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 20,501–20,600 of 56,201
Zhao, S.-J., Precioso, F., & Cord, M. (2011) · Multimedia tools and applications · doi:10.1007/s11042-010-0602-3 · Cited by 11
Copy–move forgery detection in digital image forensics: A survey
Farhan, M. H., Shaker, K., & Al-Janabi, S. T. F. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18399-2 · Cited by 11
“In 2004, the SIFT algorithm was proposed by David Lowe [44] to detect invariant interest points or extract feature vectors from images.”
Local intensity area descriptor for facial recognition in ideal and noise conditions
Tran, C.-K., Tseng, C.-D., Chao, P., et al. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.2.023011 · Cited by 11
Recent Advances in Feature Detectors and Descriptors: A Survey
Lee, H., Jeon, S., Yoon, I., et al. (2016) · IEIE Transactions on Smart Processing and Computing · doi:10.5573/ieiespc.2016.5.3.153 · Cited by 11
Video segmentation: Propagation, validation and aggregation of a preceding graph
Liu, S., Guo, D. M., Yan, C. H., et al. (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.2008.4587426 · Cited by 11
Yao, C., Zhang, H., Zhu, J., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3288594 · Cited by 11
Face Recognition Using Local Binary Probabilistic Pattern (LBPP) and 2D-DCT Frequency Decomposition
Dahmouni, A., Aharrane, N., Moutaouakil, K. E., et al. (2016) · International Conference on Computer Graphics, Imaging and Visualization (CGIV) · doi:10.1109/cgiv.2016.23 · Cited by 11
Lee, J., Lee, H., & Oh, J. (2023) · IEEE Access · doi:10.1109/access.2023.3297202 · Cited by 12
A comparative study of local feature extraction algorithms for Web pornographic image recognition
Geng, Z., Li, Z., Zhang, J., et al. (2015) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic.2015.7489815 · Cited by 11
Multi-faced Object Recognition in an Image for Inventory Counting
Sharma, T., Rajurkar, S. D., Molangur, N., Verma, N. K., & Salour, A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1135-2_26 · Cited by 11
Shotton, J., & Kohli, P. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_251 · Cited by 10
Feature Learning and Analysis for Cleanliness Classification in Restrooms
Jayasinghe, L., Wijerathne, N., Yuen, C., & Zhang, M. (2019) · IEEE Access · doi:10.1109/access.2019.2894006 · Cited by 10
Sui, H., Li, J., Lei, J., et al. (2022) · Remote Sensing · doi:10.3390/rs14225879 · Cited by 11
Rare Chinese character recognition by Radical Extraction Network
Yan, Z., Yan, C., & Zhang, C. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122728 · Cited by 11
Ghuffar, S. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-iii-1-63-2016 · Cited by 11
LBP-SURF descriptor with color invariant and texture based features for underwater images
Prabhakar, C. J., & Kumar, P. U. P. (2012) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/2425333.2425356 · Cited by 11
Liao, K., & Liu, G. (2014) · Journal of Intelligent Information Systems · doi:10.1007/s10844-014-0332-5 · Cited by 11
Learning Discriminatively Reconstructed Source Data for Object Recognition With Few Examples
Hsiao, P.-H., Chang, F.-J., & Lin, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2572602 · Cited by 10
SVMT: A MATLAB toolbox for stereo-vision motion tracking of motor reactivity
Vousdoukas, M. I., Perakakis, P., Idrissi, S., & Vila, J. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.01.006 · Cited by 12
Maiwald, F., Bruschke, J., Schneider, D., Wacker, M., & Niebling, F. (2023) · Remote Sensing · doi:10.3390/rs15071879 · Cited by 11
Low‐complexity face recognition using contour‐based binary descriptor
Lin, J., & Chiu, C. (2017) · IET Image Processing · doi:10.1049/iet-ipr.2016.1074 · Cited by 11
Merging Neuroimaging and Multimedia: Methods, Opportunities, and Challenges
Liu, T., Hu, X., Li, X., Chen, M., Han, J., & Guo, L. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2013.2296871 · Cited by 11
A monocular SLAM method for satellite proximity operations
Thomas, D., Kelly, S. J., & Black, J. T. (2016) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2016.7525555 · Cited by 11
Use of Intelligent Student Mood Classification System (ISMCS) to achieve high quality in education
Dimililer, K. (2017) · Quality & Quantity · doi:10.1007/s11135-017-0644-y · Cited by 11
Feature Matching-based Approaches to Improve the Robustness of Android Visual GUI Testing
Ardito, L., Bottino, A., Coppola, R., et al. (2021) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3477427 · Cited by 11
Large scale face identification by combined iconic features and 3D joint invariant signatures
Cadoni, M. I., Lagorio, A., & Grosso, E. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.008 · Cited by 10
On Different Colour Spaces for Medical Colour Image Classification
Ruberto, C. D., Fodde, G., & Putzu, L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23192-1_40 · Cited by 11
Face Mask Detection Using Deep Learning
Maity, S. K., Das, P., Jha, K. K., et al. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-3067-5_37 · Cited by 11
TransCNNLoc: End-to-end pixel-level learning for 2D-to-3D pose estimation in dynamic indoor scenes
Tang, S., Li, Y., Wan, J., Li, Y., Zhou, B., Guo, R., Wang, W., & Feng, Y. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.12.006 · Cited by 13
Manzorro, R., Xu, Y., Vincent, J. L., et al. (2022) · Microscopy and Microanalysis · doi:10.1017/s1431927622000356 · Cited by 11
BRIDGING STRUCTURE AND FEATURE REPRESENTATIONS IN GRAPH MATCHING
Lee, W.-J., Cheplygina, V., Tax, D. M. J., et al. (2012) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001412600051 · Cited by 11
Latent Bi-Constraint SVM for Video-Based Object Recognition
Liu, Y., Hoai, M., Shao, M., et al. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2713409 · Cited by 11
Hypergraph matching via game-theoretic hypergraph clustering
Hou, J., Pelillo, M., & Yuan, H. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108526 · Cited by 11
Münster, S., Maiwald, F., Bruschke, J., Kröber, C., Sun, Y., Dworak, D., Komorowicz, D., Munir, I., Beck, C., & Münster, D. L. (2024) · Applied Sciences · doi:10.3390/app14051992 · Cited by 16
Pointwise and pairwise clothing annotation: combining features from social media
Nogueira, K., Veloso, A., & dos Santos, J. A. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3087-2 · Cited by 11
González‐Aguilera, D., de Oña, E. R., López-Fernández, L., et al. (2020) · 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-xliii-b5-2020-213-2020 · Cited by 11
Binocular Vision-SLAM Using Improved SIFT Algorithm
Zhu, D. (2010) · International Workshop on Intelligent Systems and Applications (ISA) · doi:10.1109/iwisa.2010.5473273 · Cited by 11
Detecting image spam using local invariant features and pyramid match kernel
Zuo, H., Hu, W., Wu, O., Chen, Y., & Luo, G. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526921 · Cited by 12
Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
Narula, L., Wooten, J. M., Murrian, M. J., LaChapelle, D. M., & Humphreys, T. (2018) · Italian National Conference on Sensors · doi:10.3390/s18082452 · Cited by 12
A Hybrid 3D Descriptor With Global Structural Frames and Local Signatures of Histograms
Shen, Z., Ma, X., & Li, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2856866 · Cited by 11
Correlation based feature fusion for the temporal video scene segmentation task
Kishi, R. M., Trojahn, T. H., & Goularte, R. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6959-4 · Cited by 12
Improving Cross-Modal Constraints: Text Attribute Person Search With Graph Attention Networks
Yang, X., Wang, X., & Yang, D. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3297391 · Cited by 11
A robust watermarking scheme against frame blending and projection attacks
Gaj, S., Rathore, A. K., Sur, A., et al. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3961-6 · Cited by 11
Bag-of-features image indexing and classification in microsoft SQL server relational database
Korytkowski, M., Scherer, R., Staszewski, P., et al. (2015) · IEEE International Conference on Cybernetics (CYBCONF) · doi:10.1109/cybconf.2015.7175981 · Cited by 11
Detecting Modifications in Printed Circuit Boards from Fuel Pump Controllers
Oliveira, T. J. M. D., Wehrmeister, M. A., & Nassu, B. T. (2017) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2017.18 · Cited by 11
Compressed domain aided analysis of traffic surveillance videos
Käs, C., Brulin, M., Nicolas, H., et al. (2009) · ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) · doi:10.1109/icdsc.2009.5289345 · Cited by 11
Face Recognition using Tensors of Census Transform Histograms from Gaussian Features Maps
Ruiz-Hernandez, J. A., Crowley, J. L., Méler, A., et al. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.82 · Cited by 11
Supervised mid-level features for word image representationPreprint
Gordo, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.5224 · Cited by 11
Alternative search techniques for face detection using location estimation and binary features
Subburaman, V. B., & Marcel, S. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.002 · Cited by 11
A general shape context framework for object identification
Chi, Y., & Leung, M. K. H. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2008.05.009 · Cited by 11
Modeling the Developing Drosophila Brain: Rationale, Technique, and Application
Hartenstein, V., Cardona, A., Pereanu, W., & Younossi‐Hartenstein, A. (2008) · BioScience · doi:10.1641/b580910 · Cited by 11
Automatic 3D Teeth Reconstruction From Five Intra-Oral Photos Using Parametric Teeth Model
Chen, Y., Gao, S., Tu, P., et al. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3277914 · Cited by 11
Histogram of maximal point-edge orientation for multi-source image matching
Wu, Q., Xu, G., Cheng, Y., et al. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1727055 · Cited by 11
A comparative study of control architectures in UAV/UGV-based surveillance system
Khaleghi, A. M., Xu, D., Minaeian, S., Li, M., Yuan, Y., Liu, J., Son, Y., Vo, C., Mousavian, A., & Lien, J. (2014) · Cited by 10
Analysis and tests of visual based techniques for orbital rendezvous operations
Palmerini, G. B., Sabatini, M., & Gasbarri, P. (2013) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2013.6497417 · Cited by 12
SICO: A System for Detection of Near-Duplicate Images During Search
Foo, J. J., Sinha, R., & Zobel, J. (2007) · doi:10.1109/icme.2007.4284720 · Cited by 11
Design of a Convolutional Two-Dimensional Filter in FPGA for Image Processing Applications
Licciardo, G. D., Cappetta, C., & Benedetto, L. D. (2017) · Computers · doi:10.3390/computers6020019 · Cited by 11
Model-Independent Phenotyping of C. elegans Locomotion Using Scale-Invariant Feature Transform
Koren, Y., Sznitman, R., Arratia, P. E., Carls, C., Krajacic, P., Brown, A. E., & Sznitman, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122326 · Cited by 11
Fine-Grained Image Classification With Gaussian Mixture Layer
Liang, J., Guo, J., Liu, X., & Lao, S. (2018) · IEEE Access · doi:10.1109/access.2018.2871621 · Cited by 12
Perera, C. J., Premachandra, C., & Kawanaka, H. (2023) · Sensors · doi:10.3390/s23188004 · Cited by 11
Calibrating a wide-area camera network with non-overlapping views using mobile devices
Kuo, T., Ni, Z., Sunderrajan, S., et al. (2014) · ACM Transactions on Sensor Networks · doi:10.1145/2530284 · Cited by 11
A new approach for the bundle adjustment problem with fixed constraints in stereo vision
Xue, J., & Su, X. (2011) · Optik · doi:10.1016/j.ijleo.2011.09.025 · Cited by 11
Automatic Lip Contour Tracking And Visual Character Recognition For Computerized Lip Reading
Mehrotra, H., Agrawal, G., & Srivastava, M. (2009) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1074396 · Cited by 11
Stitching Aerial Images for Vehicle Positioning and Tracking
Tsao, P., İk, T.-U., Chen, G.-W., & Peng, W.-C. (2018) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2018.00096 · Cited by 13
Visualization of Hyperspectral Imaging Data Based on Manifold Alignment
Liao, D., Qian, Y., & Zhou, J. W. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.22 · Cited by 11
Ranking the local invariant features for the robust visual saliencies
Xia, S., Ren, P., & Hancock, E. R. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761170 · Cited by 11
A Novel Image Retrieval Algorithm Based on ROI by Using SIFT Feature Matching
Wang, Z., Jia, K., & Liu, P. (2008) · International Conference on Multimedia and Information Technology · doi:10.1109/mmit.2008.149 · Cited by 11
Progressive match expansion via coherent subspace constraint
Sun, K., Liu, L., & Tao, W. (2016) · Information Sciences · doi:10.1016/j.ins.2016.07.020 · Cited by 11
Mixture of Subspaces Image Representation and Compact Coding for Large-Scale Image Retrieval
Takahashi, T., & Kurita, T. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2382092 · Cited by 11
Real-Time Hand Gesture Recognition Based on the Depth Map for Human Robot Interaction
Hamissi, M., & Faez, K. (2013) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v3i6.3954 · Cited by 11
Multiple subsequence combination in human action recognition
Onofri, L., Soda, P., & Iannello, G. (2013) · IET Computer Vision · doi:10.1049/iet-cvi.2013.0015 · Cited by 11
A multi-class pedestrian detection network for distorted pedestrians
Zhang, J., Xiao, J., Zhou, C., et al. (2018) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2018.8397871 · Cited by 11
IPAL at ImageClef 2007 Mixing Features, Models and Knowledge
Gao, S., Chevallet, J., Le, T. H. D., et al. (2007) · Cited by 11
Interest Points of General Imbalance
Li, Q. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2028253 · Cited by 11
Labati, R. D., Genovese, A., Muñoz, E., et al. (2015) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2015.60 · Cited by 11
Agarwal, R., Shekhawat, N. S., & Luhach, A. K. (2021) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2021.103854 · Cited by 11
Pose-Graph SLAM for Underwater Navigation
Chaves, S. M., Galceran, E., Ozog, P., Walls, J. M., & Eustice, R. M. (2017) · Lecture notes in control and information sciences · doi:10.1007/978-3-319-55372-6_7 · Cited by 13
An improved deep learning method for flying object detection and recognition
Aote, S. S., Wankhade, N. R., Pardhi, A., Misra, N., Agrawal, H., & Potnurwar, A. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02703-y · Cited by 12
Salehpour, M., & Behrad, A. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015002 · Cited by 11
MacArthur, K. E., Polani, S., Klingenhof, M., et al. (2023) · ACS Applied Energy Materials · doi:10.1021/acsaem.3c00405 · Cited by 11
Improved image GPS location estimation by mining salient features
Li, J., Qian, X., Lan, K., et al. (2015) · Signal Processing Image Communication · doi:10.1016/j.image.2015.07.007 · Cited by 11
Thermal-to-Visible Face Alignment on Edge Map
Sun, L., & Zheng, Z.-W. (2017) · IEEE Access · doi:10.1109/access.2017.2712159 · Cited by 13
Copy and Move Forgery Detection Using SIFT and Local Color Dissimilarity Maps
Mahfoudi, G., Morain-Nicolier, F., Retraint, F., & Pic, M. M. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969355 · Cited by 12
Simultaneous active camera array focus plane estimation and occluded moving object imaging
Yang, T., Zhang, Y., Yu, R., et al. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.05.001 · Cited by 11
Zhang, C., & Tang, P. (2015) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784479247.034 · Cited by 11
Simultaneous Trajectory Association and Clustering for Motion Segmentation
Wang, Y., Liu, Y., Blasch, E., et al. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2773636 · Cited by 11
Deep Learning Based 3D Segmentation: A SurveyPreprint
He, Y., Yu, H., Liu, X., Yang, Z., Sun, W. J., Anwar, S., & Mian, A. S. (2021) · arXiv (Cornell University) · doi:10.2139/ssrn.4824679 · Cited by 56
Bag‐of‐features for image memorability evaluation
Lahrache, S., Ouazzani, R. E., & Qadi, A. E. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2015.0383 · Cited by 11
Ratnasari, R., Irawan, B., & Setianingsih, C. (2017) · IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) · doi:10.1109/apwimob.2017.8284003 · Cited by 11
Registration of multimodal images with edge features and scale invariant PIIFD
Xu, C. G., Liu, L., Zhang, J., et al. (2020) · Infrared Physics & Technology · doi:10.1016/j.infrared.2020.103549 · Cited by 11
Pair-Wise Similarity Knowledge Distillation for RSI Scene Classification
Zhao, H., Sun, X., Gao, F., et al. (2022) · Remote Sensing · doi:10.3390/rs14102483 · Cited by 11
Unsupervised Video Hashing via Deep Neural Network
Ma, C., Gu, Y., Gong, C., Yang, J., & Feng, D. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9812-x · Cited by 12
Robust matching and recognition using context-dependent kernels
Sahbi, H., Audibert, J.-Y., Rabarisoa, J., et al. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390264 · Cited by 11
SLAM in Underwater Environment Using SIFT and Topologic Maps
Drews, P., Botelho, S. S. D. C., & Gomes, S. C. P. (2008) · doi:10.1109/lars.2008.32 · Cited by 11
Unsupervised learning of quaternion features for image classification
Risojević, V., & Babić, Z. (2013) · International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS) · doi:10.1109/telsks.2013.6704945 · Cited by 11
Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification
Bi, Y., Zhang, M., & Xue, B. (2021) · doi:10.26686/wgtn.13884998 · Cited by 11
Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis
Pinho, E., & Costa, C. M. A. (2018) · Applied Sciences · doi:10.3390/app8081213 · Cited by 11
Li, J., Akitoshi, I. T. O., Yaguchi, H., et al. (2019) · Advanced Robotics · doi:10.1080/01691864.2019.1689166 · Cited by 11
Canny edge-based deformable image registration
Kearney, V. P., Huang, Y., Mao, W., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa5342 · Cited by 11
Feature matching based positioning algorithm for swarm robotics
Seçkin, A. Ç., Karpuz, C., & Özek, A. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.06.006 · Cited by 11
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
