super-Resolved Images: Database and Method
Zhou, F., Yao, R., Liu, B., & Qiu, G. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2898638 · Cited by 91
Hartmann, W., Havlena, M., & Schindler, K. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.9 · Cited by 105
Artificial intelligence for pediatric ophthalmology
Reid, J. E., & Eaton, E. (2019) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000593 · Cited by 95
OPEN-SOURCE IMAGE-BASED 3D RECONSTRUCTION PIPELINES: REVIEW, COMPARISON AND EVALUATION
Stathopoulou, E. K., Welponer, M., & Fabio Remondino, A. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w17-331-2019 · Cited by 98
HashFormer: Vision Transformer Based Deep Hashing for Image Retrieval
Li, T., Zhang, Z., Pei, L., et al. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3157517 · Cited by 95
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Uzkent, B., Rangnekar, A., & Hoffman, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.35 · Cited by 88
Attention-based Pyramid Aggregation Network for Visual Place Recognition
Zhu, Y., Wang, J., Xie, L., & Zheng, L. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240525 · Cited by 89
ACCURATE IMAGE RETRIEVAL BASED ON COMPACT COMPOSITE DESCRIPTORS AND RELEVANCE FEEDBACK INFORMATION
Chatzichristofis, S. A., Zagoris, K., Boutalis, Y. S., et al. (2010) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001410007890 · Cited by 95
Use of Autostitch for automatic stitching of microscope images
Ma, B., Zimmermann, T., Rohde, M., Winkelbach, S., HeFeng, F. Q., Lindenmaier, W., & Dittmar, K. E. J. (2006) · Micron · doi:10.1016/j.micron.2006.07.027 · Cited by 94
Yahyanejad, S., & Rinner, B. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.07.015 · Cited by 95
A Vision-Based Approach to UAV Detection and Tracking in Cooperative Applications
Opromolla, R., Fasano, G., & Accardo, D. (2018) · Italian National Conference on Sensors · doi:10.3390/s18103391 · Cited by 99
Gao, W., Tian, Y., Huang, T., et al. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749606 · Cited by 95
Multi-view Superpixel Stereo in Urban Environments
Mičušík, B., & Košecká, J. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0327-9 · Cited by 94
Adaptive deformable convolutional network
Chen, F., Wu, F., Xu, J. W., Gao, G., Ge, Q., & Jing, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.128 · Cited by 95
CMRNet: Camera to LiDAR-Map Registration
Cattaneo, D., Vaghi, M., Ballardini, A. L., Fontana, S., Sorrenti, D. G., & Burgard, W. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917470 · Cited by 99
Face Identification by SIFT-based Complete Graph Topology
Kisku, D. R., Rattani, A., Grosso, E., et al. (2007) · IEEE Workshop on Automatic Identification Advanced Technologies (AutoID) · doi:10.1109/autoid.2007.380594 · Cited by 95
A survey on computer aided diagnosis for ocular diseases
Zhang, Z., Srivastava, R., Liu, H., Chen, X., Duan, L., Wong, D. W. K., Kwoh, C. K., Wong, T. Y., & Liu, J. (2014) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-14-80 · Cited by 92
UAV Localization Using Autoencoded Satellite Images
Bianchi, M., & Barfoot, T. D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3060397 · Cited by 99
Controlling UAVs with sensor input spoofing attacks
Davidson, D., Wu, H., Jellinek, R., Ristenpart, T., & Singh, V. (2016) · Cited by 122
RLBP: Robust Local Binary Pattern
Chen, J., Kellokumpu, V., Zhao, G., & Pietikäinen, M. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.122 · Cited by 96
Multiple feature kernel hashing for large-scale visual search
Liu, X., He, J., & Lang, B. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.08.022 · Cited by 94
Zitnick, C. L., & Ramnath, K. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126263 · Cited by 90
Toward Robust Material Recognition for Everyday Objects
Hu, D. P., Bo, L., & Ren, X. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.48 · Cited by 89
Underwater Photogrammetry for Archaeology
Drap, P. (2012) · InTech eBooks · doi:10.5772/33999 · Cited by 95
Improving Descriptors for Fast Tree Matching by Optimal Linear Projection
Mikolajczyk, K., & Matas, J. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408871 · Cited by 91
Efficiently registering video into panoramic mosaics
Steedly, D., Pal, C., & Szeliski, R. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.86 · Cited by 95
Multi-scale interest regions from unorganized point clouds
Unnikrishnan, R., & Hebert, M. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563030 · Cited by 94
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
Bae, G., Budvytis, I., & Cipolla, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01289 · Cited by 99
Smooth object retrieval using a bag of boundaries
Arandjelović, R., & Zisserman, A. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126265 · Cited by 89
Boix, X., Gonfaus, J. M., van de Weijer, J., Bagdanov, A. D., Serrat, J., & Gonzàlez, J. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0449-8 · Cited by 96
An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images
Kupfer, B., Netanyahu, N. S., & Shimshoni, I. (2014) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2343471 · Cited by 95
Accurate Image Search with Multi-Scale Contextual Evidences
Zheng, L., Wang, S., Wang, J., & Tian, Q. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0889-2 · Cited by 93
Recognizing Complex Events Using Large Margin Joint Low-Level Event Model
Izadinia, H., & Shah, M. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_31 · Cited by 92
Mixed reality and remote sensing application of unmanned aerial vehicle in fire and smoke detection
Esfahlani, S. S. (2019) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2019.04.006 · Cited by 97
Stel component analysis: Modeling spatial correlations in image class structure
Jojić, N., Perina, A., Cristani, M., Murino, V., & Frey, B. J. (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.2009.5206581 · Cited by 96
Piermattei, L., Carturan, L., & Guarnieri, A. (2015) · Earth Surface Processes and Landforms · doi:10.1002/esp.3756 · Cited by 94
BVMatch: Lidar-Based Place Recognition Using Bird's-Eye View Images
Luo, L., Cao, S.-Y., Han, B., Shen, H.-L., & Li, J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3091386 · Cited by 96
Line matching leveraged by point correspondences
Fan, B., Wu, F., & Hu, Z. (2010) · 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.2010.5540186 · Cited by 96
Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis
Teerakanok, S., & Uehara, T. (2019) · IEEE Access · doi:10.1109/access.2019.2907316 · Cited by 95
Image Matching via Saliency Region Correspondences
Toshev, A., Shi, J., & Daniilidis, K. (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.2007.382973 · Cited by 95
Multi-View Information-Bottleneck Representation Learning
Wan, Z., Zhang, C., Zhu, P., & Hu, Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17210 · Cited by 104
Video Inpainting With Short-Term Windows: Application to Object Removal and Error Concealment
Ebdelli, M., Meur, O. L., & Guillemot, C. M. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2437193 · Cited by 86
Learning Saliency Maps for Object Categorization
Moosmann, F., Larlus, D., Moosmann, F., & Jurie, F. (2006) · Cited by 95
Hierarchical map building using visual landmarks and geometric constraints
Živković, Z., Bakker, B., & Kröse, B. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2005.1544951 · Cited by 95
A systematic review of physiological signals based driver drowsiness detection systems
Saleem, A. A., Siddiqui, H. U. R., Raza, M. A., Rustam, F., Dudley, S., & Ashraf, I. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09898-9 · Cited by 102
A Biologically Inspired Appearance Model for Robust Visual Tracking
Zhang, S., Lan, X., Yao, H., Zhou, H., Tao, D., & Li, X. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2586194 · Cited by 93
Multiple Kernel Learning for Sparse Representation-Based Classification
Shrivastava, A., Patel, V. M., & Chellappa, R. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2324290 · Cited by 93
Gao, Y., Tang, J., Hong, R., et al. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1873970 · Cited by 94
Pose estimation with radial distortion and unknown focal length
Josephson, K., & Byröd, M. (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.2009.5206756 · Cited by 94
Text extraction from scene images by character appearance and structure modeling
Yi, C., & Tian, Y. (2012) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.11.002 · Cited by 95
Scene Recognition on the Semantic Manifold
Kwitt, R., Vasconcelos, N. M., & Rasiwasia, N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_26 · Cited by 95
Spexard, T. P., Li, S., Wrede, B., Fritsch, J., Sagerer, G., Booij, O., Živković, Z., Terwijn, B., & Kröse, B. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.281770 · Cited by 92
Developments and Challenges for Autonomous Unmanned Vehicles
Finn, A., & Scheding, S. (2010) · Intelligent systems reference library · doi:10.1007/978-3-642-10704-7 · Cited by 109
State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems
Alkendi, Y., Seneviratne, L. D., & Zweiri, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3082778 · Cited by 114
Peer-to-Peer Indoor Navigation Using Smartphones
Yin, Z., Wu, C., Yang, Z., & Liu, Y. (2017) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2017.2680844 · Cited by 94
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
