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 23,601–23,700 of 56,201
An automated eye disease prediction system using bag of visual words and support vector machine
Sundaram, R., & Ravichandran, K. S. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169963 · Cited by 8
Object tracking based on ORB and temporal-spacial constraint
Wu, S., Fan, Y., Zheng, S., et al. (2012) · IEEE International Conference on Advanced Computational Intelligence · doi:10.1109/icaci.2012.6463235 · Cited by 8
Low resolution remote sensing object detection with fine grained enhancement and swin transformer
Xu, Z., Wang, X., Huang, K., & Chen, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-10286-6 · Cited by 10
“For instance, methods such as Histogram of Oriented Gradients (HOG) 14 and Scale-Invariant Feature Transform (SIFT) 15 demanded substantial time and human resources for data processing.”
Registration of 3D Geometric Model and Color Images Using SIFT and Range Intensity Images
Inomata, R., Terabayashi, K., Umeda, K., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24028-7_30 · Cited by 8
Ranking canonical views for tourist attractions
Yang, L., Johnstone, J. K., & Zhang, C. (2009) · Multimedia Tools and Applications · doi:10.1007/s11042-009-0345-1 · Cited by 8
Object Detection and Recognition Using Small Labeled Datasets
Raj, A., Gandhi, K., Nalla, B. T., et al. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1135-2_31 · Cited by 8
Singh, J., & Singh, C. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101823 · Cited by 8
Rotation invariant feature lines transform for image matching
Ye, Z., & Qu, H. (2014) · Journal of Electronic Imaging · doi:10.1117/1.jei.23.5.053002 · Cited by 8
Santarossa, M., Tatli, A., von der Burchard, C., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12081780 · Cited by 8
COTR: Correspondence Transformer for Matching Across ImagesPreprint
Jiang, W., Trulls, E., Hosang, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14167 · Cited by 8
Image-Feature-Based Integrated Path Planning and Control for Spacecraft Rendezvous Operations
Zhao, X., & Zhang, S. (2022) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g006325 · Cited by 8
Seamless Virtual Reality With Integrated Synchronizer and Synthesizer for Autonomous Driving
Li, H., Han, R., Zhao, Z., Xu, W. D., Hao, Q., Wang, S., & Xu, C. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3375266 · Cited by 9
“• SIFT BF Matcher , which finds and matches features with the SIFT algorithm [22] and brute-force matcher.”
MethodVisual defect obfuscation based self-supervised anomaly detection
Park, Y., Kang, S., Kim, M. J., Lee, Y., Kim, H. S., & Yi, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-69698-5 · Cited by 12
Speeding up the Detection of Line Drawings Using a Hash Table
Sun, W., & Kise, K. (2009) · Chinese Conference on Pattern Recognition · doi:10.1109/ccpr.2009.5343969 · Cited by 8
Using Top-Points as Interest Points for Image Matching
Platel, B., Balmachnova, E., Florack, L. M. J., et al. (2005) · Lecture notes in computer science · doi:10.1007/11577812_19 · Cited by 8
Automatic model‐based semantic registration of multimodal MRI knee data
Xue, N., Döllinger, M., Fripp, J., Ho, C. P., Surowiec, R. K., & Schwarz, R. (2014) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24609 · Cited by 8
Scale Invariant Feature Transform (SIFT) Parametric Optimization Using Taguchi Design of Experiments
Maestas, D. R., Lumia, R., Starr, G., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16584-9_61 · Cited by 8
Learning invariant object recognition from temporal correlation in a hierarchical network
Leßmann, M., & Würtz, R. P. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.02.011 · Cited by 8
Improvements in image categorization using codebook ensembles
Luo, H., Wei, H., & Hu, F. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.08.005 · Cited by 8
Enhancing collaborative road scene reconstruction with unsupervised domain alignment
Venator, M., Aklanoglu, S., Bruns, E., & Maier, A. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01144-8 · Cited by 8
Fu, Q., Luo, Y., Wen, Y., Tao, D., Li, Y., & Duan, L. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2791803 · Cited by 7
Masataka, S., & Hashimoto, M. (2013) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.133.1010 · Cited by 8
Multi-order visual phrase for scalable image search
Zhang, S. S., Tian, Q., Huang, Q., et al. (2013) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2499788.2499833 · Cited by 8
Spatio-Temporal Scale Selection in Video Data
Lindeberg, T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58771-4_1 · Cited by 8
SAR Image Registration Based on ROEWA-Blocks and Multiscale Circle Descriptor
Hong, Y., Leng, C., Zhang, X., et al. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3119923 · Cited by 8
Multi-Head Deep Metric Learning Using Global and Local Representations
Ebrahimpour, M. K., Qian, G., & Beach, A. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00141 · Cited by 8
A Distributed Dynamic Parallel Algorithm for SIFT Feature Extraction
Jiang, G., Zhang, G., & Zhang, D. (2010) · International Symposium on Parallel Architectures, Algorithms and Programming · doi:10.1109/paap.2010.58 · Cited by 8
A detection method for low-pixel ratio object
Zhang, R., Yin, D., Ding, J., Luo, Y., Liu, W., Yuan, M., Zhu, C., & Zhou, Z. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6653-6 · Cited by 8
Optimized 3D mapping of a large area with structures using multiple multirotors
Darrah, M., Trujillo, M. M., Speransky, K., et al. (2017) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2017.7991414 · Cited by 8
Detection of Two-Wheeler Traffic Rule Violation Using Deep Learning
Arshad, M., & Kumar, P. (2022) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic55036.2022.9848979 · Cited by 8
Norouzi, J., Helfroush, M. S., Liaghat, A., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124291 · Cited by 8
Automated Recovery of Structural Drawing Images Collected from Postdisaster Reconnaissance
Yeum, C. M., Lund, A., Dyke, S. J., et al. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000798 · Cited by 8
COST292 experimental framework for TRECVID 2006
Zhang, Q., Corvaglia, M., Aksoy, S., et al. (2006) · Institutional Research Information System (Università degli Studi di Brescia) · Cited by 8
Patel, H. M., Patel, A. P. P. J., & Patel, A. P. M. S. G. (2013) · Cited by 8
3D Human Face Recognition Using Sift Descriptors of Face’s Feature Regions
Quy, N. H., Quoc, N. H., Anh, N. T. L., et al. (2014) · Studies in computational intelligence · doi:10.1007/978-3-319-10774-5_11 · Cited by 8
Feature Selection Algorithms for Plant Leaf Classification: A Survey
Choudhary, M. K., & Hiranwal, S. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-5077-5_59 · Cited by 9
MultiLevel Feature Pooling Network for Uncalibrated Stereo Rectification in Autonomous Vehicles
Nguyen, T. P., Tran, T. H.-P., & Jeon, J. W. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3026307 · Cited by 8
Topoğrafik Yüzey Değişimlerinin Görüntü İşleme Teknikleriyle Belirlenmesi Üzerine Bir Araştırma
Zeybek, M., & Şanlıoğlu, İ. (2019) · Doğal Afetler ve Çevre Dergisi · doi:10.21324/dacd.531719 · Cited by 8
Local feature based retrieval approach for iris biometrics
Mehrotra, H., & Majhi, B. (2013) · Frontiers of Computer Science · doi:10.1007/s11704-013-3073-7 · Cited by 8
Looking for Hand Biometrics Interoperability
González, E., Morales, A., Ferrer, M. A., et al. (2011) · doi:10.1109/ichb.2011.6094313 · Cited by 8
Said, I. A., Sagan, V., Peterson, K. T., Alifu, H., Maiwulanjiang, A., Stylianou, A., Akkad, O. A., Sarkar, S., & Shakarji, N. A. (2025) · Sensors · doi:10.3390/s25020303 · Cited by 9
“The image on the right shows the result of w scale w VNIR h scale h Solutions from the public domains that we tested did not produce results within acceptable margins of errors for various reasons—for instance, applying traditional co-reg-istration methods using feature detection and matching, such as Scale-Invariant…”
InfluentialSIASM: Sparsity-based image alignment and stitching method for robust image mosaicking
Li, Y., & Monga, V. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532674 · Cited by 8
Automatic emotion recognition in the wild using an ensemble of static and dynamic representations
Ghazi, M. M., & Ekenel, H. K. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997634 · Cited by 8
Epipolar geometry and log-polar transform in wide baseline stereo matching
Palander, K., & Brandt, S. S. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761515 · Cited by 8
Automated Cephalometric Landmark Identification Using Shape and Local Appearance Models
Keustermans, J., Mollemans, W., Vandermeulen, D., et al. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.603 · Cited by 8
The methodology of the archival aerial image orientation based on the SfM method
Karwel, A. K., & Markiewicz, J. S. (2022) · Sensors and Machine Learning Applications · doi:10.55627/smla.001.02.0015 · Cited by 8
A Review of Using Visual Odometery Methods in Autonomous UAV Navigation in GPS-Denied Environment
Rostum, H. M., & Vásárhelyi, J. (2023) · Acta Universitatis Sapientiae Electrical and Mechanical Engineering · doi:10.2478/auseme-2023-0002 · Cited by 10
Image mosaic with relaxed motion
Fang, X., Zhu, J., & Luo, B. (2010) · Signal Image and Video Processing · doi:10.1007/s11760-010-0194-4 · Cited by 8
Multimodel Fusion Method for Cloud Detection in Satellite Laser Footprint Images
Tang, X., Yao, J., Chen, J., Li, G., & Zhang, W. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3192067 · Cited by 8
L2SSP: Robust keypoint description using local second-order statistics with soft-pooling
Song, T., Meng, F., Wu, Q., Luo, B., Zhang, T., & Xu, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.12.021 · Cited by 8
Efficient homography estimation method for panorama
Seo, S., Jeong, S., & Lee, S. (2013) · doi:10.1109/fcv.2013.6485489 · Cited by 8
Assisting Attraction Classification by Harvesting Web Data
Shen, J., Zheng, E., Cheng, Z., & Deng, C. D. (2017) · IEEE Access · doi:10.1109/access.2017.2656878 · Cited by 8
Indian dance classification using machine learning techniques: A survey
Gupta, S., & Singh, S. (2024) · Entertainment Computing · doi:10.1016/j.entcom.2024.100639 · Cited by 10
GRAPH MATCHING USING SIFT DESCRIPTORS - An Application to Pose Recovery of a Mobile Robot
Sanromà, G., Alquézar, R., & Serratosa, F. (2010) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0002829702490254 · Cited by 7
Frequency Domain Directional Filtering Based Rain Streaks Removal from a Single Color Image
Liu, C., Pang, Y., Wang, J., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09333-8_45 · Cited by 8
Piccinini, F., Tazzari, M., Tumedei, M. M., Stellato, M., Remondini, D., Giampieri, E., Martinelli, G., Castellani, G., & Carbonaro, A. (2024) · Sensors · doi:10.3390/s24020451 · Cited by 8
DRAGON: Drone and Ground Gaussian Splatting for 3D Building Reconstruction
Ham, Y., Michalkiewicz, M., & Balakrishnan, G. (2024) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccp61108.2024.10644903 · Cited by 14
Improved 3D sparse maps for high-performance SFM with low-cost omnidirectional robots
Cavestany, P., Rodríguez, A. L., Barberá, H. M., et al. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351744 · Cited by 8
Regional Varying Image Super-Resolution
Amintoosi, M., Fathy, M., & Mozayani, N. (2009) · International Joint Conference on Computational Sciences and Optimization (CSO) · doi:10.1109/cso.2009.278 · Cited by 8
A Feature-Based Forensic Procedure for Splicing Forgeries Detection
Amerini, I., Becarelli, R., Caldelli, R., & Casini, M. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/653164 · Cited by 8
LAT: Local area transform for cross modal correspondence matching
Ryu, S., Kim, S., & Sohn, K. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.006 · Cited by 8
Graph Mining for Object Tracking in Videos
Diot, F., Fromont, É., Jeudy, B., Marilly, E., & Martinot, O. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33460-3_31 · Cited by 7
Puig, L., Sturm, P. F., & Guerrero, J. J. (2012) · Machine Vision and Applications · doi:10.1007/s00138-012-0424-6 · Cited by 8
Military Image Scene Recognition Based on CNN and Semantic Information
Chen, C., Huang, J., Pan, C., et al. (2018) · International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · doi:10.1109/icmcce.2018.00126 · Cited by 8
Image Mosaic Using Speeded Up Robust Feature Detection
Murali, Y., Tech, M., & Mahesh, V. G. V. (2012) · Cited by 8
Pedestrian Detection Based on Two-Stream UDN
Wang, W., Wang, L., Ge, X., Li, J., & Yin, B. (2020) · Applied Sciences · doi:10.3390/app10051866 · Cited by 7
An Analysis-by-Synthesis Approach to Tracking of Textiles
Hasler, N., Rosenhahn, B., Asbach, M., et al. (2007) · doi:10.1109/wmvc.2007.7 · Cited by 8
Accelerating the construction of BRIEF descriptors using an FPGA-based architecture
de Lima, R., Martínez-Carranza, J., Morales-Reyes, A., et al. (2015) · International Conference on ReConFigurable Computing and FPGAs (ReConFig) · doi:10.1109/reconfig.2015.7393285 · Cited by 8
Robust matching of multi-modal retinal images using radon transform based local descriptor
Bathina, Y. B., Medathati, M. V. K., & Sivaswamy, J. (2010) · doi:10.1145/1882992.1883108 · Cited by 8
A Robust Method for Mosaicking Sequence Images Obtained from UAV
Cheng, X., Wang, J., & Xu, Y. (2010) · International Conference on Information Engineering and Computer Science · doi:10.1109/iciecs.2010.5678358 · Cited by 8
YOLO based Intelligent Tracking System for Curling Sport
Zhang, S., Lan, S., Bu, Q., et al. (2019) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis46139.2019.8940229 · Cited by 8
3D Urban Reconstruction from Wide Area Aerial Surveillance Video
Kang, Z., & Medioni, G. G. (2015) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2015.17 · Cited by 7
A Deep-Learning-Based Detection Method for Small Target Tomato Pests in Insect Traps
Wang, S., Daqing, C., Xiang, J., & Zhang, C. (2024) · Agronomy · doi:10.3390/agronomy14122887 · Cited by 14
The number of all possible meaningful or discernible pictures
Pavlidis, T. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.07.001 · Cited by 8
PL$$_1$$P - Point-Line Minimal Problems Under Partial Visibility in Three Views
Duff, T., Kohn, K., Leykin, A., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_11 · Cited by 8
Reduced Scattering Representation for Malayalam Character Recognition
Manjusha, K., Kumar, M. A., & Soman, K. P. (2017) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-017-2945-9 · Cited by 8
Zhao, J., Li, J., & Ma, Y. (2018) · International Conference on Computer Technology Applications (ICCTA) · doi:10.1109/cata.2018.8398672 · Cited by 8
Vision-based mobile robot localization and mapping using the PLOT features
Lin, R., Wang, Z., Sun, R., et al. (2012) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2012.6285115 · Cited by 8
Semi-automatic soft collaborative annotation for semantic video indexing
Ksibi, A., Elleuch, N., Ammar, A. B., et al. (2011) · IEEE EUROCON - International Conference on Computer as a Tool (EUROCON) · doi:10.1109/eurocon.2011.5929417 · Cited by 8
Memory efficient recognition of specific objects with local features
Kise, K., Kazuto, N., & Iwamura, M. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761711 · Cited by 8
A lowbit rate vocabulary coding scheme for mobile landmark search
Ji, R., Duan, L., Chen, J., et al. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946946 · Cited by 8
Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities
Khosla, A., Yao, B., & Fei-Fei, L. (2014) · Springer eBooks · doi:10.1007/978-3-319-05491-9_5 · Cited by 8
TAM-TR: Text-guided attention multi-modal transformer for object detection in UAV images
Xu, J., Fan, X., Jian, H., Chen, X., Bei, W., Ge, Q., Zhao, T., & Han, R. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.04.027 · Cited by 9
Sheta, B. I. (2012) · International Journal of Computer Science & Engineering Survey · doi:10.5121/ijcses.2012.3502 · Cited by 8
Persistent maritime surveillance using multi-sensor feature association and classification
van den Broek, S. P., Schwering, P. B. W., Liem, K. D., et al. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.920892 · Cited by 8
Stereo one-shot six-band camera system for accurate color reproduction
Tsuchida, M., Sakai, S., Miura, M., et al. (2013) · Journal of Electronic Imaging · doi:10.1117/1.jei.22.3.033025 · Cited by 8
Feng, B., Chen, F., Liu, G., et al. (2015) · Applied Optics · doi:10.1364/ao.54.004130 · Cited by 8
Content-based synchronization for multiple photos galleries
Broilo, M., Boato, G., & Natale, F. G. B. D. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467267 · Cited by 8
Improved Vehicle Detection Method Using Feedback-Ada Boost Learning
Kim, J. (2013) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2013.v5.650 · Cited by 8
Generation of Chinese ink portraits by blending face photographs with Chinese ink paintings
Chiang, P.-Y., Lin, C.-V., & Tseng, C.-H. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.02.002 · Cited by 8
Unifying Spatial and Attribute Selection for Distracter-Resilient Tracking
Jiang, N., & Wu, Y. N. (2014) · 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.2014.448 · Cited by 8
Efficient Multi-structure Robust Fitting with Incremental Top-k Lists Comparison
Wong, H. S., Chin, T.-J., Yu, J., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19282-1_44 · Cited by 8
Proß, B., Schöning, J., & Krüger, A. (2009) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/1613858.1613941 · Cited by 8
From coarse to fine: multi-level feature fusion network for fine-grained image retrieval
Wang, S., Wang, Z., Wang, N., Wang, H., & Li, H. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00899-6 · Cited by 8
Performance evaluation of object recognition tasks in visual sensor networks
Baroffio, L., Cesana, M., Redondi, A. E. C., et al. (2014) · International Teletraffic Congress · doi:10.1109/itc.2014.6932943 · Cited by 8
HartSift: A High-Accuracy and Real-Time SIFT Based on GPU
Li, Z., Jia, H., & Zhang, Y. (2017) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2017.00029 · Cited by 8
Liu, S., Jiang, S., Liu, Y., Xue, W., & Guo, B. (2022) · Remote Sensing · doi:10.3390/rs14215619 · Cited by 8
Label-to-region with continuity-biased bi-layer sparsity priors
Liu, X., Yan, S., Cheng, B., et al. (2012) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2379790.2379792 · Cited by 8
Zhang, B., Liu, Y., Zhang, Z., et al. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.045010 · Cited by 8
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
