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 13,401–13,500 of 56,200
A multisensor LBS using SIFT-based 3D models
Ruiz-Ruiz, A. J., López-de-Teruel, P. E., & Cánovas, Ó. (2012) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2012.6418901 · Cited by 21
Learning to Hallucinate Face Images via Component Generation and EnhancementPreprint
Song, Y., Zhang, J., He, S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00223 · Cited by 21
Monitoring structural damages in big industrial plants with UAV images
Moranduzzo, T., & Melgani, F. (2014) · doi:10.1109/igarss.2014.6947606 · Cited by 21
AUTOMATED AND ACCURATE ORIENTATION OF COMPLEX IMAGE SEQUENCES
Barazzetti, L., Fabio Remondino, A., & Scaioni, M. (2012) · 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-xxxviii-5-w16-277-2011 · Cited by 22
Robust line segment matching via reweighted random walks on the homography graph
Wei, D., Zhang, Y., & Li, C. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107693 · Cited by 21
Simultaneous capture of the color and topography of paintings using fringe encoded stereo vision
Zaman, T., Jonker, P. P., Lenseigne, B. A. J., & Dik, J. (2014) · Heritage Science · doi:10.1186/s40494-014-0023-0 · Cited by 21
A Novel Stereo Matching Algorithm for Digital Surface Model (DSM) Generation in Water Areas
Yang, W., Li, X., Yang, B., & Fu, Y. (2020) · Remote Sensing · doi:10.3390/rs12050870 · Cited by 24
Particle filter tracking based on color and SIFT features
Wu, P., Kong, L., Zhao, F., & Li, X. (2008) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2008.4590034 · Cited by 20
Evaluation of a Fingerprint Identification Algorithm with SIFT Features
Awad, A. I., & Baba, K. (2012) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai.2012.34 · Cited by 21
Review on computer vision-based inspection and monitoring for bridge cables
Ji, W., Luo, K., & Luo, K. (2025) · Measurement · doi:10.1016/j.measurement.2025.116892 · Cited by 23
Automatic cloud detection for high spatial resolution multi-temporal images
Han, Y., Kim, B.-H., Kim, Y., et al. (2014) · Remote Sensing Letters · doi:10.1080/2150704x.2014.942921 · Cited by 21
Generative Graph Prototypes from Information Theory
Han, L., Wilson, R. C., & Hancock, E. R. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2400451 · Cited by 21
A Performance Evaluation of SIFT and SURF for Multispectral Image Matching
Saleem, S., Bais, A., & Sablatnig, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31295-3_20 · Cited by 21
Deep Active Contours for Real-time 6-DoF Object Tracking
Wang, L., Yan, S., Zhen, J., Liu, Y., Zhang, M., Zhang, G., & Zhou, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01290 · Cited by 23
IVPR: An Instant Visual Place Recognition Approach Based on Structural Lines in Manhattan World
Pei, L., Liu, K., Zou, D., Li, T., Wu, Q., Zhu, Y., Li, Y., He, Z., Chen, Y., & Sartori, D. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2941359 · Cited by 19
Shrinkage Expansion Adaptive Metric Learning
Wang, Q., Zuo, W., Lei Zhang, A., & Li, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_30 · Cited by 18
Advanced Techniques in CBIR: Local Descriptors, Visual Dictionaries and Bags of Features
Junior, E. A. D. V., & Cord, M. (2009) · doi:10.1109/sibgrapi-tutorials.2009.14 · Cited by 24
Soccer Ball Detection by Comparing Different Feature Extraction Methodologies
Mazzeo, P. L., Leo, M., Spagnolo, P., et al. (2012) · Advances in Artificial Intelligence · doi:10.1155/2012/512159 · Cited by 21
Robust Registration of Cloudy Satellite Images Using Two-Step Segmentation
Lee, I. H., & Mahmood, M. T. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2385691 · Cited by 21
On online high-dimensional spherical data clustering and feature selection
Amayri, O., & Bouguila, N. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.10.009 · Cited by 21
Stochastic Exploration of Ambiguities for Nonrigid Shape Recovery
Moreno-Noguer, F., & Fua, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.102 · Cited by 21
Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching
Deng, Z., Yang, D., Zhang, X., et al. (2020) · Electronics · doi:10.3390/electronics9010198 · Cited by 21
Montazeri, S., Gisinger, C., Eineder, M., et al. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2769078 · Cited by 21
Dastjerdi, H. M., Dahmardeh, M., Gemeinhardt, A., Mahmoodabadi, R. G., Köstler, H., & Sandoghdar, V. (2021) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac2f68 · Cited by 22
He, S., Zhao, G. L., Chen, J., et al. (2025) · Automation in Construction · doi:10.1016/j.autcon.2024.105946 · Cited by 21
Mid‐level deep Food Part mining for food image recognition
Zheng, J., Zou, L., & Wang, Z. J. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0335 · Cited by 21
Event recognition from photo collections via PageRank
Imran, N., Liu, J., Luo, J., et al. (2009) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1631272.1631371 · Cited by 21
Face Alignment With Expression- and Pose-Based Adaptive Initialization
Mo, H., Liu, L., Zhu, W., et al. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2867262 · Cited by 21
Direction Selective Contour Detection for Salient Objects
Manno-Kovács, A. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2804438 · Cited by 21
Privacy-Preserving Visual Content Tagging using Graph Transformer Networks
Vu, X.-S., Le, D.-T., Edlund, C., Jiang, L., & Nguyen, H. D. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414047 · Cited by 23
A Hardware-Efficient Recognition Accelerator Using Haar-Like Feature and SVM Classifier
Luo, A., An, F., Zhang, X., & Mattausch, H. J. (2019) · IEEE Access · doi:10.1109/access.2019.2894169 · Cited by 22
Efficient descriptor of histogram of salient edge orientation map for finger vein recognition
Lu, Y. F., Yoon, S., Xie, S. J., et al. (2014) · Applied Optics · doi:10.1364/ao.53.004585 · Cited by 21
An extensive review on spectral imaging in biometric systems: Challenges & advancements
Munir, R., & Khan, R. A. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.102660 · Cited by 23
Seamless UAV Hyperspectral Image Stitching Using Optimal Seamline Detection via Graph Cuts
Peng, Z., Ma, Y., Zhang, Y., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3275970 · Cited by 21
Self-supervised deep metric learning for ancient papyrus fragments retrieval
Pirrone, A., Beurton‐Aimar, M., & Journet, N. (2021) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-021-00369-1 · Cited by 24
SyMFM6D: Symmetry-Aware Multi-Directional Fusion for Multi-View 6D Object Pose Estimation
Duffhauss, F., Koch, S., Ziesche, H., Vien, N. A., & Neumann, G. M. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3293317 · Cited by 24
Multi-view Texturing of Imprecise Mesh
Aganj, E., Monasse, P., & Keriven, R. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12304-7_44 · Cited by 21
The development of an automatic recognition system for earmark and earprint comparisons
Junod, S., Pasquier, J., & Champod, C. (2012) · Forensic Science International · doi:10.1016/j.forsciint.2012.05.021 · Cited by 21
Fast Near-Duplicate Video Retrieval via Motion Time Series Matching
Zhang, J. R., Ren, J., Chang, F., Wood, T. L., & Kender, J. R. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.111 · Cited by 20
Yang, Y., Song, H., Sun, S., Zhang, W., Chen, Y., Rakai, L., & Fang, Y. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01121-y · Cited by 22
Feng, R., Du, Q., Shen, H., & Li, X. (2021) · Remote Sensing · doi:10.3390/rs13081475 · Cited by 23
Zhang, Z., Li, M., Huang, K., et al. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761317 · Cited by 21
Diversity and consistency learning guided spectral embedding for multi-view clustering
Li, Z., Tang, C., Chen, J., et al. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.002 · Cited by 21
Automatic classification of medical X‐ray images: hybrid generative‐discriminative approach
Zare, M. R., Mueen, A., Awedh, M. H., et al. (2013) · IET Image Processing · doi:10.1049/iet-ipr.2013.0049 · Cited by 21
Image-based deformation measurement
Scaioni, M., Feng, T., Barazzetti, L., Previtali, M., & Roncella, R. (2014) · Applied Geomatics · doi:10.1007/s12518-014-0152-x · Cited by 23
Distance metric learning from uncertain side information for automated photo tagging
Wu, L., Hoi, S. C. H., Jin, R., et al. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1899412.1899417 · Cited by 21
The localization problem for underwater vehicles: An overview of operational solutions
Sørensen, F. F., Mai, C., von Benzon, M., et al. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.121173 · Cited by 21
Hu, S., Xu, K., Ma, L., Liu, B., Jiang, B., & Wang, J. (2013) · ACM Transactions on Graphics · doi:10.1145/2508363.2508371 · Cited by 20
Pressure distribution classification and segmentation of human hands in contact with the robot body
Albini, A., & Cannata, G. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920907688 · Cited by 21
Wu, Y. N., & Fan, J. (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.2009.5206719 · Cited by 21
A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps
Rajasegaran, J., Karunanayake, N., Gunathillake, A., Seneviratne, S., & Jourjon, G. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313427 · Cited by 19
Direct visual servoing based on multiple intensity histograms
Bateux, Q., & Marchand, É. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7140043 · Cited by 20
Rotating Vibration Measurement Using 3D Digital Image Correlation
Wang, Y., Gao, Z., Fang, Z., et al. (2023) · Experimental Mechanics · doi:10.1007/s11340-022-00934-7 · Cited by 21
Xiao, Y., Cao, Z., Wang, L., & Li, T. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.059 · Cited by 21
Robust recognition of face with partial variations using local features and statistical learning
Seo, J., & Park, H. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.09.048 · Cited by 20
Objects Tracking from Natural Features in Mobile Augmented Reality
Ng, G. W., Khan, R. U., Adruce, S. A. Z., et al. (2013) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2013.10.297 · Cited by 21
Autonomous Mapping and Exploration with Unmanned Aerial Vehicles Using Low Cost Sensors
Ravankar, A. A., Ravankar, A. A., Kobayashi, Y., & Emaru, T. (2018) · International Electronic Conference on Sensors and Applications (ECSA) · doi:10.3390/ecsa-5-05753 · Cited by 21
A Brief Survey on Modern Iris Feature Extraction Methods
Ahmed, H. M., & Taha, M. A. (2021) · Engineering and Technology Journal · doi:10.30684/etj.v39i1a.1680 · Cited by 21
Object instance recognition using triplets of feature symbols
Zitnick, L., Sun, J., Szeliski, R., et al. (2007) · Cited by 21
Sparse appearance based modeling for robot localization
Booij, O., Živković, Z., & Kröse, B. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.281980 · Cited by 21
Garg, S., & Milford, M. J. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196827 · Cited by 22
ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection
Mandal, M., Chaudhary, M., Vipparthi, S. K., Murala, S., Gonde, A. B., & Nagar, S. K. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5206 · Cited by 21
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection
Magri, L., Leveni, F., & Boracchi, G. (2021) · 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/cvpr46437.2021.00189 · Cited by 21
Huang, Z., & Epps, J. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2821135 · Cited by 21
Barrett’s esophagus analysis using infinity Restricted Boltzmann Machines
Júnior, L. A. P., de Souza, L. A., Jr., Mendel, R., et al. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.01.043 · Cited by 21
Feature Learning and Deep Learning Architecture Survey
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_10 · Cited by 22
W-Net: Reinforced U-Net for Density Map EstimationPreprint
Valloli, V. K., & Mehta, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.11249 · Cited by 21
Wireless Capsule Endoscopy Video Reduction Based on Camera Motion Estimation
Liu, H., Pan, N., Lu, H., et al. (2012) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-012-9519-x · Cited by 21
Remote Estimation of Heart Rate Based on Multi-Scale Facial ROIs
Zhao, C., Han, W., Chen, Z., et al. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00147 · Cited by 21
Scene Chinese Recognition with Local and Global Attention
Zhao, C., Yi, Y., Gan, C., Tang, Z., & Kong, D. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111013 · Cited by 23
Monocular vision and IMU based navigation for a small unmanned helicopter
Wang, C., Wang, T., Liang, J., Chen, Y., & Wu, Y. (2012) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2012.6360998 · Cited by 23
Top-Points as Interest Points for Image Matching
Platel, B., Balmachnova, E., Florack, L. M. J., & Romeny, B. M. T. H. (2006) · Lecture notes in computer science · doi:10.1007/11744023_33 · Cited by 21
Decomposition of conflict as a distribution on hypotheses in the framework on belief functions
Roquel, A., Hégarat‐Mascle, S. L., Isabelle Bloch, A., et al. (2014) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2013.12.006 · Cited by 21
Adaptive Integration of Feature Matches into Variational Optical Flow Methods
Stoll, M., Volz, S., & Bruhn, A. (2012) · Asian Conference on Computer Vision · doi:10.1007/978-3-642-37431-9_1 · Cited by 32
Wan, H., Chen, J., Huang, Z., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3093308 · Cited by 21
Ling, X., & Qin, R. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.12.013 · Cited by 20
Learning descriptive visual representation for image classification and annotation
Lu, Z., & Wang, L. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.08.008 · Cited by 20
Comparative Study Between Real-Time and Non-Real-Time Segmentation Models on Flooding Events
Safavi, F., Chowdhury, T., & Rahnemoonfar, M. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671314 · Cited by 26
Patch-based Object Recognition Using Discriminatively Trained Gaussian Mixtures
Hegerath, A., Deselaers, T., & Ney, H. (2006) · British Machine Vision Conference (BMVC) · doi:10.5244/c.20.54 · Cited by 21
Multi-sensor image registration by combining local self-similarity matching and mutual information
Liu, X., Chen, S., Zhuo, L., et al. (2018) · Frontiers of Earth Science · doi:10.1007/s11707-018-0717-9 · Cited by 21
BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition
Chan, J., Lee, J. A., & Kemao, Q. (2017) · 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.2017.322 · Cited by 21
Using low-quality video sequences for fish detection and tracking
Lantsova, E., Voitiuk, T., Zudilova, T. V., et al. (2016) · 2016 SAI Computing Conference (SAI) · doi:10.1109/sai.2016.7556017 · Cited by 21
3DG-STFM: 3D Geometric Guided Student-Teacher Feature Matching
Mao, R., Bai, C., An, Y., Zhu, F. M., & Lu, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19815-1_8 · Cited by 21
Research on 3D Point Cloud De-Distortion Algorithm and Its Application on Euclidean Clustering
Wen, L., He, L., & Gao, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2926424 · Cited by 21
Real-time monocular image-based path detection
Cristóforis, P. D., Nitsche, M., Krajník, T., et al. (2013) · Journal of Real-Time Image Processing · doi:10.1007/s11554-013-0356-z · Cited by 21
CaSnLi-YOLO: construction site multi-target detection method based on improved YOLOv5s
Hao, F., Zhang, T., He, G., Dou, R., & Meng, C. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad42c5 · Cited by 21
“…of performance levels, this method surpasses existing approaches such as histogram of oriented gradients [41], scale-invariant feature transform [42], speeded-up robust features [43], oriented fast and rotated binary robust independent element features [44], and binary robust invariant scalable…”
DOSeqSLAM: Dynamic On-line Sequence Based Loop Closure Detection Algorithm for SLAM
Tsintotas, K. A., Bampis, L., & Gasteratos, A. C. (2018) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2018.8577113 · Cited by 22
What is the best way for extracting meaningful attributes from pictures?
Liu, L., Wiliem, A., Shaokang Chen, A., & Lovell, B. C. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.034 · Cited by 20
Feature Selection using Particle Swarm Optimization for Thermal Face Recognition
Seal, A., Ganguly, S., Bhattacharjee, D., Nasipuri, M., & Gonzalo‐Martín, C. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-1985-9_2 · Cited by 22
Zhang, L., Tjondronegoro, D., & Chandran, V. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.97 · Cited by 21
Learning Sample Specific Weights for Late Fusion
Lai, K.-T., Liu, D., Chang, S., & Chen⋆, M.-S. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2423560 · Cited by 21
Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition
Yuan, C., Li, X., Hu, W., Ling, H., & Maybank, S. J. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2291319 · Cited by 21
Recognizing actions in images by fusing multiple body structure cues
Li, Y., Li, K., Li, K., et al. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107341 · Cited by 21
Transfer Learning Approach to Seed Taxonomy: A Wild Plant Case Study
Ibrahim, N. M., Gabr, D. G. I., Rahman, A., et al. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7030128 · Cited by 21
A review of edge-based 3D tracking of rigid objects
Han, P., & Zhao, G. L. (2019) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2019.10.001 · Cited by 21
A novel hardware design for SIFT generation with reduced memory requirement
Kim, E. S., & Lee, H. (2013) · JSTS Journal of Semiconductor Technology and Science · doi:10.5573/jsts.2013.13.2.157 · Cited by 21
An analysis of the relationship between painters based on their work
Bressan, M., Cifarelli, C., & Perronnin, F. (2008) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2008.4711704 · Cited by 21
Survey of Image Recognition Algorithms
Zhang, S., Wu, Y., & Chang, J. (2020) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec48623.2020.9084972 · Cited by 24
RKEM: Redundant Keypoint Elimination Method in Image Registration
Hossein‐Nejad, Z., & Nasri, M. (2017) · IET Image Processing · doi:10.1049/iet-ipr.2016.0440 · Cited by 21
Semi-parametric models for visual odometry
Guizilini, V., & Ramos, F. T. (2012) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2012.6224775 · Cited by 21
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
