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 40,101–40,200 of 56,201
Multi-view 2D–3D alignment with hybrid bundle adjustment for visual metrology
Yu, Y., Niu, C., Jun, L., et al. (2021) · The Visual Computer · doi:10.1007/s00371-021-02082-w · Cited by 2
An Empirical Analysis of Deep Feature Learning for RGB-D Object Recognition
Çağlayan, A., & Can, A. B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59876-5_35 · Cited by 2
Baldi, J. S. (2015) · theses.fr (ABES) · doi:10.70675/6f81abc4z7e8ez4fa8za280z72c0282bf465 · Cited by 2
Kinship verification via correlation calculation-based multi-task learning
Qin, X., Liu, D., & Gui, B. (2025) · PLoS ONE · doi:10.1371/journal.pone.0329574 · Cited by 2
Efficient Video Indexing for Fast-Motion Video
Park, M., & Park, R. (2014) · International Journal of Computer Graphics & Animation · doi:10.5121/ijcga.2014.4204 · Cited by 2
AN ADVANCED BENCHMARKING FOR IMAGE COMPOSITING EVALUATION
Giudici, R., Courtrai, L., & Lefèvre, S. (2019) · 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/isprs-archives-xlii-2-w13-303-2019 · Cited by 2
Lin, L., Lin, C., Nie, L., et al. (2025) · The Visual Computer · doi:10.1007/s00371-025-03889-7 · Cited by 2
Unsupervised joint face alignment with gradient correlation coefficient
Ni, W., Vu, N., & Caplier, A. (2015) · Pattern Analysis and Applications · doi:10.1007/s10044-015-0474-2 · Cited by 2
Setting of the Operating Conditions of Stereo CCTV Cameras by Weather Condition
Moon, K. I., Pyeon, M. W., Lee, S. B., et al. (2014) · Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography · doi:10.7848/ksgpc.2014.32.6.591 · Cited by 2
Chater, A., Benradi, H., & Lasfar, A. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i4.pp1644-1653 · Cited by 2
Robot‐Assisted Automated Serial‐Sectioning and Imaging for 3D Microstructural Investigations
Moschetti, M., Lemiasheuski, A., Bajer, E., et al. (2025) · Advanced Engineering Materials · doi:10.1002/adem.202502202 · Cited by 2
Image Alignment in Unseen Domains via Domain Deep GeneralizationPreprint
Truong, T.-D., Luu, K., Duong, C. N., Le, N., & Tran, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12028 · Cited by 2
Enclosure Transform for Interest Point Detection From Speckle Imagery
Yu, Y., & Wang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2636281 · Cited by 2
Content Vs Context: How About "Walking Hand-In-Hand" For Image Clustering?
Hu, S., Hou, Z., Lou, Z., et al. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053806 · Cited by 2
High Performance Online Image Search with GPUs on Large Image Databases
Cevahir, A., & Torii, J. (2013) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/jmdem.2013070102 · Cited by 2
Fan, J., Lu, X., & Cao, W. (2022) · Applied Physics Letters · doi:10.1063/5.0079965 · Cited by 2
Maritime Scene Matching for Inter-Robot Localization Across Surface and Underwater Domains
McConnell, J. R., Collado-Gonzalez, I., Szenher, P., & Shariati, A. (2024) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2025.105166 · Cited by 2
Learning to Analyze what is Beyond the Visible Spectrum
Berg, A. (2019) · Linköping studies in science and technology. Dissertations · doi:10.3384/diss.diva-161077 · Cited by 2
Object Class Recognition Using SNoW with a Part Vocabulary
Wen, M., Wang, L., Wang, L., et al. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72530-5_63 · Cited by 2
Mihelic, S. A., Sikora, W. A., Hassan, A. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.15.151076 · Cited by 2
Combining color histogram and ORB features for robust visual tracking
Wang, L., & Qiu, Z. (2012) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2012.6234742 · Cited by 2
Cylindrical Medicine Bottle's Character Recognition of Automotive Dispensing Machine
Gong, M., Zhang, X., & Ni, T. (2016) · doi:10.2991/aiie-16.2016.53 · Cited by 2
Automatic Areas of Interest Detector for Mobile Eye Trackers
Ryabinin, K. V., Alexeeva, S., & Petrova, T. (2022) · International Conference on Computer Graphics and Vision (GraphiCon) · doi:10.20948/graphicon-2022-228-239 · Cited by 2
Deep Hashing with Triplet Labels and Unification Binary Code Selection for Fast Image Retrieval
Zhou, C. S., Po, L.-M., Liu, M., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05710-7_23 · Cited by 2
Combining two point clouds generated from depth camera
Li, X., Guo, W. P., Li, M., et al. (2013) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2013.6739868 · Cited by 2
Person Search via Deep Integrated Networks
Chen, J.-C., Wu, C.-F., Chen, C.-H., et al. (2019) · Applied Sciences · doi:10.3390/app10010188 · Cited by 2
Query filtering using two-dimensional local embeddings
Vadicamo, L., Connor, R., & Chávez, E. (2021) · Information Systems · doi:10.1016/j.is.2021.101808 · Cited by 2
Towards Robust Localization Deep Feature Extraction by CNN
Carlbaum, E., Mansouri, S. S., Kanellakis, C., et al. (2020) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon43393.2020.9254941 · Cited by 2
Vehicle Detection in High-resolution SAR Image Using Saliency Visual Attention Method
Pei, H., Li, H., Huang, H., et al. (2022) · China International SAR Symposium (CISS) · doi:10.1109/ciss57580.2022.9971184 · Cited by 2
Color and Texture Based Image Matching Algorithm
He, X., & Ping, Z. (2021) · International Conference on Big Data Engineering and Education (BDEE) · doi:10.1109/bdee52938.2021.00020 · Cited by 2
Towards monocular localization using ground points
Bonin‐Font, F., Burguera, A., Ortiz, A., et al. (2010) · IEEE Conference on Emerging Technologies & Factory Automation (ETFA) · doi:10.1109/etfa.2010.5641279 · Cited by 2
Improved YOLOv5 Network Method for Remote Sensing Image Based Ground Objects RecognitionPreprint
Xue, J., Shandong University of Science and Technology, Zheng, Y., Dongye, C., Wang, P., & Yasir, M. (2022) · Research Square · doi:10.21203/rs.3.rs-1224458/v1 · Cited by 2
Towards real-time object detection on edge with deep neural networks
Zhang, Z. (2018) · doi:10.32469/10355/67715 · Cited by 2
Optic flow based on multi-scale anchor point movement and discontinuity-preserving regularization
van Dorst, P. A. G., Janssen, B. J., Florack, L. M. J., & Romeny, B. M. T. H. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.003 · Cited by 2
Supervised feature type selection for topological mapping in indoor environments
Özkucur, N. E., & Akın, H. L. (2013) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2013.6531556 · Cited by 2
Methods for Combined Monocular and Stereo Mobile Robot Localization
Fraundorfer, F., Wu, C., & Pollefeys, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17711-8_19 · Cited by 2
Natural Landmark Tracking Method to Support UAV Navigation over Rain Forest Areas
Pinage, F. A., Carvalho, J. R. H., & Neto, J. P. D. Q. (2012) · doi:10.1109/sbesc.2012.28 · Cited by 2
Supporting Diagnostics of Coronary Artery Disease with Neural Networks
Kukar, M., & Grošelj, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20282-7_9 · Cited by 2
Scalable learning for restricted Boltzmann machines
Barshan, E., & Fieguth, P. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025557 · Cited by 2
Williams, A. B., Touretzky, D. S., Tira-Thompson, E. J., et al. (2008) · ACM SIGCSE Bulletin · doi:10.1145/1352322.1352303 · Cited by 2
On filtering by means of generalized integral images: a review and applications
Haselhoff, A., & Kummert, A. (2010) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-010-0112-5 · Cited by 2
Data-driven image captioning with meta-class based retrieval
Kilickaya, M., Erdem, E., Erdem, A., İkizler-Cinbiş, N., & Çakıcı, R. (2014) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2014.6830631 · Cited by 3
Research of image detection and matching algorithms
Bai, Y.-F. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/5/20230634 · Cited by 2
Ke, T., Ye, Z., Zhang, X., et al. (2024) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-1-2024-107-2024 · Cited by 2
A Multi-Task Model for Multi-Attribute Fashion Recognition and Retrieval
Sun, Y., Wong, W. K., & Zou, X. (2021) · AATCC Journal of Research · doi:10.14504/ajr.8.s1.14 · Cited by 2
Multilayer matching of metric structures using hierarchically well-separated trees
Osmanlıoğlu, Y., & Shokoufandeh, A. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.05.030 · Cited by 2
Individual Identification Using Ear Region Based on SIFT
Kim, M. (2015) · Journal of Korea Multimedia Society · doi:10.9717/kmms.2015.18.1.001 · Cited by 2
Yang, K., & Du, E. Y. (2011) · doi:10.1109/cibim.2011.5949207 · Cited by 2
Automated phenotyping of mouse social behavior
Edelman, N. (2011) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2
A new robust fuzzy fusion technique in super resolution imaging
Hosseini, B. K., & Yaghmaee, F. (2014) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2014.6999682 · Cited by 2
Visual Feedback for Nonholonomic Mobile Robots
Usai, A., & Giamberardino, P. D. (2010) · Advances in civil and industrial engineering book series · doi:10.4018/978-1-61520-849-4.ch006 · Cited by 2
Towards 3D pipe reconstruction employing affine transformations from video information
Reyes-Acosta, A. V., López-Juárez, I., Osorio-Comparán, R., et al. (2016) · doi:10.1109/ica-acca.2016.7778430 · Cited by 2
Visional grasping control of robot based on QR code
Juang, L. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18279-9 · Cited by 2
Top-Down Saliency by Multi-scale Contextual Pooling
Qiu, Y., Zhu, J., Zhang, R., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34778-8_27 · Cited by 2
An end to end trained hybrid CNN model for multi-object tracking
Singh, D., & Srivastava, R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11463-1 · Cited by 2
Research status of damage identification algorithm based on deep learning
Zhu, D., Song, L., Yuan, F., et al. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202123304039 · Cited by 2
Object Detection, Segmentation and Categorization in Artificial Intelligence
Li, H., Xie, F., Zhou, J., & Liu, J. (2024) · Electronics · doi:10.3390/electronics13132650 · Cited by 2
Simultaneous Context Feature Learning and Hashing for Large Scale Loop Closure Detection
Fu, Z., Guo, Y., & An, W. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545135 · Cited by 2
Yan, Y., Tang, L., Huang, H., Yu, Q., Xu, H., Chen, Y., Chen, M., & Zhang, Q. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106698 · Cited by 2
MREAK: Morphological Retina Keypoint Descriptor
Vaghela, H., Oza, M., & Bagul, S. (2019) · doi:10.1109/icaiit.2019.8834665 · Cited by 2
Automatic panoramic stitching of omni-directional remote sensing images
Li, X., & Yuan, Z. (2013) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2013.6744017 · Cited by 2
Characters Verification via Siamese Convolutional Neural Network
Wang, S., Lv, X., Li, R., et al. (2018) · International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · doi:10.1109/spac46244.2018.8965605 · Cited by 2
Optical flow based guidance system design for semi-strapdown image homing guided missiles
Huang, L., Song, J., Zhang, M., et al. (2016) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2016.08.011 · Cited by 2
Liu, Z., Zhu, H., Chen, J., et al. (2019) · doi:10.1145/3356250.3360029 · Cited by 2
Feature selection for low bit rate mobile augmented reality applications
Cao, Y., Ritz, C. H., & Raad, R. (2015) · Signal Processing Image Communication · doi:10.1016/j.image.2015.06.008 · Cited by 2
OoD-Pose: Camera Pose Regression From Out-of-Distribution Synthetic Views
Ng, T., Lopez-Rodriguez, A., Balntas, V., & Mikolajczyk, K. (2022) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv57658.2022.00082 · Cited by 2
Liang, F., Xu, Y., Zhang, M., et al. (2016) · Symmetry · doi:10.3390/sym8100102 · Cited by 2
An Image Based Approach for Content Analysis in Document Collections
Huber-Mörk, R., & Schindler, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41939-3_27 · Cited by 2
A Rotation and Scale Invariant Map Matching Method for UAV Visual Geolocalization
Liu, Y., Bai, J., Xiao, Z. Q., Lian, Y., & Jiao, L. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3639900 · Cited by 3
Structure-from-Motion photogrammetry: a novel, low-cost tool for geomorphological applications
Westoby, M., Brasington, J., Glasser, N. F., et al. (2012) · EGU General Assembly Conference Abstracts · Cited by 2
Intensity-based hierarchical elastic registration using approximating splines
Serifovic-Trbalic, A., Demirovic, D., & Cattin, P. (2013) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-013-0906-7 · Cited by 3
Homography-based image mosaicing by using EM algorithm for removing inconsistent overlaid regions
Iiyoshi, T., & Mitsuhashi, W. (2008) · SICE Annual Conference (SICE) · doi:10.1109/sice.2008.4654721 · Cited by 2
Combined GPS/INS/WSS integration and visual SLAM for geo-localization of a mobile robot
Berrabah, S. A., Baudoin, Y., & Sahli, H. (2011) · IEEE International Conference on Mechatronics (ICM) · doi:10.1109/icmech.2011.5971337 · Cited by 2
Change detection and update of 3D sparse map by merging geometry and appearance
Ma, W., Song, Z., He, Y., et al. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103306 · Cited by 2
A pipeline of stereo vision based visual odometer for unmanned aerial vehicle
Lü, N., & Yin, T. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326642 · Cited by 2
A method for absolute pose regression based on cascaded attention modules
Song, X., Tang, J., Yang, K., Guo, W., Lu, X., & Hei, X. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104440 · Cited by 3
Fast Video Deduplication and Localization With Temporal Consistence Re-Ranking
Henry, C., Song, L., & Li, Z. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3420422 · Cited by 3
“Let X = { x t , t = 1 , . . . , T } represent the set of T local descriptors (such as SIFT [29])) extracted from an image.”
Context Forest for efficient object detection with large mixture modelsPreprint
Modolo, D., Vezhnevets, A. S., & Ferrari, V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.00787 · Cited by 4
A SIFT-based approach of recognition of remotely mobile phone captured text images
Mai, B. Q. L., Huynh, T. H., & Doan, A. D. (2015) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc.2015.7388337 · Cited by 2
SURF-based Human Tracking Algorithm with On-line Update of Object Model
Gupta, M., Kejriwal, N., Behera, L., et al. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140313-3-in-3024.00247 · Cited by 2
Automatic Web Page Classification Using Visual Content for Subjective and Functional Variables
Gonçalves, N., & Videira, A. (2015) · Lecture notes in business information processing · doi:10.1007/978-3-319-27030-2_18 · Cited by 2
A Fast Binocular Vision Stereo Matching Algorithm
Zhang, H., Zhang, L., & Ren, Y. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.556-562.3735 · Cited by 2
Clustering of Temporal and Visual Data: Recent Advancements
Mudgal, P. (2026) · Data · doi:10.3390/data11010007 · Cited by 2
“This assumption often holds for clustering visual features extracted from natural images, such as color histograms or SIFT descriptors [75], where the feature distributions tend to be unimodal or multimodal Gaussian-like.”
Scene Flow-Based Environment 3D Digitalization for Mobile Robot Navigation
Li, X., Jia, S., Wang, K., et al. (2012) · Advanced Robotics · doi:10.1080/01691864.2012.685221 · Cited by 2
Omnidirectional multi-view systems: Calibration, features and 3D information
Morales, L. P., & Campo, J. J. G. (2012) · Dialnet (Universidad de la Rioja) · Cited by 2
Binocular Ranging Based on Improved ORB-RANSAC
Sun, M., Hua, C., & Pan, R. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9902471 · Cited by 2
PoseGAN: A Pose-to-Image Translation Framework for Camera LocalizationPreprint
Liu, K., Li, Q., & Qiu, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12712 · Cited by 2
Ceniceros, J. C. E., Schaeffer, S. E., & Garza, S. E. (2014) · Mexican International Conference on Artificial Intelligence · doi:10.1109/micai.2014.13 · Cited by 2
Missing pins detection for power equipment firmware using unmanned aerial vehicle images
Huai, B., Wang, R., Liu, Y., et al. (2019) · doi:10.1117/12.2506338 · Cited by 2
Hurricane Damage Prediction on Satellite Imagery based on Neural Networks
Hu, D., Lei, Z., & Wan, S. (2021) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1109/icaice54393.2021.00082 · Cited by 2
Adaptive Multilevel Kernel Machine for Scene Classification
Hu, J., Wang, L., Duan, F., & Guo, P. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/324945 · Cited by 2
A correlation-based bag of visual words for image classification
Jiang, J., Wu, D., & Jiang, Z. (2017) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec.2017.8122482 · Cited by 2
Wu, L., Wang, H., & Wang, H. (2024) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-024-01933-y · Cited by 2
Efficient line matching with homography
Yan, S., Dai, Y., & Zhu, Z. (2017) · Measurement Science and Technology · doi:10.1088/1361-6501/aaa210 · Cited by 2
Finger Knuckle Print Authentication Using AES and K-Means Algorithm
Arunachalam, M., & Kannan, S. (2015) · The International Arab Journal of Information Technology · Cited by 2
Yang, N., Yang, Y., Li, P., et al. (2021) · International Workshop on Pattern Recognition (IWPR) · doi:10.1117/12.2605000 · Cited by 2
Joint Image Restoration and Matching Based on Hierarchical Sparse Representation
Li, W., Sang, N., Gao, C., et al. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803466 · Cited by 2
Image Uncertainty-Based Absolute Camera Pose Estimation with Fibonacci Outlier Elimination
Pitchandi, N., & Subramanian, S. P. (2019) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-019-00985-4 · Cited by 2
A new affine-invariant image matching method based on SIFT
Wang, P., Chen, Q., Chen, H., et al. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2033014 · Cited by 2
Vision-Based Measurement:The Application and Research in Gantry Cranes at Bulk Cargo Terminals
Chen, C., Zhang, Y., & Mi, C. (2024) · doi:10.1109/issi63632.2024.10720466 · Cited by 2
“Traditional 2D target detection algorithms primarily rely on manually designed features, such as Scale-Invariant Feature Transform (SIFT) [2], Haar features [3], Histogram of Oriented Gradients (HOG) [4], and Local Binary Patterns (LBP) [5].”
Background
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
