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 23,301–23,400 of 56,201
Improving accuracy for Ego vehicle motion estimation using epipolar geometry
Nedevschi, S., Golban, C., & Mitran, C. (2009) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2009.5309610 · Cited by 8
SRNN: Self-regularized neural network
Xu, C., Yang, J. J., Gao, J., Lai, H., & Yan, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.07.051 · Cited by 9
Multiple Copy-Move Forgery Detection Based on Density Clustering
Zhou, X. H., & Shi, Q. J. (2021) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661821010181 · Cited by 8
A Background Reconstruction Method Based on Double-background
Pan, W., Kun, W., Chai, Z., et al. (2007) · International Conference on Image and Graphics · doi:10.1109/icig.2007.14 · Cited by 8
AdvSpoofGuard: Optimal transport driven robust face presentation attack detection system
Siddique, T. H. M., Khan, S., Wang, Z., et al. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113759 · Cited by 8
Liu, G., Gingold, Y. I., & Lien, J. (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.2015.7298722 · Cited by 8
Applications of Deep Learning in Object Detection
Li, K. (2022) · International Conference on Computers, Information Processing and Advanced Education (CIPAE) · doi:10.1109/cipae55637.2022.00097 · Cited by 8
Enhancement method for non-uniform scattering images of turbid underwater based on neural network
Liu, K., & Liang, Y. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104813 · Cited by 8
Yu, L., Xia, X., Zhou, K., & Zhao, L. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2018.5488 · Cited by 8
Video Stabilization System Based on Speeded-up Robust Features
Zheng, X., Cui, S., Wang, G., et al. (2015) · Advances in computer science research · doi:10.2991/iiicec-15.2015.435 · Cited by 8
Improved Strategies for HPE Employing Learning-by-Synthesis Approaches
Larumbe-Bergera, A., Ariz, M., Bengoechea, J. J., et al. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.182 · Cited by 8
Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT
Sriman, B., & Schomaker, L. (2015) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0005218603040311 · Cited by 8
Liu, Y., Wang, C., Zhang, Y., Sun, X., Du, C., Xie, D., & Tian, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111246 · Cited by 9
Registrating oblique images by integrating affine and scale-invariant features
Yu, M., Yang, H., Deng, K., et al. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1362129 · Cited by 8
An Optimal Feature Selection Approach Using IBBO for Histopathological Image Classification
Saraswat, M., Pal, R., Singh, R., et al. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-33-4582-9_3 · Cited by 8
Event-driven stereo vision with orientation filters
Camuñas-Mesa, L. A., Serrano‐Gotarredona, T., Linares-Barranco, B., et al. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865114 · Cited by 8
On Vocabulary Size in Bag-of-Visual-Words Representation
Hou, J., Kang, J., & Qi, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15702-8_38 · Cited by 8
Surveying and Pose Estimation of a Lander Using Approximative Crater Modelling
Wokes, D., & Wokes, S. (2010) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2010-8342 · Cited by 8
Information Theoretic Learning for Pixel-Based Visual Agents
Gori, M., Melacci, S., Lippi, M., & Maggini, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_62 · Cited by 9
Deep Feature Learning with Manifold Embedding for Robust Image Retrieval
Chen, X., & Li, Y. (2020) · Algorithms · doi:10.3390/a13120318 · Cited by 8
Motion Smoothing Strategies for 2D Video Stabilization
Sánchez, J., & Morel, J. (2018) · SIAM Journal on Imaging Sciences · doi:10.1137/17m1127156 · Cited by 8
Wu, Z., Boen, J., Jindal, S., Basu, S., Bieniosek, M. F., He, S., LaPelusa, M. B., Mayer, A. T., Kaseb, A. O., Zou, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.11.656869 · Cited by 11
“The alignment process employed the Scale-Invariant Feature Transform (SIFT) algorithm[76,77], which identified key points in images and matched the feature descriptors of key points to identify corresponding pairs of points from different images.”
A wheel-based side-view car detection using snake algorithm
Vinoharan, V., Ramanan, A., & Kodituwakku, S. R. (2012) · IEEE International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs.2012.6419902 · Cited by 8
To face or not to face: Towards reducing false positive of face detection
Siqi, Y., Wiliem, A., & Lovell, B. C. (2016) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2016.7804415 · Cited by 9
Topological and Metric Robot Localization through Computer Vision Techniques
Murillo, A. C., Guerrero, J. J., & Saguüés, C. (2008) · Lecture notes in electrical engineering · doi:10.1007/978-0-387-75523-6_8 · Cited by 8
Feature Motion for Monocular Robot Navigation
Rodrigo, R. P., Chen, Z., & Samarabandu, J. K. (2006) · International Conference on Information and Automation · doi:10.1109/icinfa.2006.374112 · Cited by 8
Local Unsupervised Learning for Image AnalysisPreprint
Grinberg, L., Hopfield, J. J., & Krotov, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.08993 · Cited by 17
Image precise matching with illumination robust in vehicle visual navigation
Zhou, J., Cheng, X., Ruizhi, H., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2994542 · Cited by 8
Place Recognition with Memorable and Stable Cues for Loop Closure of Visual SLAM Systems
Islam, R., & Habibullah, H. (2022) · Robotics · doi:10.3390/robotics11060142 · Cited by 9
Simulation of Multispectral and Hyperspectral EO Products for Onboard Machine Learning Application
Longépé, N., Petrelli, I., Kadunc, N. O., et al. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3434437 · Cited by 8
Applying artificial vision models to human scene understanding
Aminoff, E., Toneva, M., Shrivastava, A., Chen, X., Misra, I., Gupta, A., & Tarr, M. J. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00008 · Cited by 7
González-Quiñones, J. J., Reinoso-Gordo, J. F., Solano, J. A. G., Cámara, J. M., León-Robles, C. A., Matás, G. J. L., & Arenas, J. M. J. (2022) · SPAL Revista de Prehistoria y Arqueología de la Universidad de Sevilla · doi:10.12795/spal.2022.i31.18 · Cited by 8
Nicosevici, T., García, R., Negahdaripour, S., et al. (2007) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2007.364245 · Cited by 8
Face identification from low resolution near-infrared images
Ghosh, S., Keshari, R., Singh, R., et al. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532495 · Cited by 8
Multi-feature hierarchical topic models for human behavior recognition
Li, H., Zhang, F., & Zhang, S. (2014) · Science China Information Sciences · doi:10.1007/s11432-013-4794-9 · Cited by 8
PolSAR Target Detection via Reflection Symmetry and a Wishart Classifier
Gu, M., Liu, H., Wang, Y., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2999472 · Cited by 8
Rotation invariant similarity measure for non-local self-similarity based image denoising
Zuo, C., Jovanov, L., Luong, H. Q., et al. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351074 · Cited by 8
Spatio-temporal constraints for on-line 3D object recognition in videos
Noceti, N., Delponte, E., & Odone, F. (2009) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.06.006 · Cited by 8
Palmprint based verification system robust to rotation, scale and occlusion
Badrinath, G. S., & Gupta, P. (2009) · doi:10.1109/iccit.2009.5407273 · Cited by 8
Location Recognition and Global Localization Based on Scale-Invariant Keypoints
Košecká, J., & Yang, X. (2004) · Cited by 8
2D Hand Detection Using Multi-Feature Skin Model Supervised Cascaded CNN
Wang, Q., Zhang, G., & Yu, S. C. (2018) · Journal of Signal Processing Systems · doi:10.1007/s11265-018-1406-3 · Cited by 8
Frankenstein: Learning Deep Face Representations using Small DataPreprint
Hu, G., Peng, X., Yang, Y., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06470 · Cited by 8
MIST: Multiple Instance Spatial Transformer
Angles, B., Jin, Y., Kornblith, S., Tagliasacchi, A., & Yi, K. M. (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/cvpr46437.2021.00244 · Cited by 8
Applying information theory to efficient SLAM
Chli, M. (2009) · Spiral (Imperial College London) · doi:10.25560/5634 · Cited by 10
A Primer on Autonomous Aerial Vehicle Design
Coppejans, H., & Myburgh, H. C. (2015) · Sensors · doi:10.3390/s151229785 · Cited by 8
González-Quiñones, J. J., Reinoso-Gordo, J. F., León-Robles, C. A., et al. (2018) · Sensors · doi:10.3390/s18113880 · Cited by 8
A visual odometry base-tracking system for intraoperative C-arm guidance
Haliburton, L., Esfandiari, H., Guy, P., et al. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02229-5 · Cited by 8
A low latency feature extraction accelerator with reduced internal memory
Sun, R., Liu, P., Wang, J., et al. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050258 · Cited by 8
Combining Dense Structure from Motion and Visual SLAM in a Behavior-Based Robot Control Architecture
Cubber, G. D., Berrabah, S. A., Doroftei, D., et al. (2010) · International Journal of Advanced Robotic Systems · doi:10.5772/7240 · Cited by 8
Shi, N., Guo, S., & Kontar, R. A. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.04.002 · Cited by 10
Learning Multi-Stage Tasks with One Demonstration via Self-Replay
Di Palo, N., & Johns, E. (2021) · arXiv · doi:10.48550/arxiv.2111.07447 · Cited by 8
“We also compared a non learning-based keypoint-detection algorithm, SIFT [31], to automatically extract matching features from the current observation and the bottleneck pose observation, used to compute how to align the end-effector to the bottleneck pose.”
MethodComputational Methods for the Analysis of Footwear Impression Evidence
Srihari, S. N., & Tang, Y. (2014) · Studies in computational intelligence · doi:10.1007/978-3-319-05885-6_15 · Cited by 9
Self-consistency and universality of camera lens distortion modelsPreprint
Tang, Z., Grompone, R., Gioi, V., et al. (2012) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 8
Huang, Z., Pan, X., Pan, W., Bian, W., Xu, Y., Cheung, K. C., Zhang, G., & Li, H. (2022) · ACM Transactions on Graphics · doi:10.1145/3550454.3555468 · Cited by 8
YOLO-ASC: You Only Look Once And See Contours
Hurtík, P., Molek, V., & Vlašánek, P. (2020) · doi:10.1109/ijcnn48605.2020.9207223 · Cited by 8
2D Human Pose Estimation from Monocular Images: A Survey
Sun, J., Xue, C., Lu, Y., & Jianwen, C. (2020) · IEEE International Conference on Computer and Communication Engineering Technology (CCET) · doi:10.1109/ccet50901.2020.9213131 · Cited by 8
Re-weighting regression and sparsity regularization for multi-view classification
Wang, Z., Men, M., & Zhong, P. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02860-y · Cited by 8
Model-Free UAV Navigation in Unknown Complex Environments Using Vision-Based Reinforcement Learning
Wu, H., Wang, W., Wang, T., et al. (2025) · Drones · doi:10.3390/drones9080566 · Cited by 8
Fully Unsupervised Domain-Agnostic Image Retrieval
Zheng, Z., Ren, H., Wu, Y., Zhang, W., Lu, H., Yang, Y., & Shen, H. T. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3335147 · Cited by 9
“These methods often involve handcrafted descriptors [36], [37], [38] or learnable feature representations [35], [39].”
MethodFeature Fusion and Multi-Stream CNNs for ScaleAdaptive Multimodal Sign Language Recognition
Singla, N., Taneja, M., Goyal, N., et al. (2023) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs57279.2023.10112915 · Cited by 8
Homographic p-norms: Metrics of homographic image transformation
Je, C., & Park, H. (2015) · Signal Processing Image Communication · doi:10.1016/j.image.2015.08.009 · Cited by 8
Williams, A. B., Touretzky, D. S., Tira-Thompson, E. J., et al. (2008) · ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/1352135.1352303 · Cited by 8
Tagging and retrieving images with co-occurrence models
Garg, N., & Gática-Pérez, D. (2009) · doi:10.1145/1631058.1631077 · Cited by 8
Which Parts Determine the Impression of the Font?
Ueda, M., Kimura, A., & Uchida, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86334-0_47 · Cited by 8
Stereo vision based swing angle sensor for mining rope shovel
Lin, L.-H., Lawrence, P. D., & Hall, R. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5650457 · Cited by 8
Multi-meter Intelligent Detection and Recognition Method under Complex Background
Ni, T., Miao, H., Wang, L., Ni, S., & Huang, L. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9189383 · Cited by 8
Lightweight identification of retail products based on improved convolutional neural network
Wang, J., Huang, C., Zhao, L., et al. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12872-6 · Cited by 8
IMAGE-BASED LOCALIZATION FOR INDOOR ENVIRONMENT USING MOBILE PHONE
Huang, Y., Wang, H., Zhan, K., et al. (2015) · 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/isprsarchives-xl-4-w5-211-2015 · Cited by 8
An Accurate Multi-Row Panorama Generation Using Multi-Point Joint Stitching
Zheng, J. G., Zhang, Z., Tao, Q., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2829082 · Cited by 8
Beyond bag of latent topics: spatial pyramid matching for scene category recognition
Lu, F., & Huang, J. (2015) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1500070 · Cited by 8
Discriminative Features for Image Classification and Retrieval
Liu, S., & Bai, X. P. (2011) · International Conference on Image and Graphics · doi:10.1109/icig.2011.149 · Cited by 9
Automatic fast feature-level image registration for high-resolution remote sensing images
Mengmeng, H. E., Guo, Q., Li, A., et al. (2018) · National Remote Sensing Bulletin · doi:10.11834/jrs.20186420 · Cited by 8
EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD
Li, W., & Yang, M. Y. (2016) · 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/isprsarchives-xli-b3-633-2016 · Cited by 8
Specific and Class Object Recognition for Service Robots through Autonomous and Interactive Methods
Mansur, A., & Kuno, Y. (2008) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e91-d.6.1793 · Cited by 8
Brain computer interface for interactive and intelligent image search and retrieval
Kumar, S., & Sahin, F. (2013) · International Symposium on High-capacity Optical Networks and Enabling Technologies · doi:10.1109/honet.2013.6729772 · Cited by 8
A non-parametric unsupervised approach for content based image retrieval and clustering
Makantasis, K., Doulamis, A. D., & Doulamis, N. D. (2013) · doi:10.1145/2510650.2510656 · Cited by 8
Segmentation of color images for interactive 3D object retrieval
Moya, J. P. A., & Kraiss, K. (2004) · Cited by 8
Zhang, J., Rao, Y., Zhang, J., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2950701 · Cited by 8
Towards AUV docking on sub-sea structures
Krupiński, S., Maurelli, F., Mallios, A., et al. (2009) · doi:10.1109/oceanse.2009.5278144 · Cited by 8
ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds
Bökman, G., Kahla, F., & Flinth, A. (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/cvpr52688.2022.01070 · Cited by 9
An Automated Refereeing and Analysis Tool for the Four-Legged League
Ruíz-del-Solar, J., Loncomilla, P., & Vallejos, P. A. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74024-7_18 · Cited by 8
Fast stereo conformer: Real-time stereo matching with enhanced feature fusion for autonomous driving
Lu, Y., He, X., Zhang, Q., & Zhang, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110565 · Cited by 8
Automated pollen identification system for forensic geo-historical location applications
Hwang, G. M., Riley, K. C., Christou, C. T., et al. (2013) · IEEE International Conference on Technologies for Homeland Security · doi:10.1109/ths.2013.6699018 · Cited by 8
A Local Characteristic Image Restoration Based on Convolutional Neural Network
Lyu, G., Yin, H., Yu, X., et al. (2016) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016edl8021 · Cited by 8
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency DetectionPreprint
Zhang, K., Li, T., Shen, S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06167 · Cited by 8
A Thermal Infrared Pedestrian-Detection Method for Edge Computing Devices
You, S., Ji, Y., Liu, S., Mei, C., Yao, X., & Feng, Y. (2022) · Sensors · doi:10.3390/s22176710 · Cited by 8
ANSAC: Adaptive Non-Minimal Sample and Consensus
Fragoso, V., Sweeney, C., Sen, P., et al. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.43 · Cited by 8
Learning multiple codebooks for low bit rate mobile visual search
Lin, J., Duan, L., Chen, J., et al. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288038 · Cited by 8
Xu, X., Mei, T., Zeng, W., et al. (2012) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2382336.2382340 · Cited by 8
Wang, Y., & Wang, X. (2021) · Measurement · doi:10.1016/j.measurement.2021.109086 · Cited by 8
Autonomous segmentation of Near-Symmetric objects through vision and robotic nudging
Li, W. H., & Kleeman, L. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4650846 · Cited by 8
PT-Net: Pyramid Transformer Network for Feature Matching Learning
Gong, Z., Xiao, G., Shi, Z., et al. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3369132 · Cited by 8
Expanded bag of words representation for object classification
Liu, T., Liu, J., Liu, Q., & Lu, H. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5413588 · Cited by 8
Cloud Instance Management and Resource Prediction For Computation-as-a-Service Platforms
Doyle, J., Giotsas, V., Anam, M. A., & Andreopoulos, Y. (2016) · Lancaster EPrints (Lancaster University) · Cited by 8
Robust visual tracking based on product sparse coding
Hong-tu, H., Du-yan, B., Yu-fei, Z., et al. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.01.014 · Cited by 8
Automatic Estimation of the Inlier Threshold in Robust Multiple Structures Fitting
Toldo, R., & Fusiello, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04146-4_15 · Cited by 8
Petschko, H., Zehner, M., Fischer, P., et al. (2022) · Remote Sensing · doi:10.3390/rs14133058 · Cited by 8
Abràmoff, M. D., & Kay, C. N. (2012) · Elsevier eBooks · doi:10.1016/b978-1-4557-0737-9.00006-0 · Cited by 8
End-to-end learning of self-rectification and self-supervised disparity prediction for stereo vision
Zhang, X., Zhao, Y., Wang, H., Zhai, H., Sun, H., & Zheng, N. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.095 · Cited by 8
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
