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 22,101–22,200 of 56,201
A Surface Approximation Method for Image and Video Correspondences
Huang, J., Wang, B., Wang, W., et al. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2462029 · Cited by 9
SIFT matching method based on K nearest neighbor support feature points
Yong, A., & Zheng, H. Y. (2016) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2016.7888225 · Cited by 9
Sponge Density and Distribution Constrained by Fluid Forcing in the Deep Sea
Culwick, T., Phillips, J., Goodwin, C., et al. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00395 · Cited by 9
An intelligent algorithm for infrared target recognition
Wang, G., Liu, G., Yu, N., et al. (2016) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2016.7832113 · Cited by 9
Geo-contextual priors for attentive urban object recognition
Amlacher, K., Fritz, G., Luley, P., et al. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152657 · Cited by 9
Chen, J., Li, Q., Zhang, W., et al. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3365794 · Cited by 9
A Precise and Real-Time Loop-closure Detection for SLAM Using the RSOM Tree
Song, S., Xia, S., Teng, Z., et al. (2015) · International Journal of Advanced Robotic Systems · doi:10.5772/60687 · Cited by 9
Pill Box Text Identification Using DBNet-CRNN
Xiang, L., Wen, H., & Zhao, M. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20053881 · Cited by 12
Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring
Fang, J., Cao, X., Wang, D., & Xu, S. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3047636 · Cited by 10
A Comparative Study on Mobile Visual Recognition
Chatzilari, E., Liaros, G., Nikolopoulos, S., & Kompatsiaris, I. Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39712-7_34 · Cited by 9
Encrypted Video Search with Single/Multiple Writers
Zheng, Y., Zhang, W., Song, W., et al. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3643887 · Cited by 9
“Pre-trained concept approach [54] allows textual queries based on high-level events (e.g., a parade) learned via semantic word embeddings [19, 20], or queries based on (extracted) features (e.g., SIFT [46]).”
Target-shooting exergame with a hand gesture control
Dardas, N. H., Silva, J. M., & Saddik, A. E. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1236-4 · Cited by 9
3D model reconstruction with common hand-held cameras
Zheng, M., Zhu, J., Xiong, X., et al. (2016) · Virtual Reality · doi:10.1007/s10055-016-0297-5 · Cited by 9
Action-scene Model for Human Action Recognition from Videos
Zhang, Y., Qu, W., & Wang, D. (2014) · AASRI Procedia · doi:10.1016/j.aasri.2014.05.016 · Cited by 9
Real-Time Mosaic-Aided Aerial Navigation: I. Motion Estimation
Indelman, V., Gurfil, P., Rivlin, E., & Rotstein, H. P. (2009) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2009-6218 · Cited by 9
Sustainable Industrial Processes by Embedded Real-Time Quality Prediction
Stolpe, M., Blom, H., & Morik, K. J. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-31858-5_10 · Cited by 11
Singh, A., Hazarika, D., & Bhattacharya, A. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.143 · Cited by 9
An Improved Algorithm on Image Stitching Based on SIFT features
Xing, J., & Miao, Z. (2007) · doi:10.1109/icicic.2007.147 · Cited by 9
Lecture Vdeo Indexing Using Boosted Margin Maximizing Neural Networks
Ma, D., Zhang, X., Ouyang, X., et al. (2017) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2017.0-155 · Cited by 9
Wu, W., Zhao, D., & Zhao, J. (2020) · Optik · doi:10.1016/j.ijleo.2020.165952 · Cited by 9
Cutting Edge: Soft Correspondences in Multimodal Scene Parsing
Namin, S. T., Najafi, M., Salzmann, M., & Petersson, L. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.141 · Cited by 9
Deshpande, S., Muron, W., & Cai, Y. (2017) · doi:10.1002/9781118971666.ch3 · Cited by 9
Perception Subsystem for Object Recognition and Pose Estimation in RGB-D Images
Kornuta, T., & Laszkowski, M. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-29357-8_52 · Cited by 9
Curved Reflection Symmetric Axes on Free-Form Surfaces and Their Extraction
Quan, L., Zhang, Y., & Tang, K. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2595589 · Cited by 9
A Hardware Architecture for the Affine-Invariant Extension of SIFT
Yum, J., Lee, C., Park, J., et al. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2740175 · Cited by 9
Matching Content-based Saliency Regions for partial-duplicate image retrieval
Li, L., Wu, Z., Zha, Z.-J., et al. (2011) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2011.6011895 · Cited by 9
Gender Classification Based on Enhanced PCA-SIFT Facial Features
Wang, Y., & Zhang, N. (2009) · doi:10.1109/icise.2009.620 · Cited by 9
Learning a bag of features based nonlinear metric for facial similarity
Lefebvre, G., & García, C. (2013) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2013.6636646 · Cited by 12
3D object retrieval with graph-based collaborative feature learning
Chen, F., Li, B., & Li, L. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.11.046 · Cited by 9
Farhaoui, O., Fethi, M. R., Zeroual, I., et al. (2023) · Data & Metadata · doi:10.56294/dm2023176 · Cited by 9
Improving the HardNet DescriptorPreprint
Pultar, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09699 · Cited by 10
Implementation of a low-cost photogrammetric methodology for 3D modelling of ceramic fragments
Kalantari, M., & Kasser, M. (2007) · INRIA a CCSD electronic archive server · Cited by 9
Static hand sign recognition using linear projection methods
Gamage, N., Chow, K. Y., & Akmeliawati, R. (2009) · doi:10.1109/icara.2000.4803982 · Cited by 9
Omnidirectional Image Stabilization by Computing Camera Trajectory
Torii, A., Havlena, M., & Pajdla, T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-540-92957-4_7 · Cited by 9
Wang, E., Hu, S., Han, H., et al. (2022) · Water · doi:10.3390/w14121957 · Cited by 9
Rejecting Outliers Based on Correspondence Manifold
Li, X., Li, X., Li, H., et al. (2009) · Acta Automatica Sinica · doi:10.3724/sp.j.1004.2009.00017 · Cited by 9
A new Method for Shot Identification in Basketball Video
Liu, Y., Liu, X., & Huang, C. (2011) · Journal of Software · doi:10.4304/jsw.6.8.1468-1475 · Cited by 9
Local Wavelet Features for Statistical Object Classification and Localization
Grzegorzek, M., Sav, S., O'Connor, N. E., et al. (2010) · IEEE Multimedia · doi:10.1109/mmul.2010.16 · Cited by 9
A comparison of techniques for robust gender recognition
Rojas-Bello, R. N., Lago-Fernández, L. F., Martínez-Muñoz, G., et al. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116610 · Cited by 9
Multi-scale Fusion and Global Semantic Encoding for Affordance Detection
Zhang, Y., Li, H., Ren, T., et al. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892363 · Cited by 9
Study on improved scale Invariant Feature Transform matching algorithm
You-liang, Y., Liu, W., & Zhang, L. (2010) · doi:10.1109/paccs.2010.5626923 · Cited by 9
Fast pedestrian detection using a modified WLD detector in salient region
Lian, G., Lai, J., & Yang, Y. (2011) · International Conference on System Science and Engineering · doi:10.1109/icsse.2011.5961966 · Cited by 9
Image annotation with semi-supervised clustering
Sayar, A., & Vural, F. T. Y. (2009) · International Symposium on Computer and Information Sciences · doi:10.1109/iscis.2009.5291929 · Cited by 9
Learning-based distortion correction enables proximal-scanning endoscopic OCT elastography
Zhang, H., Gu, C., Lan, Q., Zhang, W., Liu, C., & Yang, J. (2024) · Biomedical Optics Express · doi:10.1364/boe.528522 · Cited by 9
Integration of Gabor Features with Bag-of-Features Framework for Arabic Handwritten Word Recognition
al., M. O. A. E., & Mahmoud, S. A. (2017) · IEEE GCC Conference and Exhibition (GCCCE) · doi:10.1109/ieeegcc.2017.8447996 · Cited by 9
Pham, T. T. A., Kim, H., Lee, Y., Kang, H. W., & Park, S. (2022) · IEEE Access · doi:10.1109/access.2022.3162272 · Cited by 9
Face recognition using SIFT features under 3D meshes
Zhang, C., Gu, Y., Hu, K., et al. (2015) · Journal of Central South University · doi:10.1007/s11771-015-2700-x · Cited by 9
Answer sheet layout analysis based on YOLOv5s-DC and MSER
Fu, P., Zhang, X., & Yang, H. (2023) · The Visual Computer · doi:10.1007/s00371-023-03156-7 · Cited by 9
Detecting Instances of Shape Classes That Exhibit Variable Structure
Athitsos, V., Wang, J., Sclaroff, S., et al. (2006) · Lecture notes in computer science · doi:10.1007/11744023_10 · Cited by 9
Remote feature learning for mobile re-identification
Vernier, M., Martinel, N., Micheloni, C., et al. (2013) · International Conference on Distributed Smart Cameras · doi:10.1109/icdsc.2013.6778221 · Cited by 9
Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI
Fouhey, D. F., Higgins, R. E. L., Antiochos, S. K., et al. (2023) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aca539 · Cited by 9
DETECTION OF CRITICAL CAMERA CONFIGURATIONS FOR STRUCTURE FROM MOTION
Michelini, M., & Mayer, H. (2014) · 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-3-w1-73-2014 · Cited by 8
Scale Invariant Feature Transform for n-Dimensional Images (n-SIFT)
Cheung, W., & Hamarneh, G. (2007) · The Insight Journal · doi:10.54294/tiqso5 · Cited by 9
Image annotation via deep neural network
Chengjian, S., Zhu, S., & Shi, Z. (2015) · doi:10.1109/mva.2015.7153244 · Cited by 9
SURF algorithm with color and global characteristics
Yoon, H.-S., Chung, H., & Hahn, H. (2009) · 2009 ICCAS-SICE · Cited by 9
An approach to robot SLAM based on incremental appearance learning with omnidirectional vision
Wu, H. R., & Qin, S. (2010) · International Journal of Systems Science · doi:10.1080/00207720903572422 · Cited by 9
Transformable semantic map based navigation using autonomous deep learning object segmentation
Furuta, Y., Wada, K., Murooka, M., Nozawa, S., Kakiuchi, Y., Okada, K., & Inaba, M. (2016) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2016.7803338 · Cited by 9
Hierarchical image feature extraction and classification
Tsai, M.-H., Tsai, S.-F., & Huang, T. S. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874136 · Cited by 9
Three-dimensional point cloud recognition via distributions of geometric distances
Mahmoudi, M., & Sapiro, G. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563031 · Cited by 9
NOTES2: Networks-of-Traces for Epidemic Spread Simulations
Liu, S., Garg, Y., Candan, K. S., et al. (2015) · Digital Archive @ GSU · Cited by 9
A novel rank order LoG filter for interest point detection
Miao, Z., & Jiang, X. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288039 · Cited by 9
Enhancing Soccer Camera Calibration Through Keypoint Exploitation
Falaleev, N. S., & Chen, R. (2024) · doi:10.1145/3689061.3689074 · Cited by 9
“The Scale Invariant Feature Transform (SIFT) [19] has been widely used for keypoint detection [13].”
MethodSorting local descriptors for lowbit rate mobile visual search
Chen, J., Duan, L., Ji, R., et al. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946582 · Cited by 9
Illumination-invariant completed LTP descriptor for cloud classification
Liu, S., Wang, C., Xiao, B., et al. (2012) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2012.6469765 · Cited by 9
A compact image signature for RTS-invariant image retrieval
Ghosh, P., Manjunath, B. S., & Ramakrishnan, K. R. (2006) · International Conference on Visual Information Engineering (VIE) · doi:10.1049/cp:20060547 · Cited by 9
Takizawa, A., & Kinugawa, H. (2020) · Design Science · doi:10.1017/dsj.2020.27 · Cited by 9
BFROST: binary features from robust orientation segment tests accelerated on the GPU
Cronje, J. (2011) · Cited by 9
A new ROI based image retrieval system using an auxiliary Gaussian weighting scheme
Wang, Z., Liu, G., & Yang, Y. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1059-3 · Cited by 9
SliceMap: an algorithm for automated brain region annotation
Barbier, M., Bottelbergs, A., Nuydens, R., Ebneth, A., & Vos, W. H. D. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx658 · Cited by 8
Real-time pose estimation of rigid objects in heavily cluttered environments
Bratanič, B., Pernuš, F., Likar, B., & Tomaževič, D. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.09.002 · Cited by 9
Template Matching Technique for Panoramic Image Stitching
Bhat, A. S., Shivaprakash, A. V., Prasad, N. S., et al. (2013) · Asia Modelling Symposium · doi:10.1109/ams.2013.22 · Cited by 9
Overview of image mosaic technology by computer vision and digital image processing
Bai, X. (2021) · IEEE International Conference on Data Science and Computer Application (ICDSCA) · doi:10.1109/icdsca53499.2021.9650329 · Cited by 9
Efficient and Robust Multimodal Biometric System for Feature Level Fusion (Speech and Signature)
Kaur, D., Kaur, G., & Singh, D. (2013) · International Journal of Computer Applications · doi:10.5120/13109-0432 · Cited by 9
Design of Chinese Character Coded Targets for Feature Point Recognition Under Motion-Blur Effect
Shi, Y., & Zhang, L. (2020) · IEEE Access · doi:10.1109/access.2020.3006020 · Cited by 9
Detecting Printed and Handwritten Partial Copies of Line Drawings Embedded in Complex Backgrounds
Sun, W., & Kise, K. (2009) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2009.149 · Cited by 9
Hierarchical visual comfort assessment for stereoscopic image retargeting
Zhou, Y., Chen, Z., & Li, W. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116236 · Cited by 9
VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture
Yun, H., Lô, S., Diepenbrock, C., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00556 · Cited by 10
“Image feature points were extracted from both images using the SIFT [22] feature extractor, and they were matched using the Flann matching algorithm [1].”
MethodAudio-visual atoms for generic video concept classification
Jiang, W., Cotton, C. V., Chang, S., et al. (2010) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/1823746.1823748 · Cited by 9
Eye-based Direct Interaction for Environmental Control in Heterogeneous Smart Environments
Corno, F., Gale, A. G., Majaranta, P., et al. (2009) · Springer eBooks · doi:10.1007/978-0-387-93808-0_41 · Cited by 9
Learning from search engine and human supervision for web image search
Yang, L., & Hanjalić, A. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072016 · Cited by 9
ALCN: Adaptive Local Contrast Normalization
Rad, M., Roth, P. M., & Lepetit, V. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.102947 · Cited by 12
MODIFIED RANSAC FOR SIFT-BASED INSAR IMAGE REGISTRATION
Wang, Y., Huang, H., Dong, Z., et al. (2014) · Progress In Electromagnetics Research M · doi:10.2528/pierm14042202 · Cited by 9
Homography-based depth recovery with descent images
Cai, M., Zhou, N., Xue, X., et al. (2013) · Machine Vision and Applications · doi:10.1007/s00138-013-0498-9 · Cited by 9
Real-infraredSR: real-world infrared image super-resolution via thermal imager
Zhou, Y., Liu, Y., Yuan, L., et al. (2023) · Optics Express · doi:10.1364/oe.496484 · Cited by 9
Huo, L., Qi, H., Fei, S., Guan, C., & Li, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7592258 · Cited by 10
On Stability of Adaptive Similarity Measures for Content-Based Image Retrieval
Beecks, C., & Seidl, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-27355-1_33 · Cited by 9
Millimeterwave radar network for foreign object detection
Essen, H., Luedtke, G., Warok, P., Koch, W., Schikora, M., & Wild, K. (2010) · International Workshop on Cognitive Information Processing (CIP) · doi:10.1109/cip.2010.5604116 · Cited by 9
Image indexing and content analysis in children’s picture books using a large-scale database
Huang, C., & Jiang, H. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7440-8 · Cited by 9
Moreń, K., & Göhringer, D. (2016) · Journal of Real-Time Image Processing · doi:10.1007/s11554-016-0576-0 · Cited by 9
Translating topics to words for image annotation
Wang, Y., & Gong, S. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321594 · Cited by 9
State-of-the-Art Object Recognition Techniques: A Comparative Study
Goel, R., Sharma, A., & Kapoor, R. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-0751-9_85 · Cited by 9
Segmentation Based Features for Wide-Baseline Multi-view Reconstruction
Mustafa, A., Kim, H., İmre, E., & Hilton, A. (2015) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2015.39 · Cited by 9
HDPL: a hybrid descriptor for points and lines based on graph neural networks
Guo, Z., Lu, H., Yu, Q., Guo, R., Xiao, J., & Yu, H. (2021) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-02-2021-0042 · Cited by 9
Guo, S., Tang, F., Liu, B., et al. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3286168 · Cited by 9
Liao, Y., Tao, P., Chen, Q., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102599 · Cited by 9
Two phase cooperative learning for supervised dimensionality reduction
Nellas, I. A., Tasoulis, S. K., Georgakopoulos, S. V., & Plagianakos, V. P. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109871 · Cited by 9
A Reliable Order-Statistics-Based Approximate Nearest Neighbor Search Algorithm
Verdoliva, L., Cozzolino, D., & Poggi, G. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2624141 · Cited by 9
Deep compact discriminative representation for unconstrained face recognition
Zhang, M. M. Y., Shang, K., & Wu, H. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.03.015 · Cited by 9
Di, K., Liu, B., Peng, M., Xin, X., Jia, M., Zuo, W., Ping, J., Wu, B., & Oberst, J. (2017) · 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-xlii-3-w1-29-2017 · Cited by 9
Combining High-Resolution Images With Low-Quality Videos
Schubert, F., & Mikolajczyk, K. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.116 · Cited by 9
Metadata
- Authors
- Lowe, D. G.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Lowe, David G.
- ORCID
- Lowe, David G.
- Venue
- International Journal of Computer Vision
- Published
- 2004-11
- Volume
- 60
- Issue
- 2
- Pages
- 91-110
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2151103935
- Semantic Scholar
- 8c04f169203f9e55056a6f7f956695babe622a38
- Cited by
- 55,589
- References
- 50
- Citations by year
- 2026: 1480 · 2025: 2400 · 2024: 2519 · 2023: 2604 · 2022: 2705 · 2021: 3066 · 2020: 2933 · 2019: 3240 · 2018: 3232 · 2017: 3363 · 2016: 3431 · 2015: 3747 · 2014: 3569 · 2013: 3333 · 2012: 3003
- FWCI
- 699.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- feature matching, affine invariance, rotation invariance, object recognition, nearest neighbor matching, Hough transform, pose estimation, occlusion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review