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 40,601–40,700 of 56,201
Appearance-Based Curriculum for Semi-Supervised Learning with Multi-Angle Unlabeled Data
Tanaka, Y., Yoshida, S. M., Shibata, T., Terao, M., Okatani, T., & Sugiyama, M. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00276 · Cited by 1
Mobile robot localisation : error modelling, data synchronisation and vision techniques
Zaman, M. (2006) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 1
Kitagawa, M., & Shimizu, I. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018edl8176 · Cited by 1
Improving accuracy and flexibility of industrial robots using computer vision
Maric, P. S. (2012) · InTech eBooks · doi:10.5772/32600 · Cited by 1
Sparse Source Separation of Non-instantaneous Spatially Varying Single Path Mixtures
Achtenberg, A., & Zeevi, Y. Y. J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19282-1_12 · Cited by 1
SGBA: Subspace guidance backdoor attack with feature alignment in image classification
Luo, H., Qin, Z., Wang, L., Wu, Z., & Yang, M. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113857 · Cited by 1
Multi-source image target detection technology based on salient feature fusion
Cui, Y., Wang, S., Gao, X., et al. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2024/1/012025 · Cited by 1
Organizing rushes video by visually similar setting
Lee, F., & Bailer, W. (2008) · doi:10.1145/1386352.1386391 · Cited by 1
Image matching based on improved particle swarm optimization
Guo, Y., & Sun, Y. (2011) · International Conference on Electronics, Communications and Control · doi:10.1109/icecc.2011.6066407 · Cited by 1
Efficient Image-Vector-Generation Processor for Edge-Based Complementary Feature Representations
Yamashita, N., & Shibata, T. (2012) · Japanese Journal of Applied Physics · doi:10.1143/jjap.51.02be05 · Cited by 1
Matching DSIFT Descriptors Extracted from CSLM Images
Stanciu, S. G., Coltuc, D., Tranca, D. E., et al. (2013) · Engineering · doi:10.4236/eng.2013.510b042 · Cited by 1
Learning to Navigate for Fine-grained ClassificationPreprint
Yang, Z., Luo, T., Wang, D., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.00287 · Cited by 1
Visual Ontology Construction and Concept Detection for Multimedia Indexing and Retrieval
Kennedy, L. S., & Chang, S. (2010) · doi:10.1002/9780470588222.ch9 · Cited by 1
Background and Literature Review
Oreifej, O., & Shah, M. (2014) · The Kluwer international series in video computing · doi:10.1007/978-3-319-04184-1_2 · Cited by 1
Jiménez‐Puerto, J. (2024) · Journal of Archaeological Science Reports · doi:10.1016/j.jasrep.2024.104521 · Cited by 1
Tang, Z., Zhang, Q., Sun, X., et al. (2024) · International Conference on Signal Processing · doi:10.1109/icsp62129.2024.10846730 · Cited by 1
“RANSAC algorithm [16] is used to reject error matches.”
Fast repetition detection in TV streams using duration patterns
Carton, C., & Gros, P. (2013) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2013.6576567 · Cited by 1
O'Neil, B. (2013) · Texas ScholarWorks (Texas Digital Library) · Cited by 1
Towards Understanding Articulated Objects
Sturm, J., Stachniss, C., Pradeep, V., Plagemann, C., Konolige, K., & Burgard, W. (2009) · Cited by 2
Improving Weakly-Supervised Video Instance Segmentation by Leveraging Spatio-Temporal Consistency
Arefi, F., Mansourian, A. M., & Shohreh Kasaei, B. (2025) · IEEE Access · doi:10.1109/access.2025.3550435 · Cited by 2
“These compact In Table 8, keypoints are obtained using the SIFT algorithm [82].”
Effect of Salient Features in Object Recognition
Ahmad, K., Ahmad, N., Haider, K., et al. (2013) · International Journal of Computer Applications · doi:10.5120/9991-4839 · Cited by 1
On-line visual tracking with feature-based adaptive models
Li, L. (2009) · doi:10.14288/1.0051115 · Cited by 1
Face Sketch Matching Using Speed up Robust Feature Descriptor
Bansode, N. K., & Sinha, P. K. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-4859-3_38 · Cited by 1
Deep Graph Laplacian Hashing for Image Retrieval
Ge, J., Liu, X., Hong, R., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77380-3_1 · Cited by 1
Visual Guidance and Automatic Control for Robotic Personalized Stent Graft Manufacturing
Guo, Y. B., Sun, M., Lo, F. P.-W., et al. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794123 · Cited by 1
The histological development of the fetal human inferior colliculus during the second trimester
Nanda, R., Bota, M., Jayakumar, J., et al. (2025) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2024.1502778 · Cited by 1
“In order to create data volumes, the Nissl stained 2D images were first down-sampled to 60 μ m per pixel, and then padded and stack aligned using the scale invariant features tool (SIFT) as a feature of ImageJ/Fiji (Lowe, 2004; RRID:SCR_002285).”
Systems support for stereoscopic video compression
Feng, W., Liu, F., Niu, Y., et al. (2011) · International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) · doi:10.1145/1989240.1989265 · Cited by 1
A FWCL-based method for visual vocabulary formation
Zhang, S., Zhang, J., Guo, P., Chu, M., & Chang, K. H. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2313-7 · Cited by 1
End-to-End Large-Scale Image Retrieval Network with Convolution and Vision Transformers
Zhang, Q., Bao, F., Su, X., Wang, W., & Gao, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15937-4_52 · Cited by 1
Exploration in improving retrieval quality and robustness for deformable non-rigid 3D shapes
Kuang, Z., Li, Z., Jiang, X., et al. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2170-4 · Cited by 1
Ye, H., Zhang, S., Li, L., et al. (2024) · IEEE International Conference on Signal, Information and Data Processing (ICSIDP) · doi:10.1109/icsidp62679.2024.10869159 · Cited by 1
“Traditional methods for image registration can be categorized into feature-based [3] and area-based [4] methods.”
Enhancement Algorithm for Sandstorm Images
Hua, Z., & Nie, B. (2021) · International Conference on Control, Robotics and Intelligent System (CCRIS) · doi:10.1145/3483845.3483893 · Cited by 1
Multi-scale reflection invariance
Henderson, C., & Izquierdo, E. (2016) · 2016 SAI Computing Conference (SAI) · doi:10.1109/sai.2016.7556016 · Cited by 1
A Proposed Method to Improve Quality of Image Using Panorama Technology for WSNs
Manh, T. N., Huu, P. N., & Tran-Quang, V. (2015) · British Journal of Mathematics & Computer Science · doi:10.9734/bjmcs/2015/17632 · Cited by 1
Novel LabVIEW Implementation of FREAK Based Image Mosaicing and Object Tracking Algorithms
Lati, A., Mekhermeche, H., Benhellal, B., et al. (2020) · doi:10.1109/icee49691.2020.9249909 · Cited by 1
Survey on Image Retrieval Based on Rotation, Translation and Scaling Invariant Features
Al-Shuibi, A., Aldarawani, A., Al-Homaidi, H., et al. (2019) · doi:10.1109/icoice48418.2019.9035191 · Cited by 1
Tracking Track Targets in External Store Separation Using Computer Vision
Kusomoto, A. Y., de Vasconcelos, L. E. G., Leite, N. P. O., et al. (2014) · UA Campus Repository (The University of Arizona) · Cited by 1
Reducing the cost of visual DL datasets
Osteen, P. R., Owens, J. L., & Kaukeinen, B. (2019) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.2519114 · Cited by 1
Design of Human Facial Feature Recognition System
Mallick, P., Ankur, A., & Patra, D. (2012) · Cited by 1
Keypoint Detection and Description for Raw Bayer Images
Lin, J., Zhang, J., & Lu, G. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128220 · Cited by 1
“From left to right: the input Bayer image pairs and the outputs of our model, DISK [30], ALIKED [33], SuperPoint [4], and SIFT [9].”
InfluentialScene Categorization with Classified Codebook Model
Yang, X., Xu, D., Feng, S., et al. (2011) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e94.d.1349 · Cited by 1
Web Image Gathering with a Spatial Pyramid Kernel
Yanai, . (2007) · IEEE International Symposium on Multimedia Workshops (ISMW) · doi:10.1109/ism.workshops.2007.59 · Cited by 1
Joint Feature Selection for Object Detection and Recognition
Weinman, J. J., Hanson, A. R., & Miller, E. G. (2006) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 1
Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans
Jawarneh, M. S. (2016) · International Journal of Intelligent Information Systems · doi:10.11648/j.ijiis.20160501.12 · Cited by 1
A QUALITY CONTROL SYSTEM PROTOTYPE FOR DETECTING KNOT DEFECTS IN THE WOODEN PANEL MANUFACTURING
KILIÇ, Ö., SUSUZ, D. M., & Süzek, B. E. (2019) · Mugla Journal of Science and Technology · doi:10.22531/muglajsci.473338 · Cited by 1
An Optimal Viewpoint-Guided Visual Indexing Method for UAV Autonomous Localization
Ye, Z., Zheng, Y., Ji, Z., et al. (2025) · Remote Sensing · doi:10.3390/rs17132194 · Cited by 1
Accurate Measurements of High Strain Gradients near Notches Using a Feature-Based DIC Algorithm
Gonzáles, G. L. G., Rodrigues, L. D., & Freire, J. L. F. (2017) · Journal of Mechanics Engineering and Automation · doi:10.17265/2159-5275/2017.01.004 · Cited by 1
Research on image features classification based on graph convolutional neural network
Xu, X. (2023) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1117/12.3004260 · Cited by 1
Efficient Similarity Search via Sparse Coding
Cherian, A., & Morellas, V. (2011) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 1
Stereo Visual Odometry With Windowed Bundle Adjustment
Angeles, L. (2015) · Cited by 1
Assessment of Small Object Detection Algorithm Based on Improved YOLOv5s
Meng, J., & Shen, S. (2024) · Journal of Engineering Research and Reports · doi:10.9734/jerr/2024/v26i91263 · Cited by 1
Advancing robotic automation in wood-framed construction using vision-driven adaptive control
Xie, C., & Alwisy, A. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.106858 · Cited by 1
A multi-scale surface target recognition algorithm based on attention fusion mechanism
Guo, R., Su, S., Zuo, Z., et al. (2021) · doi:10.1109/csaiee54046.2021.9543180 · Cited by 1
A REVIEW OF VISUAL ODOMETRY FOR UAV AUTONOMOUS NAVIGATION
Khachatryan, T. B. (2023) · doi:10.53297/18293336-2023.1-9 · Cited by 1
Location Prediction of Social Images via Generative Model
Zhang, X., Li, Z., Wang, S., et al. (2015) · ACM International Conference on Multimedia Retrieval (ICMR) · doi:10.1145/2671188.2749308 · Cited by 1
Complex Cell Descriptor Learning for Robust Object Recognition
Wang, Z., Huang, Y., Luo, S., et al. (2011) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e94.d.1502 · Cited by 1
Discriminative k-means clustering
Arandjelović, O. (2013) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6707038 · Cited by 1
Core Algorithm for Structural Verification of Keypoint Matches
Malashin, R. O. (2017) · Intelligent systems reference library · doi:10.1007/978-3-319-67516-9_9 · Cited by 1
Elleuch, Z., & Marzouki, K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38628-2_39 · Cited by 1
An Efficient Index for Visual Search in Appearance-based SLAMPreprint
Hajebi, K., & Zhang, H. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.7170 · Cited by 1
DSM generation on the shade of tree area of aerial photogrammetry
Hwang, J.-T., Chen, Y.-W., Lian, W.-Y., et al. (2015) · International Conference on Geoinformatics · doi:10.1109/geoinformatics.2015.7378601 · Cited by 1
Unified Image Similarity Detection Using Neural Networks and Feature Metrics
Prathima, C., Babu, G. M., Koushik, A., et al. (2025) · doi:10.1109/icdsaai65575.2025.11011644 · Cited by 1
“To address this, the proposed approach uses more than one similarity measure including the Structural Similarity Index (SSIM) [14], histogram similarity [13], perceptual hash similarity, key point similarity [15] as well as the CNN feature extractor.”
Tian, Y., Pan, Z., Li, L., et al. (2018) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2018.8665144 · Cited by 1
Chen, T., Rushworth, A. G. A., & Jia, F. (2026) · Journal of Field Robotics · doi:10.1002/rob.70256 · Cited by 1
Trademark Image Retrieval Using Inverse Total Feature Frequency and Multiple Detectors
Mori, M., Wu, X., & Kashino, K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23192-1_65 · Cited by 1
A Network-Centric Epidemic Approach for Automated Image Label Annotation
Bohlool, M., de Menezes, R. P., & Ribeiro, E. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-25501-4_14 · Cited by 1
Modeling Local Word Spatial Configurations for Near Duplicate Document Image Retrieval
Liu, L., Lu, Y., Suen, C. Y., et al. (2013) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2013.54 · Cited by 1
Automatic Estimation for Parameters of Image Projective Transforms Based on Object-Invariant Cores
Lutsiv, V. R. (2014) · Intelligent systems reference library · doi:10.1007/978-3-319-10653-3_5 · Cited by 1
Xu, X., Zhang, L., Yue, J.-B., Zhong, H., Wang, Y., Liu, J., Lu, Y., & Qiao, H.-B. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2024.2305631 · Cited by 1
“The Scale-Invariant Feature Transform (SIFT) algorithm, originally proposed by David G. Lowe (Lowe 1999, 2004), plays a pivotal role in this context.”
MethodStudy on the fusion of MBS image and SSS image
Zhang, H., Zhao, J., & Yang, K. (2010) · OCEANS'10 IEEE SYDNEY · doi:10.1109/oceanssyd.2010.5603812 · Cited by 1
Overcoming labelled data scarcity for defect classification in scanning tunnelling microscopy
Kolev, N. L., Trouton, M., Canova, F. F., Thornton, G., Gao, D. Z., Curson, N. J., & Stock, T. J. Z. (2026) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae520e · Cited by 1
“The hysteresis correction algorithm followed the method of Zhang et al [42], with an additional filtering step to remove inaccurate matches produced by the SIFT algorithm [56].”
Query-Adaptive Image Search Re-ranking Using Deep Convolutional Neural Network Feature
Lin, B., & Tian, X. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.382 · Cited by 1
Robust Place Recognition using an Imaging LidarPreprint
Shan, T., Englot, B., Duarte, F., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.02111 · Cited by 1
TÜBITAK UZAY at TRECVID 2009: High-Level Feature Extraction and Content-Based Copy Detection.
Saracoğlu, A., Esen, E., Soysal, M., et al. (2009) · Cited by 1
Panoramic View of Human Jaw Under Ambiguity Intraoral Camera Movement
Ghanoum, M., Ali, A., Elshazly, S., et al. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098562 · Cited by 1
Hou, C., & Liu, P. (2026) · Fire · doi:10.3390/fire9050182 · Cited by 1
Image-based magnification calibration for electron microscope
Ito, K., Suzuki, A., Aoki, T., et al. (2013) · Machine Vision and Applications · doi:10.1007/s00138-013-0511-3 · Cited by 1
Exploiting parallax in panoramic capture to construct light fields
Dansereau, D. G., Wood, D. A., Montabone, S., & Williams, S. B. (2014) · QUT ePrints (Queensland University of Technology) · Cited by 3
Block classification based edge detector and object localizer
Benaissa, M., & Bennia, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6821-8 · Cited by 1
Integration of content and context modalities for multimedia big data retrieval
Zhu, Q. (2014) · Cited by 1
Evolutionary data purification for social media classification
James, S., & Collomosse, J. P. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900039 · Cited by 1
UEC at TRECVID 2011 SIN and MED task
Hizume, K., & Yanai, . (2011) · Cited by 1
Research on Image Recognition Methods Based on Deep Learning
Qin, C., & Cui, X. (2024) · doi:10.1109/mvipit65697.2024.00029 · Cited by 1
Bhattacharjee, S., Hwang, Y.-B., Carole, K. S., Kim, H., Moon, D., Cho, N.-H., & Choi, H. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47457-6_1 · Cited by 1
Bai, S., Matsumoto, T., Takeuchi, Y., Kudo, H., & Ohnishi, N. (2012) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e95.d.3060 · Cited by 1
Fundamental matrix estimation for binocular vision measuring system used in wild field
Yan, N., Wang, X., & Liu, F. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2070319 · Cited by 1
Image Correspondence from Motion Subspace Constraint and Epipolar Constraint
Sugimoto, S., Takahashi, H., & Okutomoi, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-76390-1_38 · Cited by 1
Large Parallax Image Mosaic Algorithm Based on Deep Learning and APAP Model
熊, 禹. (2023) · Journal of Image and Signal Processing · doi:10.12677/jisp.2023.122011 · Cited by 1
Harris feature vector descriptor
Wang, X., Su, J., & Cheng, H. D. (2010) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2010.5581008 · Cited by 1
Yu, A., Tong, W., Wang, Z., et al. (2025) · Remote Sensing · doi:10.3390/rs17050749 · Cited by 1
Improve Scene Classification by Using Feature and Kernel Combination
Yuan, L., Chen, F., Zhou, L. J., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42057-3_17 · Cited by 1
Optimising Image Feature Extraction and Selection: A Comprehensive Review With Spark Case Studies
Figueira-Domínguez, J. G., Remeseiro, B., & Bolón‐Canedo, V. (2026) · Expert Systems · doi:10.1111/exsy.70188 · Cited by 1
“SIFT and SURF are well-established methods known for their robustness to changes in scale, rotation and illumination, while KAZE introduces improvements in multiscale detection by operating in a nonlinear scale space.”
InfluentialToF camera ego-motion estimation
Ratshidaho, T., Tapamo, J., Claassens, J., et al. (2012) · doi:10.1109/robomech.2012.6558458 · Cited by 1
Geometry-aware Similarity Learning on SPD Manifolds for Visual RecognitionPreprint
Huang, Z., Wang, R., Li, X., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.04914 · Cited by 1
ℓ q -Norm Sample-Adaptive Multiple Kernel Learning
Wang, Q., Liu, X., & Xu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2974903 · Cited by 1
GO-PILL: A Geometry-Aware OCR Pipeline for Reliable Recognition of Debossed and Curved Pill Imprints
Jo, J., Yoon, S., & Cho, J. (2026) · Mathematics · doi:10.3390/math14020356 · Cited by 2
Hybrid annotation alignment-based multi-region crop model for high-resolution image
Yuan, Y., Xu, S., Cheng, J., et al. (2025) · The Visual Computer · doi:10.1007/s00371-025-04033-1 · Cited by 1
Automated mosaicing for improving vehicle situational awareness in real time
Nam, D., & Aouf, N. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995867 · Cited by 1
The loop closure Detection Algorithm Based on Bag of Semantic Word For Robot Navigation
Yu, P., Ruan, X., & Zhu, X. (2020) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba50161.2020.9277317 · Cited by 1
Vision-Based Automated Target Tracking System for Robotic Applications
Pandey, A. K., Krishna, K. Y. V., & Babu, R. M. S. (2018) · Lecture notes in mechanical engineering · doi:10.1007/978-981-10-8597-0_34 · Cited by 1
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
