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 32,001–32,100 of 56,201
Research on UAV Image Matching Algorithm Based on Improved AKAZE Feature and VFC Algorithm
Yan, Q., Li, Q., & Zhang, T. (2020) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/icsip49896.2020.9339441 · Cited by 4
SiLK-SLAM: accurate, robust and versatile visual SLAM with simple learned keypoints
Yao, J., & Li, Y. (2024) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-11-2023-0309 · Cited by 4
Performance Enhancement of Patch-based Descriptors for Image Copy Detection
Baber, J., Bakhtyar, M., Noor, W., Basit, A., & Ullah, I. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070361 · Cited by 5
Semi-Supervised Breast Histological Image Classification by Node-Attention Graph Transfer Network
Gong, L., Yang, J., & Zhang, X. (2020) · IEEE Access · doi:10.1109/access.2020.3020149 · Cited by 4
Triangle chain codes for image matching
Yang, S., Wei, E., Guan, R., et al. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.08.055 · Cited by 4
Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
Wells, L. A., & Chung, W. (2023) · Sensors · doi:10.3390/s23167043 · Cited by 4
Principal object detection towards product image search
Wu, X. Y., Liang, L.-L., Wang, W., et al. (2012) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2012.6376735 · Cited by 4
Mapping of indoor environments by robots using low-cost vision sensors
Taylor, T. (2009) · Cited by 4
Active learning with multiple classifiers for multimedia indexing
Safadi, B., & Quénot, G. (2010) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2010.5529910 · Cited by 4
Learning novel object parts model for object categorization
Soltanpour, S., & Ebrahimnezhad, H. (2010) · International Symposium on Telecommunications · doi:10.1109/istel.2010.5734131 · Cited by 4
Verification of visual odometry algorithms with an OpenGL-based software tool
Skulimowski, P., & Strumiłło, P. (2015) · Journal of Electronic Imaging · doi:10.1117/1.jei.24.3.033003 · Cited by 4
Lucks, L., Hirt, P.-R., Hoegner, L., et al. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2022-1063-2022 · Cited by 4
Freeze detection in 2D navigation video sequences overlaid with real satellite images
Yammine, G., Khairat, A., Simmet, F., et al. (2012) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2012.6343413 · Cited by 4
Feature Detection with an Improved Anisotropic Filter
Gobara, M., & Suter, D. (2006) · Lecture notes in computer science · doi:10.1007/11612704_64 · Cited by 4
Gugliandolo, S. G., Marvaso, G., Comi, S., Pepa, M., Romanò, C., Zerini, D., Augugliaro, M., Russo, S., Vischioni, B., Valvo, F., et al. (2020) · Physica Medica · doi:10.1016/j.ejmp.2020.07.012 · Cited by 4
Indoor Manhattan spatial layout recovery from monocular videos via line matching
Kim, C., & Manduchi, R. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.09.012 · Cited by 4
GeoAI-Enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data
Zhou, X. (2019) · Cited by 5
Li, B., Wang, W., & Ye, H. (2013) · Journal of Electronic Imaging · doi:10.1117/1.jei.22.1.013002 · Cited by 4
A framework for multi-object tracking over distributed wireless camera networks
Gau, V., & Hwang, J. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.863284 · Cited by 4
Hierarchical Clustering Matching for Features with Repetitive Patterns in Visual Odometry
Lin, T., & Wang, X. (2020) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-020-01230-z · Cited by 4
Clustering Complex Data with Group-Dependent Feature Selection
Lin, Y., Liu, T.-L., & Fuh, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_7 · Cited by 4
FRoTeMa: Fast and Robust Template Matching
Abdullah, M. M. A., I., M., & Rawya, Y. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.061028 · Cited by 4
Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound
Rasheed, H., Dorent, R., Fehrentz, M., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73647-6_8 · Cited by 4
Mobile City Explorer: An innovative GPS and Camera Phone Based Travel Assistant for City Tourists
Wiesenhofer, S., Schrom-Feiertag, H., Ray, M., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-36728-4_39 · Cited by 4
Globally Optimal and Efficient Manhattan Frame Estimation by Delimiting Rotation Search Space
Ge, W., Song, Y., Zhang, B., & Dong, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01493 · Cited by 4
STBD: a simple tri‐bit binary descriptor for point matching
Liu, H., Li, L., Wang, Z., et al. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0299 · Cited by 4
Trademark image retrieval using weighted combination of sift and HSV correlogram
Nigam, A., & Tripathi, R. C. (2016) · International Journal of Computer Applications in Technology · doi:10.1504/ijcat.2016.077797 · Cited by 4
Tian, Y., Chen, Z., Lu, S., et al. (2018) · Micromachines · doi:10.3390/mi9030113 · Cited by 4
Efficient kernel descriptor for image categorization via pivots selection
Xie, B., Liu, Y., Zhang, H., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738718 · Cited by 4
Advancements in open-source photogrammetry with a point cloud standpoint
Harshit, H., Jain, K. K., & Zlatanova, S. (2023) · Applied Geomatics · doi:10.1007/s12518-023-00529-4 · Cited by 4
Wheat3D PartNet: Annotated dataset for 3D wheat part segmentation
Reena, R., Doonan, J. H., Williams, K. S., et al. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110697 · Cited by 4
Rotational Invariance Using Gabor Convolution Neural Network and Color Space for Image Processing
Gateri, J., Rimiru, R. M., & Kimwele, M. W. (2023) · International Journal of Ambient Computing and Intelligence · doi:10.4018/ijaci.323798 · Cited by 4
Online metric learning for relevance feedback in e-commerce image retrieval applications
Gu, H., Zhao, G., & Qiu, J. (2011) · Tsinghua Science & Technology · doi:10.1016/s1007-0214(11)70055-4 · Cited by 4
Explicit foreground and background modeling in the classification of text blocks in scene images
Sriman, B., & Schomaker, L. (2015) · IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2015.7486604 · Cited by 4
PanoPoint: Self-Supervised Feature Points Detection and Description for 360° Panorama
Zhang, H., Hong, Y., Jia, H., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00686 · Cited by 4
Quantitative review of local descriptors for visual search
Bianco, S., Schettini, R., Mazzini, D., et al. (2013) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin.2013.6697972 · Cited by 4
Learning Hough Transform with Latent Structures for Joint Object Detection and Pose Estimation
Li, H., He, X., Barnes, N. M., & Wang, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27674-8_11 · Cited by 4
Park, J. H., Chen, L. R., Higgins, I., et al. (2024) · IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) · doi:10.1109/ro-man60168.2024.10731202 · Cited by 4
“This was done using FLANN Based Matching [34] with SIFT Descriptors [35].”
MethodA Robust Affine Invariant Point Extraction Algorithm for Image Registration
Wang, W., Yan, X., Wang, Z., et al. (2017) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2017.157 · Cited by 4
Boosted geometric hashing based indexing technique for finger-knuckle-print database
Jayaraman, U., Gupta, A. K., & Gupta, P. (2014) · Information Sciences · doi:10.1016/j.ins.2014.02.032 · Cited by 4
Rădoi, A., & Datcu, M. P. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519432 · Cited by 5
Efficient Querying from Weighted Binary Codes
Weng, Z., & Zhu, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6919 · Cited by 4
Automatic video to point cloud registration in a structure-from-motion framework
Vidal, E., Piotto, N., Cordara, G., et al. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351282 · Cited by 4
A Probabilistic Model for Reconstruction of Torn Forensic Documents
Roy, A., & Garain, U. (2013) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2013.105 · Cited by 4
An Efficient and Geometric-Distortion-Free Binary Robust Local Feature
Guo, J.-M., Chang, L.-Y., & Lee, J.-D. (2019) · Sensors · doi:10.3390/s19102315 · Cited by 4
Assisting the training of deep neural networks with applications to computer vision
Romero, A. (2015) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 4
Liu, M., Han, Y., Xi, X., et al. (2022) · Optics Express · doi:10.1364/oe.462708 · Cited by 4
Computer Vision Based Waste Classification and Management System
Shahriar, M. S., Shammi, S. J., Ahmed, M., et al. (2024) · doi:10.1109/compas60761.2024.10797178 · Cited by 4
“In order to distinguish between SVMs with scale-invariant feature transform (SIFT) [7] and an eleven-layer CNN design, such as AlexNet [8], Mindy Yang et al. [9] conducted a comparison study.”
Graph modelling of 3D geometric information for color consistency of multiview images
Kuse, M., & Jaiswal, S. P. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351029 · Cited by 4
An Optimization Based Approach to Visual Odometry Using Infrared Images
Nilsson, E. (2010) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 4
Chebbout, S., & Merouani, H. F. (2020) · International Journal of Computers and Applications · doi:10.1080/1206212x.2020.1712775 · Cited by 4
Image alignment method based on CUDA SURF for multi-spectral machine vision application
Maeng, H.-Y., Kim, J.-H., & Ko, Y.-H. (2014) · Journal of Korea Multimedia Society · doi:10.9717/kmms.2014.17.9.1041 · Cited by 4
Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network
Kumar, A., Jain, N., Singh, C., et al. (2018) · IEEE India Council International Conference (INDICON) · doi:10.1109/indicon45594.2018.8987153 · Cited by 4
Chen, A., Wang, D., Ma, R., & Pan, Z. (2019) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2019.1662065 · Cited by 4
Structure Is a Visual Class Invariant
Xiao, B., Song, Y.-Z., Balika, A., & Hall, P. M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89689-0_37 · Cited by 4
Study of Feature based Image Registration Algorithms for Navigation of Unmanned Aerial Vehicles
Lakshmi, K. D., & Vaithiyanathan, V. (2015) · Indian Journal of Science and Technology · doi:10.17485/ijst/2015/v8i22/79315 · Cited by 4
Kim, S., & Kweon, I. S. (2006) · Lecture notes in computer science · doi:10.1007/11612704_56 · Cited by 4
Matching of crushed highly decomposed granite particles using 3D SHOT descriptors
Zhu, Z., & Wang, J. (2023) · Géotechnique · doi:10.1680/jgeot.23.00095 · Cited by 4
TOWARD A VISUAL COGNITIVE SYSTEM USING ACTIVE TOP-DOWN SACCADIC CONTROL
Lacroix, J. P. W., Postma, E. O., van den Herik, H. J., & Murre, J. M. J. (2008) · International Journal of Humanoid Robotics · doi:10.1142/s0219843608001443 · Cited by 4
FMCW Radar on LiDAR map localization in structural urban environments
Ma, Y., Li, H., Zhao, X., Gu, Y., Lang, X., Li, L., & Liu, Y. (2024) · Journal of Field Robotics · doi:10.1002/rob.22291 · Cited by 4
“The ORB descriptor is then computed for each keypoint using the ORB descriptor method for violent matching, and mismatches are removed using a distinctive feature ‐ based method (Lowe, 2004; Rublee et al., 2011).”
MethodA robust feature detection algorithm for the binary encoded single-shot structured light system
Jiang, H., & Song, Z. P. (2016) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2016.7831833 · Cited by 4
A fast matching algorithm with an adaptive window based on quasi-dense method
Men, G., Chai, J., & Zhao, J. (2009) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2009.5212272 · Cited by 4
A CNN based car model recognition improvement
Bu, Q., Lan, S., & Xu, P. (2017) · International Conference on Deep Learning Technologies (ICDLT) · doi:10.1145/3094243.3094258 · Cited by 4
Plant or Tree Reconstruction Based on Stereo Vision
Ni, Z., & Burks, T. F. (2013) · 2013 Kansas City, Missouri, July 21 - July 24, 2013 · doi:10.13031/aim.20131596448 · Cited by 4
Sanabria, S. J., Ruby, L. C., Kuonen, J., Dettwiler, S., Colombo, V. L., Frauenfelder, T., Ettlin, D. A., & Rominger, M. B. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2020.06.022 · Cited by 5
Image Detection in Noisy Images
Yadav, K., Mohan, D., & Parihar, A. S. (2021) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs51141.2021.9432243 · Cited by 6
Feature matching based on Gaussian kernel convolution and minimum relative motion
Wang, K., Leng, C., Yan, H., et al. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107795 · Cited by 4
Sun, W., Zhang, Z., Shi, L., et al. (2020) · Precision Engineering · doi:10.1016/j.precisioneng.2020.12.015 · Cited by 4
THAIWRITTENNET: THAI HANDWRITTEN SCRIPT RECOGNITION USING DEEP NEURAL NETWORKS
Mookdarsanit, P., & Mookdarsanit, L. (2020) · Azerbaijan Journal of High Performance Computing · doi:10.32010/26166127.2020.3.1.75.93 · Cited by 4
Automatic Driver Head State Estimation in Challenging Naturalistic Driving Videos
Smith, B. M., Dyer, C. R., Chitturi, M., et al. (2017) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2663-07 · Cited by 4
k-NN Graph Construction: a Generic Online Approach.Preprint
Zhao, W. (2018) · arXiv (Cornell University) · Cited by 4
Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition
Almonte, R. A. M., Zulueta, E., Etxeberria‐Agiriano, I., & Fernández‐Gámiz, U. (2025) · Mathematics · doi:10.3390/math13030467 · Cited by 5
Learning to Select Object Recognition Methods for Autonomous Mobile Robots
Bianchi, R. A. C., Ramisa, A., & de Mántaras, R. L. (2008) · DIGITAL.CSIC (Spanish National Research Council (CSIC)) · Cited by 4
Chatbri, H., Davila, K., Kameyama, K., et al. (2015) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2015.7367128 · Cited by 4
Design of a SIFT based Target Classification Algorithm robust to Geometric Transformation of Target
Lee, H.-Y., Kim, J.-H., Kim, S., et al. (2010) · Journal of Korean institute of intelligent systems · doi:10.5391/jkiis.2010.20.1.116 · Cited by 4
Adaptive relevance feedback for large-scale image retrieval
Suditu, N., & Fleuret, F. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2610-9 · Cited by 4
Agent-based framework to individual tracking in unconstrained environments
Zaghetto, C., Aguiar, L. H. M., Zaghetto, A., et al. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.05.065 · Cited by 4
Image registration with artificial neural networks using spatial and frequency features
Gadde, P., & Yu, X.-H. (2016) · doi:10.1109/ijcnn.2016.7727809 · Cited by 4
Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison
Zhong, J., Li, M., Gruen, A., et al. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.10.009 · Cited by 4
“For more complex real-world datasets, SIFT, DISK and ALIKED achieve millimeter-level accuracy on Coral-2018, as reflected in their low 𝐸 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 and 𝐸 𝑐ℎ𝑒𝑐𝑘 .”
InfluentialMulti-L: A Novel Multi-Robot Cooperative Localization Method in Indoor Environment
Zhu, Z., Zhu, K., Zheng, Z., et al. (2022) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc55140.2022.9922050 · Cited by 4
Fast and robust detection of a known pattern in an image
Denis, L., Ferrari, A., Mary, D. L., et al. (2016) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2016.7760640 · Cited by 4
Robust ISAR Cross-Range Scaling via Two-Step Rotation Velocity Estimation
Ryu, B., Kang, B., Lee, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3120677 · Cited by 4
Dense stereo fish-eye images using a modified hemispherical ASW algorithm
Zhang, Y., Li, H., Zhang, W., et al. (2021) · Journal of the Optical Society of America A · doi:10.1364/josaa.413120 · Cited by 4
Biometrics Security: Facial Marks Detection from the Low Quality Images
Choudhury, Z. H., & Mehata, K. M. (2013) · Cited by 4
Evaluation of Mask Fidelity using automated edge placement error measurement with CD-SEM images
Fu, Z., Zhang, S., Huang, Y., et al. (2015) · China Semiconductor Technology International Conference (CSTIC) · doi:10.1109/cstic.2015.7153359 · Cited by 4
An affine invariant feature detection method based on SIFT and MSER
Wang, Z., Mo, H., Wang, H., et al. (2012) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2012.6360699 · Cited by 4
A Kernel-Based Approach for Content-Based Image Retrieval
Karmakar, P., Teng, S. W., Lu, G., et al. (2018) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2018.8634760 · Cited by 4
CBIR for a wine anti-counterfeiting system using imagery from cork stoppers
Costa, V., Sousa, A. J., & Reis, A. R. (2018) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti.2018.8399225 · Cited by 4
A local descriptor based model with visual attention guidance for generic object detection
Bi, F., Bian, M., Liu, F., et al. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5647706 · Cited by 4
TrGa: Reconsidering the Application of Graph Neural Networks in Two-View Correspondence Pruning
Dai, L., Du, X., & Tang, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681139 · Cited by 4
“A popular approach is to construct a putative correspondence set based on local feature matchings (SIFT [27], SuperPoint [12], etc.).”
InfluentialAutomatic Selection and Detection of Visual Landmarks Using Multiple Segmentations
Langdon, D., Soto, Á., & Mery, D. (2006) · Lecture notes in computer science · doi:10.1007/11949534_60 · Cited by 4
Depth estimation from a single camera image using power fit
Akhlaq, M. U., Izhar, U., & Shahbaz, U. (2014) · doi:10.1109/icreate.2014.6828369 · Cited by 4
Kohlhepp, P., Strand, M., Bretthauer, G., et al. (2007) · at - Automatisierungstechnik · doi:10.1524/auto.2007.55.3.136 · Cited by 4
Construction and evaluation of ontological tag trees
Verma, C. K., Mahadevan, V., Rasiwasia, N., et al. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.057 · Cited by 4
Sparsity-based classification using texture and depth
Kounalakis, T., & Boulgouris, N. V. (2013) · doi:10.1109/icdsp.2013.6622771 · Cited by 4
Integrated Scenario for Machine-Aided Inventory Using Ambient Sensors
Schairer, T., Weiß, C., Vorst, P., et al. (2008) · Cited by 4
Multi-Matching-Based Vision Navigation Referencing Map Tile
Shin, S.-H., Min, D., & Lee, J. (2021) · International Journal of Aeronautical and Space Sciences · doi:10.1007/s42405-021-00373-z · Cited by 4
Visual Fuzzy Control for Blimp Robot to Follow 3D Aerial Object
AL-Jarrah, R. A., & Roth, H. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-08201-1_10 · Cited by 4
Acharya, A., Mukherjee, U., & Fowlkes, C. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.02240 · Cited by 4
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
