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 21,101–21,200 of 56,201
Evaluation of Vision-Based Human Activity Recognition in Dense Trajectory Framework
Kataoka, H., Aoki, Y., Iwata, K., & Satoh, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27857-5_57 · Cited by 10
Objective Evaluation of Image Mosaics
Boutellier, J. J., Sílven, O., Tico, M., et al. (2008) · Communications in computer and information science · doi:10.1007/978-3-540-89682-1_8 · Cited by 10
Distance Measures for Gabor Jets-Based Face Authentication: A Comparative Evaluation
González-Jiménez, D., Bicego, M., Tangelder, J. W. H., et al. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74549-5_50 · Cited by 10
Dictionary-based color image retrieval using multiset theory
Besiris, D., & Zigouris, E. (2013) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2013.07.009 · Cited by 10
Local binary pattern orientation based face recognition
Shen, Y., & Chiu, C. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178138 · Cited by 10
Improved covariant local feature detector
Huo, Z., Zhang, Y., Liu, H., et al. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.03.027 · Cited by 10
Privacy Preserving Partial Localization
Geppert, M., Larsson, V., Schönberger, J. L., & Pollefeys, M. (2022) · 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/cvpr52688.2022.01682 · Cited by 11
Improved MLP Point Cloud Processing with High-Dimensional Positional Encoding
Zou, Y., Yu, H., Yang, Z., Li, Z., & Akhtar, N. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28625 · Cited by 11
“Inspired by the widely used SIFT descriptor (Lowe 2004), PointSIFT (Jiang et al. 2018) develops a 3D SIFT descriptor that considers eight crucial orientations and scales for local scale-invariant feature transform.”
MethodDiscriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification
Wen, Z., Hou, B., & Jiao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2700761 · Cited by 10
Stride towards aging problem in face recognition by applying hybrid local feature descriptors
Kamarajugadda, K. K., & Polipalli, T. R. (2018) · Evolving Systems · doi:10.1007/s12530-018-9256-6 · Cited by 10
Expression Recognition Using the Periocular Region: A Feasibility Study
Alonso‐Fernandez, F., Bigün, J., & Englund, C. (2018) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2018.00087 · Cited by 10
Relative Pose Estimation for Light Field Cameras Based on LF-Point-LF-Point Correspondence Model
Zhang, S., Jin, D., Dai, Y., et al. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3144891 · Cited by 10
Heterogeneous domain adaptation method for video annotation
Wang, H., Wu, X., & Jia, Y. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0148 · Cited by 10
EVA: An efficient vision architecture for mobile systems
Clemons, J., Pellegrini, A., Savarese, S., et al. (2013) · International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES) · doi:10.1109/cases.2013.6662517 · Cited by 10
Swin Attention Augmented Residual Network: a fine-grained pest image recognition method
Wang, X., Xiao, Z., & Deng, Z. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1619551 · Cited by 11
Markless augmented reality registration algorithm based on ORB
Du, Y., Miao, Z., & Cen, Y. (2014) · doi:10.1109/icosp.2014.7015197 · Cited by 10
A practical guide to starting SEM array tomography—An accessible volume EM technique
White, I. J., & Burden, J. J. (2023) · Methods in cell biology · doi:10.1016/bs.mcb.2022.12.023 · Cited by 15
A pilot study to determine vitiligo target size using a computer-based image analysis program
Sheth, V. M., Rithe, R., Pandya, A. G., & Chandrakasan, A. P. (2015) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2015.04.035 · Cited by 10
Manoranjitham, R., & L, D. P. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4982-5 · Cited by 10
Hong, D., Su, J. D., Qing-gen, H., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101866 · Cited by 10
Keypoint Detection and Description through Deep Learning in Unstructured Environments
Petrakis, G., & Partsinevelos, P. (2023) · Robotics · doi:10.3390/robotics12050137 · Cited by 10
Automatic and robust hand gesture recognition by SDD features based model matching
Wang, Z. (2022) · Applied Intelligence · doi:10.1007/s10489-021-02933-y · Cited by 10
Machine Vision based Sample-Tube Localization for Mars Sample Return
Daftry, S., Ridge, B., Seto, W., Pham, T.-H., Ilhardt, P., Maggiolino, G., van der Merwe, M., Brinkman, A., Mayo, J., Kulczyski, E., et al. (2021) · 2021 IEEE Aerospace Conference (50100) · doi:10.1109/aero50100.2021.9438364 · Cited by 10
Zou, Y., Kästner, M., & Reithmeier, E. (2015) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2015.05.005 · Cited by 10
Coupling Dynamic Programming with Machine Learning for Horizon Line Detection
Ahmad, T., Bebis, G. N., Regentova, E. E., Nefian, A., & Fong, T. W. (2015) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213015400187 · Cited by 10
Object Detection Boosting using Object Attributes in Detect and Describe Framework
Khan, M. J., Zafar, A., Tumanian, V., Yue, D., & Li, G. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00126 · Cited by 10
Automated Detection of Threat Materials in X-Ray Baggage Inspection Systems (XBISs)
Kayalvizhi, R., Malarvizhi, S., Choudhury, S. D., et al. (2022) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2022.3182771 · Cited by 10
Practical scalable image analysis and indexing using Hadoop
Hare, J. S., Samangooei, S., & Lewis, P. H. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1256-0 · Cited by 10
Enhanced Change Detection Performance Based on Deep Despeckling of Synthetic Aperture Radar Images
Ihmeida, M., & Shahzad, M. S. (2023) · IEEE Access · doi:10.1109/access.2023.3307208 · Cited by 10
Chen, Y., Hua, C., & Ruilin, B. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.12.005 · Cited by 10
Feature Selection and Pedestrian Detection Based on Sparse Representation
Yao, S., Wang, T., Shen, W., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0134242 · Cited by 10
Schwalbach, E. J., Chapman, M. G., & Groeber, M. A. (2021) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-021-00220-9 · Cited by 10
The relative pose estimation of aircraft based on contour model
Fu, T., & Sun, X. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2267118 · Cited by 10
Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles
Zheng, Z., Akash, K., Misu, T., Krishnamoorthy, V., Dong, M., Lee, Y. Y., & Huang, G. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536221.3556637 · Cited by 11
Se, S., & Pears, N. E. (2012) · Springer eBooks · doi:10.1007/978-1-4471-4063-4_2 · Cited by 10
Huang, W., Gao, X., & Lu, J. (2024) · Drones · doi:10.3390/drones8090502 · Cited by 10
Kılıç, E., & Öztürk, S. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1562260 · Cited by 10
Privacy-preserving multimedia cloud computing via compressive sensing and sparse representation
Kang, L., Muchtar, K., Wei, J.-D., et al. (2012) · doi:10.1109/isic.2012.6449752 · Cited by 10
Spatio-temporal Video Parsing for Abnormality DetectionPreprint
Antić, B., & Ommer, B. (2015) · Open access LMU (Ludwid Maxmilian's Universitat Munchen) · doi:10.48550/arxiv.1502.06235 · Cited by 10
Detection and analysis of landslide geomorphology based on UAV vertical photogrammetry
Bi, R., Gan, S., Yuan, X., et al. (2024) · Journal of Mountain Science · doi:10.1007/s11629-022-7766-y · Cited by 10
“First, the scale-invariant feature transform (SIFT) algorithm (Lowe 2004) was used to extract the feature points on the images.”
Xie, B., Li, J., & Zhao, X. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2692 · Cited by 10
To Learn or Not to Learn: Visual Localization from Essential MatricesPreprint
Zhou, Q., Sattler, T., Pollefeys, M., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01293 · Cited by 10
Cooperative Augmentation of Smart Objects with Projector-Camera Systems
Molyneaux, D., Gellersen, H. W., Kortuem, G., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-74853-3_29 · Cited by 10
Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features
Xu, M., Zhang, Z., Gong, Y., & Poslad, S. (2023) · Sensors · doi:10.3390/s23084063 · Cited by 11
Multimodal remote sensing change detection: An image matching perspective
Chen, H., Lan, C., Song, J., et al. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.02.004 · Cited by 10
Combining RGB and Depth Images for Indoor Scene Classification Using Deep Learning
Pujar, K., Chickerur, S., & Patil, M. S. (2017) · IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) · doi:10.1109/iccic.2017.8524231 · Cited by 10
Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment
Schall, K., Barthel, K. U., Hezel, N., & Jung, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75823-2_9 · Cited by 14
HOPIS: Hybrid Omnidirectional and Perspective Imaging System for Mobile Robots
Lin, H., & Wang, M.-L. (2014) · Sensors · doi:10.3390/s140916508 · Cited by 10
Stereo-image rectification for dense 3D reconstruction in scanning electron microscope
Kudryavtsev, A. V., Dembélé, S., & Fort-Piat, N. L. (2017) · International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS) · doi:10.1109/marss.2017.8001905 · Cited by 10
Badr, E. (2021) · ACM Computing Surveys · doi:10.1145/3453657 · Cited by 11
Content-Based Image Retrial Based on Hadoop
Yin, D., & Liu, D. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/684615 · Cited by 10
Criado, R., Romance, M., & Sánchez, Á. (2012) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127412501635 · Cited by 10
Analysis Of 3-D Building Modeling Using Photogrammetric Software: Agisoft Metashape And Micmac
Rahman, A., & Cahyono, A. B. (2023) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1276/1/012044 · Cited by 10
Adaptive Feature Selection and Feature Fusion for Semi-supervised Classification
Du, W., Phlypo, R., & Adalı, T. (2018) · Journal of Signal Processing Systems · doi:10.1007/s11265-018-1355-x · Cited by 10
Nanglia, P., Kumar, S., Rathi, D., et al. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-3140-4_27 · Cited by 10
New Perspectives of Earth Surface Remote Detection for Hydro-Geomorphological Monitoring of Rivers
Zingaro, M., Salandra, M. L., & Capolongo, D. (2022) · Sustainability · doi:10.3390/su142114093 · Cited by 10
SURF based matching for SAR image registration
Durgam, U. K., Paul, S., & Pati, U. C. (2016) · IEEE Students Conference on Electrical, Electronics and Computer Science · doi:10.1109/sceecs.2016.7509292 · Cited by 10
Figueroa, N., Dong, H., & Saddik, A. E. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629673 · Cited by 10
Multi-view content-context information bottleneck for image clustering
Hu, S., Wang, B., Lou, Z., et al. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115374 · Cited by 10
Dominant SIFT: A novel compact descriptor
Tra, A. T., Lin, W., & Kot, A. C. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178189 · Cited by 10
Stanciu, S. G., Tranca, D. E., & Coltuc, D. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.08.008 · Cited by 10
Zhao, S., Gong, M., Zhao, H., Zhang, J., & Tao, D. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01837-3 · Cited by 10
Deep Neural Network-based Feature Descriptor for Retinal Image Registration
abanovie, E., Stankevieius, G., & Matuzevičius, D. (2018) · IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) · doi:10.1109/aieee.2018.8592033 · Cited by 10
Weißbrich, M., García-Ortiz, A., & Vayá, G. P. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101647 · Cited by 10
CoopCL: Cooperative Execution of OpenCL Programs on Heterogeneous CPU-GPU Platforms
Moreń, K., & Göhringer, D. (2020) · Proceedings - Euromicro Workshop on Parallel and Distributed Processing/Proceedings · doi:10.1109/pdp50117.2020.00042 · Cited by 10
Object detection algorithm based AdaBoost residual correction Fast R-CNN on network
Lin, M., Chen, C., & Lai, C. (2019) · International Conference on Deep Learning Technologies (ICDLT) · doi:10.1145/3342999.3343013 · Cited by 10
Distinctive and compact features
Akselrod-Ballin, A., & Ullman, S. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.03.005 · Cited by 10
Lopes, G., Ribeiro, A. F., Sillero, N., et al. (2016) · Sensors · doi:10.3390/s16040558 · Cited by 10
Person re-identification by integrating metric learning and support vector machine
Zhao, D., Wang, H., Yin, H., et al. (2019) · Signal Processing · doi:10.1016/j.sigpro.2019.107277 · Cited by 10
Son, C. (2021) · Applied Sciences · doi:10.3390/app11177960 · Cited by 12
Classification of optical coherence tomography images for diagnosing different ocular diseases
Gholami, P., Hassani, M. S., Zelek, J., et al. (2018) · doi:10.1117/12.2292520 · Cited by 10
ARTOT: Autonomous real-time object detection and tracking by a moving camera
Angelov, P. P., Gude, C., Sadeghi‐Tehran, P., et al. (2012) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2012.6335175 · Cited by 10
Pose estimation of metal workpieces based on RPM-Net for robot grasping from point cloud
Li, L., Chen, X., & Zhang, T. (2022) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-03-2022-0081 · Cited by 10
Key frame extraction based on dynamic color histogram and fast wavelet histogram
Zong, Z., & Gong, Q. S. (2017) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2017.8078903 · Cited by 10
Graph Matching Based on Stochastic Perturbation
Leng, C., Xu, W., Cheng, I., et al. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2469153 · Cited by 10
Registration of infrared and visible images based on the correlation of the edges
Yi, X., Wang, B., Yao, F., et al. (2013) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2013.6745309 · Cited by 10
Adaptive Application Configuration and Distribution in Mobile Cloudlet Middleware
Verbelen, T., Simoens, P., Turck, F. D., et al. (2013) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-36660-4_13 · Cited by 10
A novel self-boosting dual-branch model for pedestrian attribute recognition
Cao, Y., Fang, Y., Zhang, Y., et al. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2023.116961 · Cited by 10
Efficient camera motion and 3D recovery using an inertial sensor
Labrie, M., & Hébert, P. (2007) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2007.23 · Cited by 10
Scene image classification with biased spatial block and pLSA
Ji, Z., Jing, P., Wang, J., et al. (2012) · International Journal of Digital Content Technology and its Applications · doi:10.4156/jdcta.vol6.issue1.48 · Cited by 10
Remote Sensing Image Detection Method Combining Dynamic Convolution and Attention Mechanism
Zhang, Y., Chen, M., & Chen, C. (2025) · IEEE Access · doi:10.1109/access.2025.3550586 · Cited by 10
Retinal vessel landmark detection using deep learning and hessian matrix
Fang, T., Su, R., Xie, L., et al. (2015) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2015.7407910 · Cited by 10
Environmental mapping for mobile robot by tracking SIFT feature Points using trinocular vision
Ogawa, Y., Shimada, N., & Shirai, Y. (2007) · SICE Annual Conference (SICE) · doi:10.1109/sice.2007.4421314 · Cited by 10
Interest point detection in depth images through scale-space surface analysis
Stückler, J., & Behnke, S. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980474 · Cited by 10
A line matching method based on local and global appearance
Zhang, Y., Yang, H., & Liu, X. (2011) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2011.6100414 · Cited by 10
Shan, X., Tang, P. T. P., & Hu, C. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.967884 · Cited by 10
Factors underlying inter-observer agreement in gaze patterns
Rahman, S., & Bruce, N. D. B. (2016) · doi:10.1145/2857491.2857495 · Cited by 10
Orientation descriptors for localization in urban environments
David, P., & Ho, S. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094556 · Cited by 10
Robust aerial image mosaicing algorithm based on fuzzy outliers rejection
Lati, A., Belhocine, M., & Achour, N. (2019) · Evolving Systems · doi:10.1007/s12530-019-09279-4 · Cited by 10
GPU-Based Optimization of Self-Organizing Map Feature Matching for Real-Time Stereo Vision
Sharma, K., Saifullah, S., & Moon, I. (2014) · Journal of information and communication convergence engineering · doi:10.6109/jicce.2014.12.2.128 · Cited by 10
Reliability Validation of Learning Enabled Vehicle Tracking
Sun, Y., Zhou, Y., Maskell, S. R., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196932 · Cited by 10
Huang, M., Mu, Z., & Zeng, H. (2015) · IET Image Processing · doi:10.1049/iet-ipr.2015.0329 · Cited by 10
Model Scaling in Smartphone GNSS-Aided Photogrammetry for Fragmentation Size Distribution Estimation
Tungol, Z. P. L., Toriya, H., Owada, N., et al. (2021) · Minerals · doi:10.3390/min11121301 · Cited by 10
A Residual Attention-based EfficientNet Homography Estimation Model for Sports Field Registration
Oo, Y. M., Jamsrandorj, A., Chao, V., Mun, K.-R., & Kim, J. (2023) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon51785.2023.10312494 · Cited by 10
Field of Junctions: Extracting Boundary Structure at Low SNR
Verbin, D., & Zickler, T. E. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00679 · Cited by 10
On Ancient Coin Classification
Zaharieva, M., Huber-Moerk, R., Noelle, M., et al. (2007) · Eurographics · doi:10.2312/vast/vast07/055-062 · Cited by 10
Object recognition using saliency guided searching
Mesquita, R. G., & Mello, C. A. B. (2016) · Integrated Computer-Aided Engineering · doi:10.3233/ica-160528 · Cited by 10
Visual homing by robust interpolation for sparse motion flow
Zhao, J. Y., & Ma, J. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202303 · Cited by 10
Kuthuru, S., Deaderick, W. P., Bai, H. X., Su, C., Vu, T. H., Monga, V., & Rao, A. U. K. (2018) · Cancer Informatics · doi:10.1177/1176935118802796 · Cited by 10
Fast and exact fixed-radius neighbor search based on sorting
Chen, X., & Güttel, S. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1929 · Cited by 9
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