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 45,101–45,200 of 56,200
An Integrated Methods for Refined Facial-Image Retrieval using Sift and Click-Through Data
Balasubramanian, C., & Sekar, J. R. (2019) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.a1011.1291s619 · Cited by 1
Corner Detection in Images Under Different Noise Levels
Song, Y., Shen, Y., Li, S., Zhu, C., Zhang, J., & Chang, H. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.166 · Cited by 1
A Two-Step Global Alignment Method for Feature-Based Image Mosaicing
Elibol, A. (2016) · Mathematical and Computational Applications · doi:10.3390/mca21030030 · Cited by 1
Kim, T.-H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.08339 · Cited by 1
A face-based authentication system using correlation filters on videos
de Oliveira, J. F. L., da Silva, E. A. B., Cardoso, M. A. P., et al. (2009) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2009.5293276 · Cited by 1
Efficient and robust feature model for visual tracking
Wang, L., Zhuo, Q., & Wang, W. (2009) · Tsinghua Science & Technology · doi:10.1016/s1007-0214(09)70023-9 · Cited by 1
Research on intelligent auxiliary assembly technology based on deep learning
Wang, Y., Wei, W., & Baitao, T. (2024) · Cobot · doi:10.12688/cobot.17668.2 · Cited by 1
MRNet: A Deep Learning Framework for Drivable Area Detection in Multi-Scenario Unstructured Roads
Yang, J. K., Chen, J., Wang, Y., Sun, S., Xie, H., Wu, J., & Wang, W. (2025) · Electronics · doi:10.3390/electronics14112242 · Cited by 2
Prystavka, P., & Cholyshkina, O. (2020) · Advanced Information Systems · doi:10.20998/2522-9052.2020.4.10 · Cited by 1
AUTOMATED PHOTOGRAMMETRIC IMAGE MATCHING WITH SIFT ALGORITHM AND DELAUNAY TRIANGULATION
Karagiannis, G., Castro, F. A., & Mioc, D. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-iii-2-23-2016 · Cited by 1
Peng, Z., Song, R., Yang, H., Li, Y., Lin, J., Xiao, Z., & Yan, B. (2024) · Applied Sciences · doi:10.3390/app14146276 · Cited by 1
“However, there are some well-established methods for extracting local features, such as SIFT [16] and ORB [17].”
MethodAffine invariant salient patch descriptors for image retrieval
Isikdogan, F., & Salah, A. A. (2013) · International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS) · doi:10.1109/wiamis.2013.6616136 · Cited by 1
Cross-Day Grape Cluster Tracking Using Branch-Based 3D Alignment in Vineyards
Yoshida, T., Teng, P., Ota, T., et al. (2026) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2026.p0953 · Cited by 1
Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites
Li, L., Cao, G., Liu, J., & Cai, X. (2023) · Sensors · doi:10.3390/s23239479 · Cited by 1
Wang, C., Wu, Y., Wang, Y., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3111168 · Cited by 1
Anomaly Detection on Video by Detecting and Tracking Feature Points
Fomin, I., Rezets, Y., & Smirnova, E. (2023) · doi:10.3390/engproc2023033034 · Cited by 1
GPU Accelerated Cascade Hashing Image Matching for Large Scale 3D ReconstructionPreprint
Xu, T., Sun, K., & Tao, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08995 · Cited by 1
Detection of dynamic objects for environment mapping by time-of-flight cameras
Dalbah, Y., Rohr, S., & Wahl, F. M. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025195 · Cited by 1
Saliency weight and local quadrant constraint for visual search
Zhao, H., Nong, Z., Liu, P., et al. (2015) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2015.7407942 · Cited by 1
Kresović, M., Theoharis, T., & Hardeberg, J. Y. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104696 · Cited by 1
Recovering Projective Transformations between Binary Shapes
Németh, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33140-4_33 · Cited by 1
Map Slammer: Densifying Scattered KSLAM 3D Maps with Estimated Depth
Torres-Camara, J. M., Escalona, F., Gomez‐Donoso, F., et al. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-36150-1_46 · Cited by 1
From Emotion Recognition to Website Customizations
Oliveira Gina, M. B. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070772 · Cited by 1
Synthetic vision‐based perceptual attention for augmented reality agents
Oh, S., Baek, W., & Woo, W. (2010) · Computer Animation and Virtual Worlds · doi:10.1002/cav.368 · Cited by 1
Ni, K., Wu, Y., & Wang, P. (2019) · Remote Sensing · doi:10.3390/rs11091079 · Cited by 1
Structure-Preserved Face Cartoonization
Gao, C., Sheng, B., & Shen, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70090-8_63 · Cited by 1
A Novel Spatial Layout Representation for Object Recognition
Elfiky, N. M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-44289-7_52 · Cited by 1
SUGrasping: a semantic grasping framework based on multi-head 3D U-Net
Cao, H., Zhang, Y., Ge, Z., et al. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20037-w · Cited by 1
A No-Reference Image Quality Assessment For Detecting Illumination Alteration
Tafran, C., El-Abed, M., Elkabani, I., et al. (2019) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/iacs.2019.8809123 · Cited by 1
GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY
Ivanchuk, О. М. (2015) · GEODESY CARTOGRAPHY AND AERIAL PHOTOGRAPHY · doi:10.23939/istcgcap2015.01.112 · Cited by 1
Feature-Less Stitching of Cylindrical Cable for Surface Inspection of Cable-Stayed Bridges
Zhang, K., Zhou, L., & Xiao, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60639-8_18 · Cited by 1
Feng, X., Wang, Y., & Liu, B. (2016) · International Conference on Signal Processing · doi:10.1109/icsp.2016.7877995 · Cited by 1
Light-field features for robotic vision in the presence of refractive objects
Tsai, D. (2020) · Queensland University of Technology · doi:10.5204/thesis.eprints.192102 · Cited by 1
Triple-Camera Rectification for Depth Estimation Sensor
Jeon, M., Park, J., Kim, J.-W., et al. (2024) · Sensors · doi:10.3390/s24186100 · Cited by 1
“…area except the approximate chart area considering the margin is Various feature detection methods such as Harris corner detection [22], SIFT [23], SURF [24], ORB [25], FAST [26], BRIEF [27], HOG [28], and MSER [29] often show poor performance near the out-of-focus area (in our case, it…”
MethodVenkatesan, R., Chandakkar, P. S., & Li, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01264 · Cited by 1
Oxford-IIIT TRECVID 2010 - Notebook paper.
Juneja, M., Chandra, S., Parkhi, O., et al. (2010) · Cited by 1
Loop Closure using AnyLoc Visual Place Recognition in DPV-SLAM
Zhang, W., Adachi, K., Hara, Y., et al. (2026) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii64115.2026.11404452 · Cited by 1
Adaptively Designed Shape Regression Model for Facial Point Detection
Takahashi, K., & Mitsukura, Y. (2015) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.69.j126 · Cited by 1
Fast image matching using multi-level texture descriptor
Ng, H., Lin, C., & Muindisi, T. (2014) · doi:10.1109/apsipa.2014.7041672 · Cited by 1
UMHE: Unsupervised Multispectral Homography Estimation
Shin, J., Kim, J., Kwon, S. M., et al. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3383453 · Cited by 1
“Local feature-based methods [1], [10], [14], [25] can be used for estimating a homography from the keypoints and descriptors followed by RANSAC [9].”
MethodLocal Features for Range and Vision-Based Robotic Automation
Vikstén, F. (2010) · Cited by 1
Osaka Prefecture University at TRECVID 2011
Sakata, T., Matozaki, N., Kise, K., et al. (2011) · TRECVID · Cited by 1
Daudt, R. C. (2020) · theses.fr (ABES) · doi:10.70675/cf613c02zcc46z484az9776z2d118b4d58a9 · Cited by 3
Pratama, Y., Virgono, A., & Dirgantara, F. M. (2022) · MULTINETICS · doi:10.32722/multinetics.v7i2.4222 · Cited by 1
Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation MaximizationPreprint
Karimian, A., Yang, Z., & Tron, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02638 · Cited by 1
Cross-modality matching based on Fisher Vector with neural word embeddings and deep image features
Han, L., Wang, W., Fan, M., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952691 · Cited by 1
Random projections for feature matching
McCane, B. (2014) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1145/2683405.2683432 · Cited by 1
Optimal Difference Operator Selection
Veelaert, P., & Teelen, K. (2008) · Springer eBooks · doi:10.1007/978-3-540-79126-3_44 · Cited by 1
Co-Evolutionary Dual-Tree Genetic Programming for Fine-Grained Flower Image Classification
Wang, Q., Bi, Y., Xue, B., & Zhang, M. (2025) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2025.3590738 · Cited by 2
“…functions aim to extract features from these regions or the whole image, including domain-independent features ( DI F ) [33], histograms ( Hist ) [34], SIFT features ( SI FT ) [26], HOG features ( H OG ) [35], and uniform LBP histograms ( uL B P ) [36], as well as the color distribution ( Color ) .”
Vanishing Point Guided Natural Image StitchingPreprint
Chen, K., Yao, J., Tu, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.02478 · Cited by 1
Liu, Y., & Liu, J. (2025) · Soft Computing · doi:10.1007/s00500-025-10500-7 · Cited by 1
Squeezing the DCT to Fight Camouflage
Escudero-Viñolo, M., & Bescós, J. (2019) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-019-00930-y · Cited by 1
Hardware implementation of an optimized scale-invariant feature detector for robotic applications
Vourvoulakis, J. V., Lygouras, J. N., & Kalomiros, J. A. (2014) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2014.6958478 · Cited by 1
Local Dissimilarity Measures of Frames in Visual Substitution System for Blind People
Jabnoun, H., Morain-Nicolier, F., Bensarti, F., & Amiri, H. (2016) · International Conference on Computer Graphics, Imaging and Visualization (CGIV) · doi:10.1109/cgiv.2016.53 · Cited by 1
In vivo imaging of central nervous system regeneration using Danionella cerebrum
Misyuk, M. K., Austin, J. L., Walechka, N. R., Peterka, J. A., Veldman, M. B., & Lam, P. (2026) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-25-00948 · Cited by 2
Fast Keypoint Reduction for Image Retrieval by Accelerated Diverse Density Computation
Wada, T., & Mukai, Y. (2015) · doi:10.1109/icdmw.2015.156 · Cited by 1
An Image Mosaic Method Based on SIFT Feature Matching
Cao, L. J., & Lv, M. Y. (2012) · Advanced materials research · doi:10.4028/www.scientific.net/amr.433-440.5420 · Cited by 1
A Novel Method for Movie Character Identification and its Facial Expression Recognition
Purna, M. D., & Praveen, N. (2014) · Cited by 1
StylizedFacePoint: Facial Landmark Detection for Stylized Characters
Cheng, S., Ma, C., & Pan, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680984 · Cited by 1
A dynamically selecting model to track a moving object
Zhu, A., Chen, Y., & Yi, X. H. (2013) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2013.6739717 · Cited by 1
Recognition of object categories using affine kernels
Syeda-Mahmood, T. F., Wang, F., & Beymer, D. (2010) · doi:10.1145/1743384.1743396 · Cited by 1
A Methodology for Multi-Camera Surface-Shape Estimation of Deformable Unknown Objects
Nuger, E., & Benhabib, B. (2018) · Robotics · doi:10.3390/robotics7040069 · Cited by 2
Approximate Similarity Search for Time Series Data Enhanced by Section Min-Hash
Tomoda, R., & Koga, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46994-7_2 · Cited by 1
Generic object recognition with local features: From bags to subspaces
Raytchev, B., Kikutsugi, Y., Shigenaka, R., Tamaki, T., & Kaneda, K. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706938 · Cited by 1
A Far Road Sign Recognition by Applying Super-Resolution to Extracted Regions from Successive Frames
Yamauchi, H., Kojima, A., Miyamoto, T., et al. (2007) · doi:10.1109/icmech.2007.4279978 · Cited by 1
Least Squares Consensus for Matching Local Features
Zhang, Q., Shi, B., & Xu, H. (2019) · Information · doi:10.3390/info10090275 · Cited by 1
Impact of Downscaling on Adversarial Images
Bonnet, B., Furon, T., & Bas, P. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897433 · Cited by 1
Image Representation Learning by Deep Appearance and Spatial Coding
Liu, B., Liu, J., Li, Z., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16865-4_43 · Cited by 1
Corner-Based 3D Object Pose Estimation in Robot Vision
Zhang, L., Guo, Z., Chen, H., et al. (2016) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2016.200 · Cited by 1
Person re-identification algorithm based on multi-module convolutional neural network
Lei, H., Jiao, Z., Lin, J., et al. (2021) · IEEE International Conference on Emergency Science and Information Technology (ICESIT) · doi:10.1109/icesit53460.2021.9696542 · Cited by 1
Visual Tracking Using Multimodal Particle Filter
Tung, T., & Matsuyama, T. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5204-8.ch044 · Cited by 1
Unifying vision and control for mobile robots
Becerra, H. M. (2011) · Cited by 1
Aircraft Wing Deformation Measurement Method Based on Three-dimensional Speckle Image
Yang, L., Zhang, W., & Yu, H. (2019) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit48058.2019.8973229 · Cited by 1
Combining Textural and Geometrical Descriptors for Scene Recognition
Bayramoğlu, N., Heikkilä, J., & Pietikäinen, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33868-7_4 · Cited by 1
Tree-stem Volume Calculation from SfM-MVS to Improve Estimates in the Timber Industry in Indonesia
(2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.b1105.0982s1019 · Cited by 1
Point Set Registration for Target Localization Using Unmanned Aerial Vehicles
Darji, D., & Vejarano, G. (2023) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3586575 · Cited by 1
Traffic Scenes Classification for Self-driving Car
Lv, H., Zhuang, X., & Cao, H. (2016) · doi:10.2991/icsmim-15.2016.25 · Cited by 1
AI and machine learning for computer vision applications in open world
Kim, S., & Sohn, K. (2023) · Advances in computers · doi:10.1016/bs.adcom.2023.02.002 · Cited by 1
Fast automatic elimination of vertical parallax of multiview images
Wang, H., Guo-zhong, W., Liliang, L., et al. (2010) · International Conference on Signal Processing · doi:10.1109/icosp.2010.5655852 · Cited by 1
Embedded Vision‐Based Sensing System for Noncontact Cable Vibration Monitoring With IoT Technologies
Zhang, S., Ni, P., Wen, J., et al. (2025) · Structural Control and Health Monitoring · doi:10.1155/stc/6945296 · Cited by 1
Design of video aerial target detection and tracking system based on AM5728
Li, L., Zhao, G., & Xu, S. (2018) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2018.8408277 · Cited by 1
Soper, O., Assir, M. Z. K., Ramírez, D., Crawford, G., Gyuris, R., Valkila, S. S. M., Lecante, L. L., Liu, P., Fowler, P., González, J. A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.03.686207 · Cited by 1
Automatic and Quantitative evaluation of attribute discovery methodsPreprint
Liu, L., Wiliem, A., Chen, S., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.01940 · Cited by 1
Kosykh, V. P., Gromilin, G. I., & Yakovenko, N. S. (2024) · Optoelectronics Instrumentation and Data Processing · doi:10.3103/s8756699024700195 · Cited by 1
“…internal parameters, but unknown relative position and orientation, consists of the following stages: —using one of the detectors (for example, [3–5]), search each of the images for a set of feature points, the vicinity of which has characteristic properties that make it possible to…”
InfluentialWang, S., Li, X., Zhang, Y., et al. (2025) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-05-2024-0235 · Cited by 1
Exploring Video Hyperlinking in Broadcast Media
Eskevich, M., Bui, Q.-M., Le, H.-A., et al. (2015) · doi:10.1145/2802558.2814647 · Cited by 1
Image-Based 3D Shape Generation Used for 3D Printing
Li, Z., Li, Z., Sun, Y., Ren, L., & Laili, Y. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-2853-4_42 · Cited by 1
Encrypted Image Search Based on SGX and Hierarchical Index
Li, K., Wan, J., Xiang, Z., Hu, M., Cao, J., & He, T. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-0855-0_20 · Cited by 1
Choi, J., Larson, M. A., Li, X., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.01335 · Cited by 1
Chen, P., Liu, Z., Han, J., Dai, Y. M., Wang, Z., & Chen, Z. (2026) · Machines · doi:10.3390/machines14020176 · Cited by 2
Stabilizing FOV Under the Microscope: Towards Eye-Hand Coordination in Motor-Control Tasks
Khalil, S., Franco-González, I. T., Fung, E., et al. (2026) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/3797246.3805857 · Cited by 1
“For each frame, SIFT features [Lowe 2004] were extracted and matched to the reference using brute-force L2 matching with Lowe’s ratio filtering, and a 3 × 3 homography was estimated with RANSAC [Fischler and Bolles 1981].”
A New Image Stitching Method with Entropy and Template Matching
Wu, J., Lin, Y., Teng, K., et al. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444502 · Cited by 1
Multi-camera recognition of people operating home medical devices
Gao, Z., Detyniecki, M., Chen, M., et al. (2010) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2010.5639676 · Cited by 1
Multi-Region Indoor Localization Based on WVP System
Zhang, L., Xu, Y., Bao, J., Wang, Q., Xu, J., Li, D., Yang, Y., & Zhang, M. (2021) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads53394.2021.00102 · Cited by 2
Secure and efficient integration of big data for multi‐cells based on micro images
Yin, X. L., & Sun, Y. (2013) · Security and Communication Networks · doi:10.1002/sec.778 · Cited by 1
An improved feature extraction-based colour image matching method using quaternion matrix
Zou, H., Liu, C., Shen, Q., et al. (2016) · International Journal of Communication Networks and Distributed Systems · doi:10.1504/ijcnds.2016.077672 · Cited by 1
Li, L. (2015) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip.2015.7493988 · Cited by 1
MIMIC: a flexible pipeline to register and summarize IMC-MSI experiments
Gerber, R., Griner, J., Guglietta, S., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-09961-9 · Cited by 1
“For three samples, the manual registration was performed three times each to investigate consistency.”
Somebody helps me: Travel video scene detection using web-based context
Chu, W.-T., & Li, C.-J. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.05.042 · 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
