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 15,801–15,900 of 56,202
MultiScene: A Large-Scale Dataset and Benchmark for Multiscene Recognition in Single Aerial Images
Hua, Y., Mou, L., Jin, P., & Zhu, X. X. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3110314 · Cited by 19
Joint Compression of Near-Duplicate Videos
Wang, H., Tian, T., Ma, M., et al. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2645398 · Cited by 17
Probabilistic optimized ranking for multimedia semantic concept detection via RVM
Zheng, Y.-T., Neo, S.-Y., Chua, T., et al. (2008) · doi:10.1145/1386352.1386378 · Cited by 17
Structure tensor‐based SIFT algorithm for SAR image registration
Divya, S., Paul, S., & Pati, U. C. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2019.0568 · Cited by 17
Wavelet SIFT Feature Descriptors for Robust Face Recognition
Kumar, N. A. M., & Sathidevi, P. S. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-31552-7_87 · Cited by 17
Super resolution based on scale invariant feature transform
Yuan, Z. J., Yan, P., & Li, S. (2008) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2008.4590265 · Cited by 17
Video-rate recognition and localization for wearable cameras
Castle, R. O., Gawley, D., Klein, G., & Murray, D. W. (2007) · British Machine Vision Conference (BMVC) · doi:10.5244/c.21.112 · Cited by 17
Han, F. S., Liu, S., Liu, S., Zou, J., Ai, Y., & Xu, C. (2020) · International Conference on Electronic Packaging Technology (ICEPT) · doi:10.1109/icept50128.2020.9202566 · Cited by 20
Multi-view support vector ordinal regression with data uncertainty
Xiao, Y., Li, X., Liu, B., et al. (2022) · Information Sciences · doi:10.1016/j.ins.2021.12.128 · Cited by 17
Scale invariant texture classification via sparse representation
Sun, X., Wang, J., She, M. F. H., et al. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.06.016 · Cited by 17
Holons Visual Representation for Image Retrieval
Dong, L., Liang, Y., Kong, G., Zhang, Q., Cao, X., & Ebroul Izquierdo, M. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2530399 · Cited by 17
Çabo, C., Ordóñez, C., Muñiz‐Calvente, M., et al. (2019) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2019.06.083 · Cited by 17
Half-CNN: A General Framework for Whole-Image RegressionPreprint
Yuan, J., Ni, B., & Kassim, A. A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6885 · Cited by 20
Li, Y., Chen, Y., Zhang, J., et al. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3512600 · Cited by 17
“This process is critical for image alignment and 3D reconstruction. scale invariant feature transform (SIFT) [87] is a widely used method for keypoint detection.”
Stereo computation combining structured light and passive stereo matching
Pribanić, T., Obradovic, N., & Salví, J. (2011) · Optics Communications · doi:10.1016/j.optcom.2011.10.045 · Cited by 17
Fast and robust feature-based stitching algorithm for microscopic images
Mohammadi, F. S., Shabani, H., & Zarei, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-61970-y · Cited by 17
“Various feature detection methods have been used in literature, and SIFT 24 , SURF 25 , Harris 26 , BRISK 27 , and Shi-Thomasi 28 are the most popular.”
MethodOn the usability of different optical measuring techniques for joint roughness evaluation
Marsch, K., Wujanz, D., & Fernández-Steeger, T. M. (2019) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-019-01606-y · Cited by 17
Tella, M., Daga, P., Chadebecq, F., et al. (2016) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.72 · Cited by 17
An Advanced Rotation Invariant Descriptor for SAR Image Registration
Xiang, Y., Wang, F., Wan, L., et al. (2017) · Remote Sensing · doi:10.3390/rs9070686 · Cited by 17
Surface expressions of morphostructural features at Hasandağ stratovolcano on DEM datasets
Ulusoy, İ., Diker, C., Şen, E., et al. (2020) · Mediterranean Geoscience Reviews · doi:10.1007/s42990-020-00044-3 · Cited by 17
Hua, Y., Mou, L., & Zhu, X. X. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519576 · Cited by 17
Image classification with Bag-of-Words model based on improved SIFT algorithm
Gao, H., Dou, L., Chen, W., et al. (2013) · Asian Control Conference (ASCC) · doi:10.1109/ascc.2013.6606268 · Cited by 17
Traversable Region Estimation for Mobile Robots in an Outdoor Image
Matsuzaki, S., Yamazaki, K., Hara, Y., et al. (2018) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0760-x · Cited by 17
Liu, R., Zhu, H., Wang, L., Han, B., Du, J., & Jia, Y. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103532 · Cited by 15
High Level Video Temporal Segmentation
Țapu, R., & Zaharia, T. B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24028-7_21 · Cited by 17
A novel approach for salient image regions detection and description
Vázquez-Martín, R., Marfil, R., Núñez, P., Bandera, A. J., & Sandoval, F. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.08.003 · Cited by 17
Multiscale Incremental Dictionary Learning With Label Constraint for SAR Object Recognition
Tao, L., Jiang, X., Li, Z., et al. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2868789 · Cited by 17
Targetless Extrinsic Calibration of Stereo, Thermal, and Laser Sensors in Structured Environments
Fu, T., Yu, H., Yang, W. T., et al. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3204338 · Cited by 17
Wheelchair Detection Using Cascaded Decision Tree
Huang, C.-R., Chung, P., Lin, K.-W., & Tseng, S.-C. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2037618 · Cited by 17
Hwang, S., Kobayashi, K., & Sugiyama, J. (2020) · Journal of Wood Science · doi:10.1186/s10086-020-01864-5 · Cited by 17
Industrial Printing Image Defect Detection Using Multi‐Edge Feature Fusion Algorithm
Liu, B., Chen, Y., Xie, J., & Chen, B. (2021) · Complexity · doi:10.1155/2021/2036466 · Cited by 19
Automatic Registration of Images to Untextured Geometry Using Average Shading Gradients
Plötz, T., & Roth, S. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1022-x · Cited by 17
A probabilistic analysis of a common RANSAC heuristic
Sangappa, H. K., & Ramakrishnan, K. R. (2018) · Machine Vision and Applications · doi:10.1007/s00138-018-0973-4 · Cited by 17
Toward Accurate Sensing with Knitted Fabric: Applications and Technical Considerations
Vallett, R., McDonald, D. Q., Dion, G., et al. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3394981 · Cited by 17
Hu, Y. (2010) · Mathematical and Computer Modelling · doi:10.1016/j.mcm.2010.11.016 · Cited by 17
Variation in trunk taper of buttressed trees within and among five lowland tropical forests
Cushman, K. C., Bunyavejchewin, S., Cárdenas, D., et al. (2021) · Biotropica · doi:10.1111/btp.12994 · Cited by 17
Airborne Feature Matching Velocimetry for surface flow measurements in rivers
Cao, L., Weitbrecht, V., Li, D., et al. (2020) · Journal of Hydraulic Research · doi:10.1080/00221686.2020.1818309 · Cited by 17
Robust Non-parametric Data Fitting for Correspondence Modeling
Lin, W.-Y., Cheng, M., Zheng, S., et al. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.295 · Cited by 17
Chest Radiograph Pathology Categorization via Transfer Learning
Diamant, I., Bar, Y., Geva, O., Wolf, L., Zimmerman‐Moreno, G., Lieberman, S., Konen, E., & Greenspan, H. K. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-810408-8.00018-3 · Cited by 18
Cascade Semantic Fusion for Image Captioning
Wang, S., Lan, L., Zhang, X., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2917979 · Cited by 17
Li, Y., & Lim, J. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291368 · Cited by 17
Video pornography detection through deep learning techniques and motion information
Pérez, M. (2016) · doi:10.47749/t/unicamp.2016.973327 · Cited by 17
Gloudemans, D., Zachár, G., Wang, Y., Ji, J., Nice, M. W., Bunting, M., Barbour, W., Sprinkle, J. M., Piccoli, B., Monache, M. L. D., et al. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00447 · Cited by 17
An efficient real-time FPGA implementation for object detection
Zhao, J. K., Huang, X., & Massoud, Y. (2014) · IEEE International New Circuits and Systems Conference (NEWCAS) · doi:10.1109/newcas.2014.6934045 · Cited by 17
Conjugate rotation: Parameterization and estimation from an affine feature correspondence
Köser, K., Beder, C., & Koch, R. (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.2008.4587796 · Cited by 17
Higgins, R. E. L., Fouhey, D. F., Antiochos, S. K., et al. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac42d5 · Cited by 17
Alberts, E., Tetteh, G., Trebeschi, S., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67675-3_18 · Cited by 17
Cheng, Y., Jin, S., Wang, M., et al. (2017) · Remote Sensing Letters · doi:10.1080/2150704x.2017.1349960 · Cited by 17
Asymmetric Joint Learning for Heterogeneous Face Recognition
Cao, B., Wang, N., Gao, X., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12226 · Cited by 17
Li, H., Li, X., Ding, W., & Huang, Y. (2015) · Remote Sensing · doi:10.3390/rs71012606 · Cited by 17
Interframe Coding of Canonical Patches for Mobile Augmented Reality
Makar, M. A., Tsai, S. S., Chandrasekhar, V., et al. (2012) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2012.18 · Cited by 17
An End‐to‐End Deep Learning Approach for Plate Recognition in Intelligent Transportation Systems
Pirgazi, J., Kallehbasti, M. M. P., & Sorkhi, A. G. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/3364921 · Cited by 17
Geometry and Topology Preserving Hashing for SIFT Feature
Chen, K., Zhu, L., Qian, X., Han, J., Wang, M., & Tang, Y. Y. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2883868 · Cited by 17
Dog Identification Method Based on Muzzle Pattern Image
Jang, D.-H., Kwon, K.-S., Kim, J.-K., et al. (2020) · Applied Sciences · doi:10.3390/app10248994 · Cited by 17
Endoscopic Video Texture Mapping on Pre-Built 3-D Anatomical Objects Without Camera Tracking
Wang, X., Zhang, Q., Han, Q., et al. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2028341 · Cited by 17
Low-rank sparse coding and region of interest pooling for dynamic 3D facial expression recognition
Zarbakhsh, P., & Demirel, H. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1318-5 · Cited by 17
Sparse Spatial Coding: A Novel Approach to Visual Recognition
Oliveira, G. L., Nascimento, E. R., Vieira, A. W., & Campos, M. F. M. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2317988 · Cited by 16
Keypoints-Based Image Passive Forensics Method for Copy-Move Attacks
Wang, X., He, G., Tang, C. L., et al. (2015) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001416550089 · Cited by 17
Nonnegative correlation coding for image classification
Dong, Z., Liang, W. G., Wu, Y., Pei, M., & Jia, Y. (2015) · Science China Information Sciences · doi:10.1007/s11432-015-5289-7 · Cited by 17
Kulyukin, V., Nicholson, J. A., & Coster, D. C. (2008) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/1414471.1414518 · Cited by 17
Bennour, A., Boudraa, M., Siddiqi, I., Al-Sarem, M., Al-Shabi, M., & Ghabban, F. M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18187-y · Cited by 18
Edge information based crater detection and matching for lunar exploration
He, J., Cui, H., & Junhua, F. (2010) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip.2010.5565284 · Cited by 18
A simultaneous sample-and-filter strategy for robust multi-structure model fitting
Wong, H. S., Chin, T.-J., Yu, J., et al. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.08.007 · Cited by 17
Fast measurement of temporal noise of digital camera's photosensors
Аркадьевич, Ч. П., Evtikhiev, N. N., Krasnov, V. V., Rodin, V. G., Starikov, R. S., & Starikov, S. N. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2194979 · Cited by 17
Pose-aware Multi-feature Fusion Network for Driver Distraction Recognition
Wu, M., Zhang, X., Shen, L., & Yu, H. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413337 · Cited by 19
Crater matching algorithm based on feature descriptor
Shao, W., Xie, J., Cao, L., Leng, J., & Wang, B. (2020) · Advances in Space Research · doi:10.1016/j.asr.2019.09.031 · Cited by 17
Real-time Seamless Image Stitching in Autonomous Driving
Kinzig, C., Cortés, I., Fernández, C., et al. (2022) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion49751.2022.9841307 · Cited by 17
Optimizing the automated recognition of individual animals to support population monitoring
de Lorm, T. A., Horswill, C., Rabaiotti, D., Ewers, R. M., Groom, R. J., Watermeyer, J. P., & Woodroffe, R. (2023) · Ecology and Evolution · doi:10.1002/ece3.10260 · Cited by 17
An end-to-end system for free viewpoint video for smooth camera transitions
Goorts, P., Dumont, M., Rogmans, S., & Bekaert, P. (2012) · International Conference on D Imaging · doi:10.1109/ic3d.2012.6615136 · Cited by 16
SmartWoodID—an image collection of large end-grain surfaces to support wood identification systems
Blaere, R. D., Lievens, K., Hassel, D. V., et al. (2023) · Database · doi:10.1093/database/baad034 · Cited by 17
Bourouis, S., Al-Osaimi, F. R., Bouguila, N., et al. (2018) · Soft Computing · doi:10.1007/s00500-018-3244-4 · Cited by 17
Terrain Mapping and Landing Operations Using Vision Based Navigation Systems
Majji, M., Davis, J., Doebbler, J., et al. (2011) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2011-6581 · Cited by 17
Real-time panorama canvas of natural images
Kim, B., Lee, S., & Cho, N. I. (2011) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2011.6131177 · Cited by 17
Fast Frequency Domain Screen-Shooting Watermarking Algorithm Based on ORB Feature Points
Bai, Y., Li, L., Zhang, S., et al. (2023) · Mathematics · doi:10.3390/math11071730 · Cited by 17
Wierzbicki, D., & Nienaltowski, M. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8020083 · Cited by 17
High Accuracy Feature Detection for Camera Calibration: A Multi-steerable Approach
Mühlich, M., & Aach, T. (2007) · Springer eBooks · doi:10.1007/978-3-540-74936-3_29 · Cited by 17
Underwater Image Enhancement Based on Dehazing and Color Correction
Li, H., Zhuang, P., Wang, W., et al. (2019) · IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) · doi:10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00196 · Cited by 17
Anubhove, M. S. T., Ashrafi, N., Saleque, A. M., et al. (2020) · International Conference on Computational Performance Evaluation (ComPE) · doi:10.1109/compe49325.2020.9200129 · Cited by 17
Image mosaicing via quadric surface estimation with priors for tunnel inspection
Chaiyasarn, K., Kim, T., Viola, F., Cipolla, R., & Soga, K. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5413902 · Cited by 19
Image Classification and Object Detection Algorithm Based on Convolutional Neural Network
Group of Network Computation, D. O. M. A. C., & Leonard, J. K. (2019) · Science Insights · doi:10.15354/si.19.re117 · Cited by 20
Refinedbox: Refining for fewer and high-quality object proposals
Liu, Y., Li, S., & Cheng, M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.017 · Cited by 15
Automatic detection of anatomical landmarks on the knee joint using MRI data
Xue, N., Döllinger, M., Ho, C. P., et al. (2014) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24516 · Cited by 17
Efficient Nearest Neighbors via Robust Sparse Hashing
Cherian, A., Sra, S., Morellas, V., & Papanikolopoulos, N. P. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2324280 · Cited by 17
Optimal Relative Pose with Unknown Correspondences
Fredriksson, J., Larsson, V., Olsson, C., et al. (2016) · 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.2016.191 · Cited by 17
Image encryption algorithm based on lattice hash function and privacy protection
Wang, Y., Chen, L., Yu, K., & Lu, T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12714-5 · Cited by 17
Visibility constraints on features of 3D objects
Basri, R., Felzenszwalb, P. F., Girshick, R., et al. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206726 · Cited by 17
Tran, D. N., Pham, L. H., Nguyen, H.-H., Tran, T. H.-P., Jeon, H., & Jeon, J. W. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00461 · Cited by 18
Channel-Coded Feature Maps for Computer Vision and Machine Learning
Jonsson, E. (2008) · DiVA (Linkoping University) · Cited by 17
GST Data-processing Workflow: Image Registration and Alignment
Yang, X., Cao, W., & Yurchyshyn, V. B. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac91c9 · Cited by 17
Real-Time Methods for Long-Term Tissue Feature Tracking in Endoscopic Scenes
Yip, M. C., Lowe, D. G., Salcudean, S. E., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30618-1_4 · Cited by 17
Self-citationMoving object detection from a moving stereo camera via depth information and visual odometry
Lin, S., & Huáng, S.-H. (2018) · 2018 IEEE International Conference on Applied System Invention (ICASI) · doi:10.1109/icasi.2018.8394278 · Cited by 17
Conn, B., & Arandjelović, O. (2017) · doi:10.1109/ijcnn.2017.7966024 · Cited by 17
Anomaly detection in automated fibre placement: learning with data limitations
Ghamisi, A., Charter, T., Li, J., et al. (2024) · Frontiers in Manufacturing Technology · doi:10.3389/fmtec.2024.1277152 · Cited by 17
A Behaviour-Based Architecture for Mapless Navigation Using Vision
Güzel, M. S., & Bicker, R. (2012) · International Journal of Advanced Robotic Systems · doi:10.5772/46200 · Cited by 17
Chu, G., Yan, P., & Luo, X. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293111 · Cited by 20
FOLD-EM: automated fold recognition in medium- and low-resolution (4–15 Å) electron density maps
Saha, M., & Morais, M. C. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts616 · Cited by 17
3D Detection and Pose Estimation of Vehicle in Cooperative Vehicle Infrastructure System
Guo, E., Chen, Z., Rahardja, S., & Yang, J. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3101497 · Cited by 19
Content-based image retrieval based on multiple extended fuzzy-rough framework
Islam, S. M., Banerjee, M., Bhattacharyya, S., & Chakraborty, S. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.03.036 · Cited by 17
Ahn, H. S. (2008) · InTech eBooks · doi:10.5772/80 · Cited by 17
Linguraru, M. G., Wang, S., Shah, F., et al. (2009) · doi:10.1109/iembs.2009.5334012 · Cited by 17
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