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 16,601–16,700 of 56,201
Park, S. I., Bişğin, H., Ding, H., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157940 · Cited by 16
Dynamic texture and geometry features for facial expression recognition in video
Chen, J., Chen, Z., Chi, Z., & Fu, H. C. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351752 · Cited by 17
Vision Based Object Counting Using Speeded Up Robust Features for Inventory Control
Verma, N. K., Sharma, T., Sevakula, R. K., & Salour, A. (2016) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2016.0139 · Cited by 17
Retinal image preprocessing, enhancement, and registration
Hernandez-Matas, C., Argyros, A., & Zabulis, X. (2019) · Elsevier eBooks · doi:10.1016/b978-0-08-102816-2.00004-6 · Cited by 16
The Retrieval of the Beautiful
Wang, J., Zhu, S., Xu, J., & Cao, D. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3356059 · Cited by 16
Representing local binary descriptors with BossaNova for visual recognition
Caetano, C., Avila, S., Guimarães, S. J. F., et al. (2014) · ACM Symposium on Applied Computing · doi:10.1145/2554850.2555058 · Cited by 16
Using Language to Drive the Perceptual Grouping of Local Image Features
Jamieson, M., Dickinson, S., Stevenson, S., et al. (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.2006.325 · Cited by 16
Data Association and Localization of Classified Objects in Visual SLAM
Iqbal, A., & Gans, N. (2020) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-020-01189-x · Cited by 16
HLD: A robust descriptor for line matching
Wang, Z., Liu, H., & Wu, F. (2009) · International Conference on Computer-Aided Design and Computer Graphics (CAD/Graphics) · doi:10.1109/cadcg.2009.5246918 · Cited by 17
CHOSEN ASPECTS OF THE PRODUCTION OF THE BASIC MAP USING UAV IMAGERY
Kędzierski, M., Fryśkowska-Skibniewska, A., Wierzbicki, D., et al. (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/isprs-archives-xli-b1-873-2016 · Cited by 16
Multiscale analysis method for profiled composite structures considering the forming process
Liu, C., Ge, J., Zhao, S., et al. (2024) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2024.106014 · Cited by 16
Active Multimodal Sensor System for Target Recognition and Tracking
Qu, Y., Zhang, G., Zou, Z., Liu, Z., & Mao, J. (2017) · Sensors · doi:10.3390/s17071518 · Cited by 16
Exploring the topic evolution of Dunhuang murals through image classification
Zeng, Z., Sun, S., Li, T., Yin, J., Shen, Y., & Huang, Q. (2022) · Journal of Information Science · doi:10.1177/01655515221074336 · Cited by 17
Computer Vision for Mobile Augmented Reality
Turk, M., & Fragoso, V. (2015) · Springer eBooks · doi:10.1007/978-3-319-24702-1_1 · Cited by 16
Tensor-SIFT Based Earth Mover’s Distance for Contour Tracking
Li, P. (2012) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-012-0369-4 · Cited by 16
Accurate Affine Invariant Image Matching Using Oriented Least Square
Sedaghat, A., & Ebadi, H. (2015) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.81.9.733 · Cited by 16
Ecological monitoring of invasive species through deep learning-based object detection
Ji, X., Zhang, Y., Wan, J., Yang, T., Yang, Q., & Liao, J. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113572 · Cited by 16
Bringing Semantics in Word Image Retrieval
Krishnan, P., & Jawahar, C. V. (2013) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2013.150 · Cited by 16
A Deep-Learning-Based Multimodal Data Fusion Framework for Urban Region Function Recognition
Yu, M., Xu, H., Zhou, F., Xu, S., & Yin, H. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12120468 · Cited by 20
Luévano, L. S., Chang, L., Méndez-Vázquez, H., Martínez-Díaz, Y., & González-Mendoza, M. (2021) · IEEE Access · doi:10.1109/access.2021.3080712 · Cited by 21
Joint Dictionary and Classifier Learning for Categorization of Images Using a Max-margin Framework
Löbel, H., Vidal, R., Mery, D., & Soto, Á. (2014) · Lecture notes in computer science · doi:10.1007/978-3-642-53842-1_8 · Cited by 16
Combining 3D face representations using region covariance descriptors and statistical models
Križaj, J., Štruc, V., & Dobrišek, S. (2013) · IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2013.6553816 · Cited by 16
NeRF-HuGS: Improved Neural Radiance Fields in Non-static Scenes Using Heuristics-Guided Segmentation
Chen, J., Qin, Y., Liu, L., Lu, J., & Li, G. (2024) · 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/cvpr52733.2024.01838 · Cited by 22
“COLMAP uses SIFT [27] to extract image features, and we set COLMAP’s parameters to default values.”
MethodFine‐grained recognition of maritime vessels and land vehicles by deep feature embedding
Solmaz, B., Gündoğdu, E., Yücesoy, V., Koç, A., & Alatan, A. A. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5187 · Cited by 16
Adaptive Fusion of Heterogeneous Manifolds for Subspace Clustering
Wang, B., Hu, Y., Gao, J., et al. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3011717 · Cited by 16
Automatic image registration by local descriptors in remote sensing
Liu, X., Yang, J., & Shen, H.-B. (2008) · Optical Engineering · doi:10.1117/1.2969121 · Cited by 16
Tracking Registration Algorithm for Augmented Reality Based on Template Tracking
Cao, P.-X., Li, W., & Ma, W. (2019) · Machine Intelligence Research · doi:10.1007/s11633-019-1198-3 · Cited by 16
A survey on traditional and deep learning copy move forgery detection (CMFD) techniques
Elaskily, M. A., Dessouky, M. M., Faragallah, O. S., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14424-y · Cited by 16
Image copy-move forgery detection using hierarchical feature point matching
Li, Y., & Zhou, J. (2016) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2016.7820758 · Cited by 16
An Improved RANSAC Algorithm Based on Similar Structure Constraints
Zhu, W., Sun, W., Wang, Y., et al. (2016) · International Conference on Robots & Intelligent System · doi:10.1109/icris.2016.19 · Cited by 16
Deep CNN based online image deduplication technique for cloud storage system
Kaur, R., Bhattacharya, J., & Chana, I. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13182-7 · Cited by 16
The influence of software algorithms on photogrammetric micro-feature measurement’s uncertainty
Lavecchia, F., Guerra, M. G., & Galantucci, L. M. (2017) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0786-z · Cited by 16
Image Analysis Techniques to Accompany a new In Situ Ichthyoplankton Imaging System
Tsechpenakis, G., Guigand, C. M., & Cowen, R. K. (2007) · OCEANS 2007 - Europe · doi:10.1109/oceanse.2007.4302271 · Cited by 16
An annotated grain kernel image database for visual quality inspection
Fan, L., Ding, Y., Fan, D., Wu, Y., Chu, H., Pagnucco, M., & Song, Y. (2023) · Scientific Data · doi:10.1038/s41597-023-02660-8 · Cited by 25
IMAGE STITCHING WITH PERSPECTIVE-PRESERVING WARPING
Xiang, T.-Z., Xia, G.-S., & Zhang, L. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-iii-3-287-2016 · Cited by 16
A new productive framework for point‐based matching of oblique aircraft and UAV‐based images
Baş, S., & Ok, A. Ö. (2021) · The Photogrammetric Record · doi:10.1111/phor.12374 · Cited by 16
Zhuang, F., Li, X., Jin, X. Y., Zhang, D., Qiu, L., & He, Q. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2732818 · Cited by 15
Optical-to-SAR Image Matching Using Multiscale Masked Structure Features
Ye, Y., Yang, C., Zhang, J., et al. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3171265 · Cited by 16
Learning weighted Hamming distance for binary descriptors
Fan, B., Kong, Q., Yuan, X., et al. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638084 · Cited by 16
Jha, S., Li, Y., Noghabi, S. A., Ranganathan, V. N., Kumar, P., Nelson, A., Toelle, M., Sinha, S. N., Chandra, R., & Badam, A. (2021) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3447993.3483273 · Cited by 20
Object Detecting Augmented Reality System
Hu, M., Weng, D., Chen, F., et al. (2020) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct50939.2020.9295761 · Cited by 16
Manifold Regularization Graph Structure Auto-Encoder to Detect Loop Closure for Visual SLAM
Wang, Z., Peng, Z., Guan, Y., & Wu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2914943 · Cited by 16
Sketch-based aerial image retrieval
Jiang, T., Xia, G.-S., & Lu, Q. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296971 · Cited by 16
Localization estimation based on Extended Kalman filter using multiple sensors
Hoang, V.-D., Le, M.-H., Hernández, D. C., & Jo, K.-H. (2013) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2013.6700032 · Cited by 18
Research on Real-Time Disconnector State Evaluation Method Based on Multi-Source Images
Huang, S., Shang, B., Song, Y., et al. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3137864 · Cited by 16
An Optimized Scale-Invariant Feature Transform Using Chamfer Distance in Image Matching
Al-Shurbaji, T., Kaabneh, K., Alhadid, I. H., & Masa’deh, R. M. T. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.019654 · Cited by 15
A Bag of Words Based Approach for Classification of HEp-2 Cell Images
Ensafi, S., Lu, S., Kassim, A. A., et al. (2014) · doi:10.1109/i3a.2014.12 · Cited by 16
Digital Multimedia Perception and Design
(2006) · IGI Global eBooks · doi:10.4018/978-1-59140-860-4 · Cited by 16
Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
Kaya, H., & Salah, A. A. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-98131-4_10 · Cited by 18
Deep Image Similarity Measurement Based on the Improved Triplet Network with Spatial Pyramid Pooling
Yuan, X., Liu, Q., Long, J., Hu, L., & Wang, Y. (2019) · Information · doi:10.3390/info10040129 · Cited by 16
A Picture is Worth a Thousand Words: Share Your Real-Time View on the Road
Wang, X., Ding, L., Wang, Q., et al. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2592685 · Cited by 16
Implicit Negative Sub-Categorization and Sink Diversion for Object Detection
Li, Y., Tang, S., Lin, M., Zhang, Y., Li, J., & Yan, S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2779270 · Cited by 14
Attention-Enhanced Cross-Modal Localization Between Spherical Images and Point Clouds
Zhao, Z., Yu, H., Lyu, C., Yang, W. T., & Scherer, S. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3306377 · Cited by 20
Vedaldi, A., Ling, H., & Soatto, S. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-12848-6_2 · Cited by 16
Geometric Evaluation of Mobile-Phone Camera Images for 3D Information
Yılmaztürk, F., & Gurbak, A. E. (2019) · International Journal of Optics · doi:10.1155/2019/8561380 · Cited by 16
MASIC: Deep Mask Stereo Image Compression
Deng, X., Deng, Y., Yang, R., et al. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3253702 · Cited by 16
SHREC’08 entry: Local volumetric features for 3D model retrieval
Osada, K., Furuya, T., & Ohbuchi, R. (2008) · IEEE International Conference on Shape Modeling and Applications · doi:10.1109/smi.2008.4547989 · Cited by 16
Photogrammetric Processing of Tianwen-1 HiRIC Imagery for Precision Topographic Mapping on Mars
Li, Z., Wu, B., Liu, W. C., et al. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3194081 · Cited by 16
Identifying Surprising Events in Videos Using Bayesian Topic Models
Hendel, A., Weinshall, D., & Peleg, B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19318-7_35 · Cited by 16
Topic modeling and improvement of image representation for large-scale image retrieval
Tu, N. A., Dinh, D.-L., Rasel, M. K., & Lee, Y.-K. (2016) · Information Sciences · doi:10.1016/j.ins.2016.05.029 · Cited by 16
Spherical Kernel for Efficient Graph Convolution on 3D Point CloudsPreprint
Lei, H., Akhtar, N., & Mian, A. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.09287 · Cited by 16
Research on optimization of image fast feature point matching algorithm
Wu, M. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0354-y · Cited by 16
Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry
Gladkova, M., Wang, R., Zeller, N., & Cremers, D. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561217 · Cited by 17
Sector-ring HOG for rotation-invariant human detection
Liu, B., Wu, H., Su, W., & Sun, J. (2017) · Signal Processing Image Communication · doi:10.1016/j.image.2017.02.008 · Cited by 16
Stanciu, S. G., Hristu, R., Boriga, R. E., & Stanciu, G. A. (2010) · Microscopy and Microanalysis · doi:10.1017/s1431927610000371 · Cited by 16
Gazzea, M., Sommervold, O., & Arghandeh, R. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3277550 · Cited by 16
Image Stitching With Manifold Optimization
Zhang, L., & Huang, H. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3161839 · Cited by 16
Rotation Invariant Co-occurrence Matrix Features
Putzu, L., & Ruberto, C. D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68560-1_35 · Cited by 16
A non-rigid image registration method based on multi-level B-spline and L2-regularization
Ji, H., Li, Y., Dong, E., et al. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1274-0 · Cited by 16
New and robust composite micro structure descriptor (CMSD) for CBIR
Umamaheswaran, S., Lakshmanan, R., Vinothkumar, V., Arvind, K. S., & Nagarajan, S. (2019) · International Journal of Speech Technology · doi:10.1007/s10772-019-09663-0 · Cited by 16
Change Detection with Global Viewpoint Localization
Murase, T., Kanji, T., & Takayama, A. (2017) · IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2017.21 · Cited by 16
An adversarial optimization approach to efficient outlier removal
Yu, J., Eriksson, A. P., Chin, T.-J., et al. (2011) · International Conference on Computer Vision · doi:10.1109/iccv.2011.6126268 · Cited by 16
Fast stereo-based visual odometry for rover navigation
Cumani, A., & Guiducci, A. (2008) · WSEAS Transactions on Circuits and Systems archive · Cited by 16
Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization
Xu, Y., Zhang, J., & Tao, D. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00480 · Cited by 18
Heat Diffusion Long-Short Term Memory Learning for 3D Shape Analysis
Zhu, F., Xie, J., & Fang, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_19 · Cited by 14
Deployment of a deep-learning based multi-view stereo approach for measurement of ship shell plates
He, P., Hu, D., & Hu, Y. L. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.111968 · Cited by 16
Image retrieval by dense caption reasoning
Wei, X., Qi, Y., Liu, J., et al. (2017) · IEEE Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2017.8305157 · Cited by 16
Collaborative multi-view K-means clustering
Bettoumi, S., Jlassi, C., & Arous, N. (2017) · Soft Computing · doi:10.1007/s00500-017-2801-6 · Cited by 16
Evaluation of Intensity and Color Corner Detectors for Affine Invariant Salient Regions
Sebe, N., Gevers, T., Dijkstra, S., & van de Weijer, J. (2006) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2006.75 · Cited by 16
Implementation of Workpiece Recognition and Location Based on Opencv
Huang, C., Chen, D., & Tang, X. (2015) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2015.143 · Cited by 16
Generalized Descriptor Compression for Storage and Matching
Johnson, M. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.23 · Cited by 15
Wan, X., Liu, J. G., Li, S., et al. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907933 · Cited by 16
Product identification in retail stores by combining faster r-cnn and recurrent neural network
Ghosh, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15633-1 · Cited by 16
A Review of Co-saliency Detection Technique: Fundamentals, Applications, and ChallengesPreprint
Zhang, D., Fu, H., Han, J., Borji, A., & Li, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.07090 · Cited by 16
Medical Image Retrieval and Automatic Annotation: OHSU at ImageCLEF 2007
Kalpathy–Cramer, J., & Hersh, W. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85760-0_79 · Cited by 15
Ananthi, M., Rajkumar, P. R., Sabitha, R., & Karthik, S. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.10.011 · Cited by 15
Video segmentation into scenes using entropy and SURF
Baber, J., Afzulpurkar, N., & Bakhtyar, M. (2011) · International Conference on Emerging Technologies · doi:10.1109/icet.2011.6048496 · Cited by 15
Zheng, H., Lefebvre, G., & Laurent, C. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2007.911741 · Cited by 15
Evolving fuzzy image segmentation
Othman, A. A., & Tizhoosh, H. R. (2011) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzzy.2011.6007601 · Cited by 15
ERODE: An efficient and robust outlier detector and its application to stereovisual odometry
Moreno, F.-Á., Blanco, J., & González-Jiménez, J. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631245 · Cited by 15
Image-based pose estimation for 3-D modeling in rapid, hand-held motion
Strobl, K. H., Mair, E., & Hirzinger, G. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979944 · Cited by 15
Feature Based Correspondence: A Comparative Study on Image Matching Algorithms
Muhammad, U., Tanvir, M., & Khurshid, K. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070329 · Cited by 14
Object Recognition via Local Patch Labelling
Bishop, C., & Ulusoy, İ. (2005) · Lecture notes in computer science · doi:10.1007/11559887_1 · Cited by 15
VSFormer: Visual-Spatial Fusion Transformer for Correspondence Pruning
Liao, T., Zhang, X., Zhao, L., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28123 · Cited by 15
Meo, R., Roglia, E., & Bottino, A. (2012) · Sensors · doi:10.3390/s121217504 · Cited by 15
Pedestrian walking speed monitoring at street scale by an in-flight drone
Jiao, D., & Fei, T. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1226 · Cited by 17
Automated integration of facial and intra-oral images of anterior teeth
Li, M., Xu, X., Punithakumar, K., et al. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103794 · Cited by 15
Illumination invariant representation for privacy preserving face identification
Moskovich, B., & Osadchy, M. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5544620 · Cited by 15
Comparative study of histogram distance measures for re-identificationPreprint
Marín-Reyes, P. A., Lorenzo-Navarro, J., & Castrillón-Santana, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08134 · Cited by 15
Burkhardt, M. R., Soper, T. D., Yoon, W. J., et al. (2013) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2013.2237783 · Cited by 15
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
