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 36,501–36,600 of 56,201
Steps towards the object semantic hierarchy
Xu, C. (2011) · Cited by 2
Nie, X., Li, X., Sun, J., & Yin, Y. (2017) · doi:10.1145/3132734.3132738 · Cited by 2
Enhanced smartphone imaging: Assessment of deep learning based image signal processing pipeline
Pal, A., Sehgal, P., & Bansal, R. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105179 · Cited by 2
Mohanraj, V., Vaidehi, V., Kumar, R., et al. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2653-6_5 · Cited by 2
Semantic-aware visual localization for robust aerial perception
Gao, Y., Liu, T., Ren, K., et al. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.111294 · Cited by 2
A Multi-scale Phase Method for Content Based Image Retrieval
Chen, X., Zhang, R., Liu, Z., et al. (2007) · International Conference on Image and Graphics · doi:10.1109/icig.2007.4 · Cited by 2
Matsuyama, Y., Shikano, A., Iwase, H., et al. (2015) · doi:10.1109/ijcnn.2015.7280423 · Cited by 2
Improving Human Action Recognition by Non-action ClassificationPreprint
Wang, Y., & Hoai, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06397 · Cited by 2
Object Tracking Based on Particle Filter and Scale Invariant Feature Transform
Jiang, M., Zhang, L., & Huang, Y. (2010) · doi:10.1109/icmult.2010.5631001 · Cited by 2
Image Visual Similarity Based on High Level Features of Convolutional Neural Networks
Poghosyan, A., & Sarukhanyan, H. G. (2016) · Mathematical Problems of Computer Science · Cited by 2
Analysis Of Paintings Using Multi-Sensor Data
Almeida, P., Correia, N., Jesus, R. M., et al. (2013) · INFM-OAR (INFN Catania) · doi:10.5281/zenodo.43600 · Cited by 2
Web image co-clustering based on tag and image content fusion
Chen, J., Tan, J., Yin, X., et al. (2010) · IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) · doi:10.1109/icnidc.2010.5657793 · Cited by 2
Huang, F., Wang, X., Chen, Y., et al. (2024) · Optics Express · doi:10.1364/oe.524154 · Cited by 2
Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
Cui, H., Zhao, Z., & Zhang, F. (2023) · Applied Sciences · doi:10.3390/app132212309 · Cited by 2
Efficient computation of histograms on densely overlapped polygonal regions
Zhang, Y., Wang, Y., Pan, G., et al. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.02.027 · Cited by 2
Zemzemoglu, M., & Ünel, M. (2025) · Composites Part B Engineering · doi:10.1016/j.compositesb.2025.112951 · Cited by 2
Image Matching Techniques: A Review
Kaur, R., & Devendran, V. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0098-3_74 · Cited by 2
Visual Measurement of Material Segregation in Steel Wires
Cervinka, L., & Horák, K. (2014) · Procedia Engineering · doi:10.1016/j.proeng.2014.03.021 · Cited by 2
UAS-Based Multi-Temporal Rice Plant Height Change Prediction
Lin, Y., He, J., Liu, G., et al. (2023) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.22-00107r2 · Cited by 2
Zhao, L., Wan, L., & Ma, L. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10642225 · Cited by 2
“We incorporate boundary information into extracted features layer by layer, which refer to the process of constructing a difference-of-Gaussian (DoG) pyramid in SIFT [8].”
MethodActive Perception System for Enhanced Visual Signal Recovery Using Deep Reinforcement Learning
Chaudhary, G., Behera, L., & Sandhan, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097084 · Cited by 3
Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images
Zukal, M., Beneš, R., Číka, P., et al. (2013) · Measurement Science Review · doi:10.2478/msr-2013-0049 · Cited by 2
Learning-Based Local Visual Representation and Indexing
(2015) · Elsevier eBooks · doi:10.1016/c2014-0-01997-1 · Cited by 2
Visual Topological Mapping Using an Appearance-Based Location Selection Method
Khan, M. A., & Labrosse, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63486-5_12 · Cited by 2
INESC, Porto at TRECVID 2007: Automatic and Interactive Video Search.
Calistru, C., Ribeiro, C., David, G., et al. (2007) · Cited by 2
Chu, T., Starek, M. J., Brewer, M. J., et al. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127962 · Cited by 2
Multiple level visual semantic fusion method for image re-ranking
Qi, S., Wang, F., Wang, X., et al. (2015) · Multimedia Systems · doi:10.1007/s00530-014-0448-z · Cited by 2
Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image
Ketmaneechairat, H., Maliyaem, M., & Intarat, C. (2021) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj060413 · Cited by 2
Gartner, C. M., Mathew, J. K., & Bullock, D. M. (2024) · Future Transportation · doi:10.3390/futuretransp4040062 · Cited by 3
“SIFT SIFT (Scale Invariant Feature Transformation), first published by Lowe [12], utilizes distinctive image features.”
SPEN: Sub-Pixel Position Error Estimation Network for Multi-Modal Image Matching
Hu, M., Sun, B., Li, S., et al. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3656936 · Cited by 2
“While it improves the performance of many pixel-level matching methods, it tends to perform poorly when applied to detectors that already achieve sub-pixel accuracy, such as SIFT [13].”
A Novel Telugu Script Recognition and Retrieval Approach Based on Hash Coded Hamming
Lakshmi, K. M., & Babu, T. R. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-0212-1_58 · Cited by 2
Boulekchour, M., Aouf, N., & Richardson, M. A. (2014) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/0954410014562261 · Cited by 2
On-line object segmentation through human-robot interaction
Kim, S., Kim, D. H., & Park, S.-K. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5651041 · Cited by 2
Nizar, T. N., Supatmi, S., & Putro, E. P. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/662/5/052004 · Cited by 2
Khokthong, W. (2019) · doi:10.53846/goediss-7388 · Cited by 2
Adaptive Fusion-Based 3D Keypoint Detection for RGB Point Clouds
Iqbal, M. Z., Bobkov, D., & Steinbach, E. G. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803680 · Cited by 2
Hajigholam, M.-H., Raie, A. A., & Karim Faez, A. (2020) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-020-00351-3 · Cited by 2
Image analysis for artistic style identification
Doulamis, A. D., & Varvarigou, T. A. (2012) · CRC Press eBooks · doi:10.1201/b11837-14 · Cited by 2
Multi-View Image Registration for Wide-Baseline Visual Sensor Networks
Caner, G., Tekalp, A. M., Sharma, G., et al. (2006) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2006.313170 · Cited by 2
Wide-baseline matching based on line intersection features
Zhai, Y., He, P. F., Han, D.-S., et al. (2018) · International Conference on Digital Image Processing · doi:10.1117/12.2502982 · Cited by 2
A Novel Mosaic Quality Measurement Method for UAV Surveillance and Remote Sensing
Buyukyazi, T., Bayraktar, S., & Lazoğlu, İ. (2013) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-7-w2-55-2013 · Cited by 2
Structure-Aware Correspondence Learning for Relative Pose Estimation
Chen, Y., Yang, W., Ren, H., Zhang, S., Zhang, T., & Wu, F. (2025) · 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/cvpr52734.2025.01084 · Cited by 2
“Current methods can be broadly categorized into three types: 2D correspondence-based methods [25, 33, 36], hypothesis-and-verification-based methods [19, 28, 48, 49], and 3D correspondence-based methods [49, 50].”
Sun, A., Bhowmick, S. S., & Liu, Y. (2010) · Proceedings of the VLDB Endowment · doi:10.14778/1920841.1921051 · Cited by 2
Finding Regions of Interest Based on Scale-Space Keypoint Detection
Zeng, M., Yang, T., Li, Y., et al. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22456-0_61 · Cited by 2
StructGCN: A structure-aware graph convolutional network for spacecraft pose estimation
Yongqi, M. U., Zhi, X., Gong, J., JIANG, S., Jianming, H. U., Xinkai, W. A. N. G., & Bao, G. (2026) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2026.104204 · Cited by 3
PnLCalib: Sports field registration via points and lines optimization
Gutiérrez-Pérez, M., & Agudo, A. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104712 · Cited by 2
“Traditional sports field registration relied on feature-based methods [37], such as detecting and matching local features like SIFT [27] and MSER [31] to estimate pairwise correspondences and compute a homography matrix by using RANSAC [15].”
Image resizing with SIFT feature preservation
Mishiba, K., & Yoshitome, T. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738205 · Cited by 2
Wang, Y., Du, X., Luo, W., & Hou, Y. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae488b · Cited by 4
“In this study, the widely used SIFT [46] algorithm is employed to detect keypoints in images from the Challenging60 underwater dataset, comparing the original degraded images against those enhanced by DGMLA-Net.”
An Improved Algorithm for Image Mosaic Based on Speeded-Up Robust Features
Lu, X., Jun-ben, W., & He, Z. (2010) · International Conference on Management and Service Science (MASS) · doi:10.1109/icmss.2010.5575648 · Cited by 2
An improved residual learning model and its application to hardware image classification
Zhang, Z., Li, W., Cheng, Y., et al. (2024) · The Visual Computer · doi:10.1007/s00371-024-03340-3 · Cited by 2
Generation of 3D Sparse Feature Models Using Multiple Stereo Views
Watson, M., Bhatti, A., Abdi, H., & Nahavandi, S. (2012) · InTech eBooks · doi:10.5772/45905 · Cited by 2
Image forgery detection for region duplication tampering
Kuo, T.-Y., Lo, Y.-C., & Huang, S.-N. (2013) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2013.6607452 · Cited by 2
A Brief Overview of Methodologies and Applications in Visual Internet of Things
Braović, M., Šerić, L., & Stipaničev, D. (2019) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech.2019.8783042 · Cited by 2
Cross-Resolution Deep Features Based Image Search
Massoli, F. V., Falchi, F., Gennaro, C., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60936-8_27 · Cited by 2
Occlusion-robust multi-object tracking against viewpoint changing for UAV
Zhai, Y., Lu, D., Li, J., & Wang, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132440 · Cited by 2
Image Classification Using Bag of Visual Words and Visual Saliency Model
Jang, H., & Cho, S. (2014) · KIPS Transactions on Software and Data Engineering · doi:10.3745/ktsde.2014.3.12.547 · Cited by 2
Learning to see like children: proof of conceptPreprint
Gori, M., Lippi, M., Maggini, M., & Melacci, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.2478 · Cited by 2
Analysis of Electrocardiograph using Perceptron Feed Forward Neural Network
PrashantShivaji, B., Dipak, B. J., Suryakant, B. A., et al. (2014) · International Journal of Computer Applications · doi:10.5120/15536-4137 · Cited by 2
MAP Estimation of Epipolar Geometry by EM Algorithm and Local Diffusion
Li, W., & Li, B. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379800 · Cited by 2
Biomedical image retrieval using microscopic configuration with local structural information
Deep, G., Kaur, L., & Gupta, S. (2018) · Sadhana · doi:10.1007/s12046-018-0783-4 · Cited by 2
Visual Vocabulary Optimization with Spatial Context for Image Annotation and Classification
Yang, Z., Peng, Y., & Xiao, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-27355-1_11 · Cited by 2
Deep Learning Based Driver Drowsiness Detection in Real Time Environment - An Enhanced Approach
Ajay, A., Ashok, N. K., Balajiram, G., et al. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933001 · Cited by 2
“Two popular algorithms, SIFT and SURF, were applied to the image dataset: SIFT (Scale Invariant Feature Transform) is widely recognized for its ability to detect and describe local features that are invariant to scale and rotation [1].”
Detection of dish waste degree based on image processing and convolutional neural networks
Pu, J. J., Zhu, S., Miao, Y., et al. (2022) · Environmental Progress & Sustainable Energy · doi:10.1002/ep.13942 · Cited by 2
Finger-Knuckle-Print Verification Using Vector Similarity Matching of Keypoints
Kim, M. (2013) · Journal of Korea Multimedia Society · doi:10.9717/kmms.2013.16.9.1057 · Cited by 2
Subpixel keypoint localization and angle prediction for lithography marks based on deep learning
Li, Y., Cao, Y., Li, S., Tian, H., Qi, G., & Wei, R. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02400-8 · Cited by 3
“For keypoint registration methods, three popular algorithms, namely SURF (Bay et al., 2008), SIFT (Lowe, 2004), and ORB (Rublee et al., 2011), were employed to detect keypoints of image patches.”
InfluentialA comparison of geometric and energy-based point cloud semantic segmentation methods
Dubois, M., Rozo, P. K., Gepperth, A., et al. (2013) · European Conference on Mobile Robots · doi:10.1109/ecmr.2013.6698825 · Cited by 2
Joint Panchromatic and Multispectral Geometric Calibration Method for the DS-1 Satellite
Jiang, X., Zhang, X., Liu, M., et al. (2024) · Remote Sensing · doi:10.3390/rs16020433 · Cited by 2
“The corresponding and control points during the calibration are automatically acquired with a scale-invariant feature transform (SIFT) [12] and least-squares matching [13].”
Underwater engineering image enhancement via turbidity prior and multi-scale adaptive
Kong, H., Yuan, A., Chen, Q., Liu, J., & Qiu, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113685 · Cited by 4
A practical classifier for photographs and non-photographic images based on local visual features
Terayama, K., & Hioki, H. (2015) · doi:10.1109/mva.2015.7153192 · Cited by 2
Wang, S., Yao, J., & Summers, R. M. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4760992 · Cited by 2
Han, H., Wang, Z. H., Ding, P., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3568378 · Cited by 2
Marker Registration Technique for Handwritten Text Marker in Augmented Reality Applications
Thanaborvornwiwat, N., & Patanukhom, K. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1004/1/012005 · Cited by 2
Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration
Tourani, S., Reddy, J., Thakur, S., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610738 · Cited by 2
“All of these methods estimate correspondences using a weighted version of Lowe’s ratio test [28] for correspondence estimation and the Kabsch algorithm [29] for relative pose estimation between point cloud pairs.”
Speed Matters, a robust infrared and visible image matching method at real-time speed
Chang, R., Yang, C., Zhang, H., et al. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01395-4 · Cited by 2
Feature recognition and fingerprint sensing for guiding a wood patching robot
Pahlberg, T., & Hagman, O. (2012) · Diva portal (Dalarna University Library) · Cited by 2
Color correction and geometric calibration for multi-view images with feature correspondence
Shao, F., Jiang, G., & Yu, M. (2009) · Optoelectronics Letters · doi:10.1007/s11801-009-8002-5 · Cited by 2
An Effective Iris Recognition Method Based on Scale Invariant Feature Transformation
Huo, G., Liu, Y., Zhu, X., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12484-1_34 · Cited by 2
Liao, X., Wang, W., Wang, W., et al. (2021) · Mobile Information Systems · doi:10.1155/2021/6987170 · Cited by 2
Registration of OCT Fundus Images with Color Fundus Images Based on Invariant Features
Li, P., Chen, Q., Fan, W., & Yuan, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68542-7_40 · Cited by 2
Matching points in poor edge information images
Posse, A., Torres, J., & Menéndez, J. M. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414221 · Cited by 2
Axis-Level Symmetry Detection with Group-Equivariant Representation
유원균, Seo, A., & Cho, M. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02299 · Cited by 2
“SIFT-based techniques [36, 37] normalized orientation for rotation detection, while GVF [40] and polar domain representations [1] improved boundary detection.”
InfluentialAn Attention‐Based Detection Method of Fatigue Cracks on Steel
Yu, Q.-Q., Wang, J., Gu, X.-L., He, S., & Zhang, S. (2025) · Structural Control and Health Monitoring · doi:10.1155/stc/7487687 · Cited by 2
The Analysis of Similarity Measure Function in Image Matching Algorithms
Liu, H. L., Song, W. G., Na, P. Y., et al. (2013) · Advanced materials research · doi:10.4028/www.scientific.net/amr.842.649 · Cited by 2
GPS/Optical/Inertial Integration for 3D Navigation Using Multi-Copter Platforms
Dill, E. T., Young, S. D., & de Haag, M. U. (2017) · Proceedings of the ION ... Pacific PNT Meeting/Proceedings of the Institute of Navigation Pacific PNT · doi:10.33012/2017.15023 · Cited by 2
Improved Kernel Descriptors for Effective and Efficient Image Classification
Karmakar, P., Teng, S. W., Zhang, D., et al. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227446 · Cited by 2
Barbuti, N., & Caldarola, T. (2025) · Digital Library Perspectives · doi:10.1108/dlp-05-2025-0063 · Cited by 2
Registration of multi-spectral satellite images with scale-restricted SURF
Teke, M., & Temizel, A. (2010) · IEEE Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2010.5650776 · Cited by 2
Comparison and analysis of transformation models on UAV image mosaic
Wang, Y., Shao, P., & Chen, Y. (2013) · doi:10.1109/mec.2013.6885216 · Cited by 2
An efficient depth estimation using temporal 3D-Warping
Kumar, S. H., Suraj, K., & Ramakrishnan, K. R. (2014) · International Conference on D Imaging · doi:10.1109/ic3d.2014.7032586 · Cited by 2
CamPU: A Multi-Camera Processing Unit for Deep Learning-based 3D Spatial Computing Systems
Im, D., & Yoo, H. (2024) · Proceedings of the ... annual International Symposium on Microarchitecture/Proceedings of the annual International Symposium on Microarchitecture · doi:10.1109/micro61859.2024.00014 · Cited by 2
“Previous works [1], [23], [45] have focused on accelerating feature extraction [6], [32] and feature matching in unknown camera environments.”
Combining SIFT and Global Features for Web Image Classification
Cheng, Q., Wen, Y., Zha, Z.-J., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34778-8_69 · Cited by 2
Self-Supervised Underwater Caustics Removal and Descattering via Deep Monocular SLAM
Sauder, J. F., & Tuia, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72907-2_13 · Cited by 2
Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval
Bilquees, S., Dawood, H., Dawood, H., et al. (2021) · International Journal of Optics · doi:10.1155/2021/4151482 · Cited by 2
Feature Learning for Facial Kinship Verification
Yan, H., & Lu, J. (2017) · SpringerBriefs in computer science · doi:10.1007/978-981-10-4484-7_2 · Cited by 2
Mesmakhosroshahi, M., & Kim, J. (2012) · Visual Communications and Image Processing · doi:10.1109/vcip.2012.6410811 · Cited by 2
A Nearest Neighbor Classifier-Based Automated On-Line Novel Visual Percept Detection Method
Wang, X., Wang, X., Wang, X., et al. (2020) · Springer eBooks · doi:10.1007/978-981-15-9519-6_9 · Cited by 2
Human ear recognition based on SIFT feature
Lei, S., Yao, H., Min, Q. I., et al. (2009) · Journal of Computer Applications · doi:10.3724/sp.j.1087.2009.01690 · Cited by 2
Searching for the Visual Components of Object Perception
Leeds, D. (2018) · KiltHub Repository · doi:10.1184/r1/6721427 · Cited by 2
Payload derived position and attitude acquisition system for parachute recovery systems
Yakimenko, O. A., Tiaden, R. D., & Berlind, R. M. (2008) · 2006 14th Mediterranean Conference on Control and Automation · doi:10.1109/med.2008.4601976 · Cited by 2
Elaksher, A. F., & Alharthy, A. (2008) · JES. Journal of Engineering Sciences/JES. Journal of engineering sciences · doi:10.21608/jesaun.2008.115585 · Cited by 2
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
