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 9,901–10,000 of 56,200
Zhou, H., Ma, J., Yang, C., et al. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2514521 · Cited by 32
Yu, Y., Xu, M., & Gu, J. J. (2019) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.5409 · Cited by 36
Ni, Y., Mao, J., Wang, H., Xi, Z., & Chen, Z. (2024) · Structural Control and Health Monitoring · doi:10.1155/2024/9988793 · Cited by 37
A video summarization approach based on the emulation of bottom-up mechanisms of visual attention
Jacob, H. G., Pádua, F. L. C., Lacerda, A. M., et al. (2017) · Journal of Intelligent Information Systems · doi:10.1007/s10844-016-0441-4 · Cited by 32
Putting local features on a manifold
Torki, M., & Elgammal, A. (2010) · 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.2010.5539843 · Cited by 32
Historical manuscript dating based on temporal pattern codebook
He, S., Samara, P., Burgers, J. W. J., et al. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.08.008 · Cited by 32
Signature matching distance for content-based image retrieval
Beecks, C., Kirchhoff, S., & Seidl, T. (2013) · doi:10.1145/2461466.2461474 · Cited by 32
Robust 3D visual tracking using particle filtering on the SE(3) group
Choi, C., & Christensen, H. I. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980245 · Cited by 32
Chadha, A., & Andreopoulos, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2673415 · Cited by 32
Weighted feature trajectories and concatenated bag-of-features for action recognition
Yu, J.-M., Jeon, M., & Pedrycz, W. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.10.024 · Cited by 32
Efficient image signatures and similarities using tensor products of local descriptors
Picard, D., & Gosselin, P.-H. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.02.004 · Cited by 34
Two Routes to Face Perception: Evidence From Psychophysics and Computational Modeling
Schwaninger, A., Lobmaier, J. S., Wallraven, C., & Collishaw, S. (2009) · Cognitive Science · doi:10.1111/j.1551-6709.2009.01059.x · Cited by 32
Xu, X., Zhang, X., Zhang, T., et al. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3223514 · Cited by 32
Ni, T., Zhang, C., & Gu, X. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.3013938 · Cited by 32
Lang, H., Zhang, J., Zhang, T., et al. (2014) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.8.083623 · Cited by 32
Characterization of pore structure of polymer blended films used for controlled drug release
Häbel, H., Andersson, H., Olsson, A., et al. (2015) · Journal of Controlled Release · doi:10.1016/j.jconrel.2015.12.011 · Cited by 32
Chen, M., Habib, A. F., He, H., et al. (2017) · Remote Sensing · doi:10.3390/rs9090882 · Cited by 32
Ma, D., Gao, F., Bai, Y., Lou, Y., Wang, S., Huang, T., & Duan, L. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123325 · Cited by 30
MTLDesc: Looking Wider to Describe Better
Wang, C., Xu, R., Zhang, Y., Xu, S., Meng, W., Fan, B., & Zhang, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20138 · Cited by 33
Color-Based Monocular Visuoinertial 3-D Pose Estimation of a Volant Robot
Kyriakoulis, N., & Gasteratos, A. C. (2010) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2010.2045258 · Cited by 31
Image-Based Tracking of the Teeth for Orthodontic Augmented Reality
Aichert, A., Wein, W., Ladikos, A., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33418-4_74 · Cited by 31
A Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization
He, S., Samara, P., Burgers, J. W. J., et al. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2602078 · Cited by 31
Self-organizing subspace clustering for high-dimensional and multi-view data
Araujo, A. F. R., Antonino, V. O., & Ponce-Guevara, K. L. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.06.022 · Cited by 31
Son, J., Kim, S., & Sohn, K. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.035 · Cited by 29
Paczkowski, P., Kim, M. H., Morvan, Y., Dorsey, J. O., Rushmeier, H., & O’Sullivan, C. (2011) · ACM Transactions on Graphics · doi:10.1145/2070781.2024216 · Cited by 33
Subframe Temporal Alignment of Non-Stationary Cameras
Meyer, B. J., Stich, T., & Pollefeys, M. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.11 · Cited by 31
Retinal image registration from 2D to 3D
Lin, Y., & Medioni, G. G. (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.2008.4587705 · Cited by 31
The IMMED Project: Wearable Video Monitoring of People with Age Dementia
Mégret, R., Dovgalecs, V., Wannous, H., et al. (2010) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 31
Color Invariant SURF in Discriminative Object Tracking
Chu, D. M., & Smeulders, A. W. M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35740-4_6 · Cited by 31
Choi, J., Thomée, B., Friedland, G. H., et al. (2014) · doi:10.1145/2661118.2661125 · Cited by 31
Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device
He, X., Aloi, D. N., & Li, J. (2015) · Sensors · doi:10.3390/s151229867 · Cited by 31
Automation of “ground truth” annotation for multi-view RGB-D object instance recognition datasets
Aldomà, A., Fäulhammer, T., & Vincze, M. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6943275 · Cited by 31
Pedestrian Detection Using Boosted HOG Features
Wang, Z., Jia, Y., Huang, H., & Tang, S. (2008) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2008.4732553 · Cited by 32
Crowding with detection and coarse discrimination of simple visual features
Põder, E. (2008) · Journal of Vision · doi:10.1167/8.4.24 · Cited by 31
Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System
Luo, J., & Lin, C. H. (2018) · Sensors · doi:10.3390/s18041174 · Cited by 32
Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
Jiang, D., & Kim, J. (2021) · Applied Sciences · doi:10.3390/app11125701 · Cited by 36
SOIC: Semantic Online Initialization and Calibration for LiDAR and CameraPreprint
Wang, W., Nobuhara, S., Nakamura, R., & Sakurada, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04260 · Cited by 49
An Orientation Invariant Visual Homing Algorithm
Churchill, D. G., & Vardy, A. (2012) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-012-9730-5 · Cited by 31
Hu, W., Zhang, Y., Liang, Y., Yin, Y., Georgescu, A., Tran, A. T. T., Kruppa, H., Ng, S.-K., & Zimmermann, R. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548102 · Cited by 35
An Integrated Bayesian Approach to Shape Representation and Perceptual Organization
Feldman, J., Singh, M., Briscoe, E., et al. (2013) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-5195-1_4 · Cited by 31
Prototype-Based Image Search Reranking
Yang, L., & Hanjalić, A. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2187778 · Cited by 31
Li, J., Shi, X., You, Z., Yi, H.-C., Chen, Z., Lin, Q., & Fang, M. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.2965919 · Cited by 30
Hussain, K., Afifi, M., & Moussa, G. S. (2019) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2018.2838117 · Cited by 24
Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue.Preprint
Bian, J.-W., Zhan, H., Wang, N., Chin, T.-J., Shen, C., & Reid, I. D. (2020) · arXiv (Cornell University) · Cited by 26
Regionwise Classification of Building Facade Images
Yang, M. Y., & Förstner, W. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24393-6_18 · Cited by 31
Asheghi, B., Salehpour, P., Khiavi, A. M., et al. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108496 · Cited by 31
IMU-aided stereo visual odometry for ground-tracking AUV applications
Hildebrandt, M., & Kirchner, F. (2010) · OCEANS'10 IEEE SYDNEY · doi:10.1109/oceanssyd.2010.5603681 · Cited by 31
Akai, N., Morales, L. Y., & Murase, H. (2018) · Advanced Robotics · doi:10.1080/01691864.2018.1509726 · Cited by 31
A study of projections for key point based registration of panoramic terrestrial 3D laser scan
Houshiar, H., Elseberg, J., Borrmann, D., & Nüchter, A. (2015) · Geo-spatial Information Science · doi:10.1080/10095020.2015.1017913 · Cited by 30
Clustering near-duplicate images in large collections
Foo, J. J., Zobel, J., & Sinha, R. (2007) · Multimedia Information Retrieval · doi:10.1145/1290082.1290089 · Cited by 31
Advances in Video Summarization and Skimming
Jiang, R., Sadka, A. H., & Crookes, D. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-02900-4_2 · Cited by 31
Web-enabled Robots -- Robots that Use the Web as an Information Resource
Tenorth, M., Klank, U., Pangercic, D., et al. (2011) · Cited by 31
VSAC: Efficient and Accurate Estimator for H and F
Ivashechkin, M., Baráth, D., & Matas, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01496 · Cited by 31
Development of Modular Bio-Inspired Autonomous Underwater Vehicle for Close Subsea Asset Inspection
Gorma, W., Post, M. A., White, J., Gardner, J., Luo, Y., Kim, J., Mitchell, P., Morozs, N., Wright, M., & Xiao, Q. (2021) · Applied Sciences · doi:10.3390/app11125401 · Cited by 31
Localization accuracy of region detectors
Haja, A., Jähne, B., & Abraham, S. (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.2008.4587829 · Cited by 30
Panoramic Crack Detection for Steel Beam Based on Structured Random Forests
Wang, S., Liu, X., Yang, T., & Wu, X. (2018) · IEEE Access · doi:10.1109/access.2018.2812141 · Cited by 34
An image-based intelligent system for pointer instrument reading
Yang, Z., Niu, W., Peng, X., Gao, Y., Qiao, Y., & Dai, Y. (2014) · IEEE International Conference on Information Science and Technology (ICIST) · doi:10.1109/icist.2014.6920593 · Cited by 34
Keypoint descriptor matching with context-based orientation estimation
Bellavia, F., Tegolo, D., & Valenti, C. F. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.05.002 · Cited by 31
Shyu, W.-H., Lee, W.-P., Chiang, M.-H., Chang, C. C., Fu, T., Chiang, H., Wu, T., & Wu, C. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008153 · Cited by 29
A Recursive Constrained Framework for Unsupervised Video Action Clustering
Peng, B., Lei, J., Fu, H., Shao, L., & Huang, Q. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2937514 · Cited by 27
Zhang, G., Qin, D., Yang, J., Yan, M., Tang, H., Bie, H., & Ma, L. (2022) · Remote Sensing · doi:10.3390/rs14236013 · Cited by 31
Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
Chen, B., Yuan, D., Liu, C., & Wu, Q. H. (2019) · Applied Sciences · doi:10.3390/app9061120 · Cited by 31
Spatio-temporal SIFT and Its Application to Human Action Classification
Alghamdi, M., Zhang, L., & Gotoh, Y. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33863-2_30 · Cited by 31
Video-based descriptors for object recognition
Lee, T., & Soatto, S. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.08.003 · Cited by 29
Virtual observers in a mobile surveillance system
Greenhill, S., & Venkatesh, S. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180759 · Cited by 31
Identifying Semantically Equivalent Object Fragments
Epshtein, B., & Ullman, S. (2005) · 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.2005.180 · Cited by 31
Segmentation of Solid Oxide Cell Electrodes by Patch Convolutional Neural Network
Ściążko, A., Komatsu, Y., Shimura, T., et al. (2021) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/abef84 · Cited by 31
DRPnet: automated particle picking in cryo-electron micrographs using deep regression
Nguyen, N. P., Gotberg, J., Ersoy, I., Bunyak, F., & White, T. (2021) · BMC Bioinformatics · doi:10.1186/s12859-020-03948-x · Cited by 30
Photometric Heat Kernel Signatures
Kovnatsky, A., Bronstein, M. M., Bronstein, A. M., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-24785-9_52 · Cited by 31
A novel approach for tracking high speed skaters in sports using a panning camera
Liu, G., Tang, X., Cheng, H.-D., Huang, J., & Liu, J. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.03.022 · Cited by 31
Gaze-Informed Egocentric Action Recognition for Memory Aid Systems
Zuo, Z., Yang, L., Peng, Y., Chao, F., & Qu, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2808486 · Cited by 30
OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions
Talbot, B., Garg, S., & Milford, M. J. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593761 · Cited by 34
Shen, R., Lin, Y., Juang, T. T.-Y., et al. (2017) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2017.2780882 · Cited by 31
Visual inferences of material changes: color as clue and distraction
Zaidi, Q. (2011) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.148 · Cited by 31
Sleep posture recognition based on machine learning: A systematic review
Li, X., Gong, Y., Jin, X., & Shang, P. (2023) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2023.101752 · Cited by 32
People Reidentification in a Camera Network
de Oliveira, Í. O., & Pio, J. L. D. S. (2009) · IEEE International Conference on Dependable, Autonomic and Secure Computing · doi:10.1109/dasc.2009.33 · Cited by 31
Gualdi, G., Prati, A., & Cucchiara, R. (2011) · EURASIP Journal on Image and Video Processing · doi:10.1155/2011/684819 · Cited by 31
Halloran, K. M., Murdoch, J. D., & Becker, M. S. (2014) · African Journal of Ecology · doi:10.1111/aje.12145 · Cited by 31
Semantic Mapping with Mobile Robots
Pronobis, A. (2011) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 32
Learning Efficient Linear Predictors for Motion Estimation
Matas, J., Zimmermann, K., Svoboda, T., & Hilton, A. (2006) · Lecture notes in computer science · doi:10.1007/11949619_40 · Cited by 32
Geometric statistics-based descriptor for 3D ear recognition
Ganapathi, I. I., Ali, S. S., & Prakash, S. (2018) · The Visual Computer · doi:10.1007/s00371-018-1593-8 · Cited by 31
Wang, T., Li, X., Guo Zhang, A., Lin, M., Deng, M., Cui, H., Jiang, B., Wang, Y., Zhu, Y. M., Wang, H., et al. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3142372 · Cited by 32
Keypoint-based copy-move detection scheme by adopting MSCRs and improved feature matching
Li, J., Yang, F., Lu, W., & Sun, W. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3967-0 · Cited by 31
OmniSIFT: Scale invariant features in omnidirectional images
Arican, Z., & Pascal Frossard, B. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5650845 · Cited by 31
Monocular 3-D Tracking of Inextensible Deformable Surfaces Under $L_2$-Norm
Shen, S., Shi, W., & Liu, Y. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2038115 · Cited by 31
A novel cross-modal hashing algorithm based on multimodal deep learning
Qu, W., Wang, D., Feng, S., et al. (2017) · Science China Information Sciences · doi:10.1007/s11432-015-0902-2 · Cited by 31
Yang, X., Chen, A., Zhou, G., Wang, J., Chen, W., Gao, Y., & Jiang, R. (2020) · IEEE Access · doi:10.1109/access.2020.3017560 · Cited by 35
Adaptive linear feature-reuse network for rapid forest fire smoke detection model
Li, J., Zhou, G., Chen, A., Wang, Y., Jiang, J., Hu, Y., & Lü, C. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101584 · Cited by 32
ALOHA: An efficient binary descriptor based on Haar features
Saha, S., & Demoulin, V. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467367 · Cited by 31
Liu, F., Ghosh, S., Johnson, N. P., et al. (2014) · Proceedings - ACM IEEE Design Automation Conference · doi:10.1145/2593069.2593105 · Cited by 31
Fault diagnosis for rolling bearing based on SIFT-KPCA and SVM
Cheng, Y., Yuan, H., Liu, H., et al. (2017) · Engineering Computations · doi:10.1108/ec-01-2016-0005 · Cited by 31
Finger Vein Recognition Using Histogram of Competitive Gabor Responses
Lu, Y. F., Yoon, S., Xie, S. J., Yang, J., Wang, Z., & Park, D. S. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.309 · Cited by 33
Introduction to machine learning
Kerner, H., Campbell, J., & Strickland, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818721-0.00007-0 · Cited by 34
Energy-Efficient Ground Traversability Mapping Based on UAV-UGV Collaborative System
Li, J., Cheng, Y., Zhou, J., Chen, J., Liu, Z., Hu, S., & Leung, V. C. M. (2021) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3107291 · Cited by 31
Simple low-dimensional features approximating NCC-based image matching
Satoh, S. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.07.027 · Cited by 31
Feature based multi-view image registration using SURF
Patel, M. S., Patel, N., & Holia, M. S. (2015) · International Symposium on Advanced Computing and Communication (ISACC) · doi:10.1109/isacc.2015.7377344 · Cited by 33
Discrete Approximations of Gaussian Smoothing and Gaussian Derivatives
Lindeberg, T. (2024) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-024-01196-9 · Cited by 38
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification
Bailly, A., Malinowski, S., Tavenard, R., Chapel, L., & Guyet, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44412-3_2 · Cited by 34
SDE: A Novel Selective, Discriminative and Equalizing Feature Representation for Visual Recognition
Xie, G.-S., Zhang, X.-Y., Yan, S., & Liu, C. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1007-9 · Cited by 29
DISCOV: A Framework for Discovering Objects in Video
Liu, D., & Chen, T. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2007.911781 · Cited by 31
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
