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 31,601–31,700 of 56,201
3D Model Identification Using Weighted Implicit Shape Representation and Panoramic View
Jin, X., & Kim, J. (2017) · Applied Sciences · doi:10.3390/app7080764 · Cited by 4
Boosting k-Nearest Neighbors Classification
Piro, P., Nock, R., Ali, W. B. H., et al. (2013) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-5520-1_12 · Cited by 4
Valenzuela, R. E. G., Schwartz, W. R., & Pedrini, H. (2014) · Journal of Electronic Imaging · doi:10.1117/1.jei.23.3.033017 · Cited by 4
Stereo-vision based object grasping using robotic manipulator
Verma, N. K., Mustafa, A., & Salour, A. (2016) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciinfs.2016.8262914 · Cited by 4
A multi-layer approach for camera-based complex map image retrieval and spotting system
Dang, Q. B., Luqman, M. M., Coustaty, M., et al. (2014) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2014.7001968 · Cited by 4
High performance GPU based optimized feature matching for computer vision applications
Sharma, K. (2015) · Optik · doi:10.1016/j.ijleo.2015.10.206 · Cited by 4
Lightweight feature point detection network with channel enhancement
Li, Z., Zhao, X., Bao, C., et al. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103716 · Cited by 4
SILT: Scale-invariant line transform
Khaleghi, B., Baklouti, M., & Karray, F. O. (2009) · IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) · doi:10.1109/cira.2009.5423244 · Cited by 4
Logo Recognition by Combining Deep Convolutional Models in a Parallel Structure
Karimi, M. R., & Behrad, A. (2019) · doi:10.1109/pria.2019.8786032 · Cited by 5
Building Effective Representations for Sketch Recognition
Guo, J. F., Wang, C., & Chao, H. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9794 · Cited by 4
Thaher, T., Saffarini, M., Mafarja, M. M., Alashbi, A. A., Mohamed, A. H. H. M., & El‐Saleh, A. A. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.065388 · Cited by 5
Inertia Sensor Aided Alignment for Burst Pipeline in Low Light Conditions
Zhang, S., & Stevenson, R. L. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451134 · Cited by 4
Homologous point transformer for multi-modality prostate image registration
Ruchti, A., Neuwirth, A., Lowman, A. K., et al. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1155 · Cited by 4
Solving the correspondence problem in stereo vision by internal simulation
Kaiser, A., Schenck, W., & Möller, R. (2013) · Adaptive Behavior · doi:10.1177/1059712313488425 · Cited by 4
VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field
Xue, F., Budvytis, I., Reino, D. O., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91989-3_6 · Cited by 4
Fusing WiFi and Video Sensing for Accurate Group Detection in Indoor Spaces
Jayarajah, K., Lantra, Z., & Misra, A. (2016) · doi:10.1145/2935651.2935659 · Cited by 4
Tracking Object by Combining Particle Filters and SIFT Features
Feng, B., Zeng, B., & Qiu, J. (2009) · International Conference on Image and Graphics · doi:10.1109/icig.2009.61 · Cited by 4
3DTV View Generation Using Uncalibrated Cameras
Jarusirisawad, S., & Saitô, H. (2008) · 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video · doi:10.1109/3dtv.2008.4547807 · Cited by 4
Learning Generative Models of Object Parts from a Few Positive Examples
Riabchenko, E., Kämäräinen, J., & Chen, K. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.397 · Cited by 4
Content-Based Image Retrieval for Multiple Objects Search
Szücs, G., & Papp, D. (2017) · Cybernetics and Information Technologies · doi:10.1515/cait-2017-0020 · Cited by 4
Learning to perceive coherent objects
Dorfman, N., Harari, D., & Ullman, S. (2013) · eScholarship (California Digital Library) · Cited by 4
A Reconstruction Error Formulation for Semi-Supervised Multi-task and Multi-view LearningPreprint
Qian, B., Wang, X., & Davidson, I. A. K. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1202.0855 · Cited by 4
A sparse coding approach for local-to-global 3D shape description
Boscaini, D., & Castellani, U. (2014) · The Visual Computer · doi:10.1007/s00371-014-0938-1 · Cited by 4
Mitosis detection in biomedical images
Lu, Y., Liu, A.-A., & Su, Y. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-814972-0.00006-0 · Cited by 6
Cheng, C.-M., Lai, S., & Su, S.-H. (2009) · Cited by 4
Compressing a set of CHoG features
Chandrasekhar, V., Tsai, S. S., Reznik, Y. A., et al. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.895431 · Cited by 4
VizCheck: Enhancing Phishing Attack Detection through Visual Domain Name Homograph Analysis
Zouahi, H., Talhi, C., & Boudar, O. (2024) · Conference on Cloud and Internet of Things (CIoT) · doi:10.1109/ciot63799.2024.10757049 · Cited by 4
A COMPARISON BETWEEN "OLD AND NEW" FEATURE EXTRACTION AND MATCHING TECHNIQUES IN PHOTOGRAMMETRY
Lingua, A. M., Marenchino, D., & Nex, F. (2009) · PORTO@Iris · Cited by 4
A Novel CBCD Approach Using MPEG-7 Motion Activity Descriptors
Roopalakshmi, R., & Reddy, G. R. M. (2011) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2011.36 · Cited by 4
A Query-by-Example Concept and User Interface for Global and Partial 3D Object Retrieval
Schreck, T., Bustos, B., & Walter, M. (2009) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 4
Probabilistic Kernel Combination for Hierarchical Object Categorization
Kapoor, A., Urtasun, R., & Darrell, T. J. (2009) · Cited by 4
Action Unit recognition in still images using graph-based feature selection
Sechkova, T., Tonchev, K., & Manolova, A. H. (2016) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2016.7737496 · Cited by 4
[POSTER] A Particle Filter Approach to Outdoor Localization Using Image-Based Rendering
Poglitsch, C., Arth, C., Schmalstieg, D., et al. (2015) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2015.39 · Cited by 4
An automated data exploitation system for airborne sensors
Chen, H., & McGurr, M. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2050675 · Cited by 4
Improved Data Association Method in Binocular Vision-SLAM
Wang, X., & Li, P. (2010) · International Conference on Intelligent Computation Technology and Automation · doi:10.1109/icicta.2010.344 · Cited by 4
Autonomic drone landing system based on LEDs pattern and visual markers recognition
Walendziuk, W., Skoczylas, M., Gadomer, Ł., et al. (2018) · Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA) · doi:10.1117/12.2501437 · Cited by 4
Side information generation with constrained relaxation for distributed multi-view video coding
Lv, H., Xiong, H., Zhang, Y., et al. (2008) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2008.4542201 · Cited by 4
FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer
Wu, W., Hu, X., Zhu Li, B., & Luo, X. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3581378 · Cited by 4
Fast estimation of asteroid shape and motion for spacecraft navigation
Tanimoto, A., Takeishi, N., Yairi, T., et al. (2013) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2013.6739687 · Cited by 4
Weighted CoHOG (W-CoHOG) Feature Extraction for Human Detection
Andavarapu, N., & Vatsavayi, V. K. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-0451-3_26 · Cited by 4
CREATION OF 3D MODELS FROM LARGE UNSTRUCTURED IMAGE AND VIDEO DATASETS
Hollick, J., Moncrieff, S., Belton, D., et al. (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-1-w1-133-2013 · Cited by 4
Automatic recognition of emotions from facial expressions
Xue, H., & Gertner, I. C. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2057796 · Cited by 4
Annotation-Free Word Spotting with Bag-of-Features HMMs
Rothacker, L., Wolf, F., & Fink, G. A. (2020) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421530013 · Cited by 4
Arigbabu, O. A., Liao, I. Y., Abdullah, N., & Noor, M. H. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54190-7_21 · Cited by 5
Efficient stitching method of tiled scanned microelectronic images
Zhu, H., Han, X., & Tao, Y. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac632a · Cited by 4
Gupta, D., Sharma, A., Kaur, P., et al. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14081-7 · Cited by 4
Wu, S., & Lew, M. S. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654994 · Cited by 4
FISHEYE LENS IMAGE CAPTURE ANALYSIS FOR INDOOR 3D RECONSTRUCTION AND EVALUATION
León-Vega, H. A., & Rodríguez-Laitón, M. I. (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-w17-179-2019 · Cited by 4
Near-Duplicate Video Detection Based on Temporal and Spatial Key Points
Zhang, D., Sun, Z., & Jia, K. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-3867-4_16 · Cited by 4
Feature Detection for Hand Hygiene StagesPreprint
Bakshi, R., Courtney, J., Berry, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.03015 · Cited by 4
A Multi-Camera Rig with Non-Overlapping Views for Dynamic Six-Degree-of-Freedom Measurement
Niu, Z., Ren, Y., Yang, L., et al. (2019) · Sensors · doi:10.3390/s19020250 · Cited by 4
MV–MR: Multi-Views and Multi-Representations for Self-Supervised Learning and Knowledge Distillation
Kinakh, V., Drozdova, M., & Voloshynovskiy, S. (2024) · Entropy · doi:10.3390/e26060466 · Cited by 4
COMPARISON BETWEEN RGB AND RGB-D CAMERAS FOR SUPPORTING LOW-COST GNSS URBAN NAVIGATION
Rossi, L., Gaetani, C. I. D., Pagliari, D., et al. (2018) · 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-2-991-2018 · Cited by 4
Zhang, Z., Chen, H., Zhou, L., et al. (2021) · International Conference on Communication, Image and Signal Processing (CCISP) · doi:10.1109/ccisp52774.2021.9639346 · Cited by 4
Entropy-based features for robust place recognition
Rady, S., Wagner, A., & Badreddin, E. (2008) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2008.4811362 · Cited by 4
Alonso‐Fernandez, F., Farrugia, R. A., & Bigün, J. (2017) · International Conference of the Biometrics Special Interest Group (BIOSIG) · doi:10.23919/biosig.2017.8053512 · Cited by 4
Covariant Peak Constraint for Accurate Keypoint Detection and Keypoint-Specific Descriptor Learning
Fu, Y., Zhang, P., Tang, F., & Wu, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3333211 · Cited by 4
Enhanced Fiilltterr-based SIFT Apprroach fforr Copy-Move Forrgerry Dettecttiion
Elaskily, M. A., Aslan, H. K., Dessouky, M. M., et al. (2019) · Menoufia Journal of Electronic Engineering Research · doi:10.21608/mjeer.2019.62749 · Cited by 4
Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types
Soni, P., Singh, M., Sharma, P., et al. (2025) · Journal of Electrical and Computer Engineering · doi:10.1155/jece/3889925 · Cited by 4
“…orientation is assigned to each of the extracted key points and then the fnal representation is formed with a unique set of features. b. HOG [57]: Tis feature extraction method works on the principle of gradient descriptors and records the appearance and shape of the sample along with the…”
Video super-resolution based on local invariant features matching
Ferreira, R. U., Hung, E. M., & de Queiroz, R. L. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467000 · Cited by 4
Learning-based essential matrix estimation for visual localization
Son, M., & Ko, K. H. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac046 · Cited by 4
Hazelhoff, L., Creusen, I. M., Woudsma, T., & de With, P. H. N. (2015) · Journal of Electronic Imaging · doi:10.1117/1.jei.24.6.061203 · Cited by 5
A GRAPH BASED OBJECT DESCRIPTION AND RECOGNITION METHODOLOGY
Bourbakis, N., Yuan, P. F., & Kakumanu, P. (2008) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213008004345 · Cited by 4
Automated video processing and image analysis software to support visual inspection of AGR cores
Murray, P., West, G. M., Law, K., et al. (2016) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 4
Multi-Sensor Mapping for Low Contrast, Quasi-Dynamic, Large Objects
Shah, V., Schild, K. M., Lindeman, M., et al. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2962357 · Cited by 4
Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor
Journal, B. S. (2017) · Baghdad Science Journal · doi:10.21123/bsj.14.3.651-661 · Cited by 4
Chen, K., Liang, G., Lu, Y., Li, H., & Wang, L. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3617801 · Cited by 5
“However, under low-light visibility, this method encounters difficulties in computing and matching SIFT key points [50], essential for determining and acquiring overlapping segments of synchronized videos.”
Fast Image Stitching Based on Improved SURF Algorithm Using Meaningful Features
Ahn, H., & Rhee, S.-B. (2012) · The KIPS Transactions PartB · doi:10.3745/kipstb.2012.19b.2.093 · Cited by 4
Forensic authentication of banknotes on mobile phones
Dewaele, T., Diephuis, M., Holotyak, T., et al. (2016) · Electronic Imaging · doi:10.2352/issn.2470-1173.2016.8.mwsf-083 · Cited by 4
Efficient cost aggregation for feature-vector-based wide-baseline stereo matching
Peng, X., Bouzerdoum, A., & Phung, S. L. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0249-y · Cited by 4
Sonar-Based Simultaneous Localization and Mapping Using a Neuro-Evolutionary Optimization
Kang, J.-G., An, S.-Y., Kim, S., et al. (2010) · Advanced Robotics · doi:10.1163/016918610x501435 · Cited by 4
SHREC—08 entry: Local 2D visual features for CAD Model retrieval
Osada, K., Furuya, T., & Ohbuchi, R. (2008) · IEEE International Conference on Shape Modeling and Applications · doi:10.1109/smi.2008.4547985 · Cited by 4
HiPeR: Hierarchical Progressive Exact Retrieval in Multidimensional Spaces
Bouteldja, N., Gouet-Brunet, V., & Scholl, M. (2008) · doi:10.1109/sisap.2008.19 · Cited by 4
Deep‐sea image stitching: Using multi‐channel fusion and improved AKAZE
Yuan, P., Fan, C. M., & Zhang, C. (2023) · IET Image Processing · doi:10.1049/ipr2.12918 · Cited by 4
Automatization Techniques. Slide Scanning
Sánchez, C., Ruiz-Santaquiteria, J., Aranda, J. L. E., et al. (2020) · Springer eBooks · doi:10.1007/978-3-030-39212-3_7 · Cited by 4
An overview of objectionable image detection
Shayan, J., Abdullah, S. M., & Karamizadeh, S. (2015) · International Symposium on Technology Management and Emerging Technologies (ISTMET) · doi:10.1109/istmet.2015.7359066 · Cited by 4
An FPGA-accelerated partial duplicate image retrieval engine for a document search system
Matsumura, H., Sugimura, M., Yamasaki, H., et al. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477662 · Cited by 4
Associating locations from wearable cameras
Rivera-Rubio, J., Alexiou, I., Dickens, L., Secoli, R., Lupu, E., & Bharath, A. A. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.35 · Cited by 4
Stereo Vision-Based Gamma-Ray Imaging for 3D Scene Data Fusion
Rathnayaka, P., Baek, S.-H., & Park, S.-Y. (2019) · IEEE Access · doi:10.1109/access.2019.2926542 · Cited by 4
A 2D-View Depth Image- and CNN-Based 3D Model Identification Method
Hong, Y., & Kim, J. (2017) · Applied Sciences · doi:10.3390/app7100988 · Cited by 4
Zhong, J., Gan, Y., & Yang, J. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103863 · Cited by 4
Plug-and-Play General Image Registration for Misaligned Multi-Modal Image Fusion
Zheng, T., Dong, G., Zhang, P., et al. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3570530 · Cited by 4
“We compare our PGMR with some SOTA registration methods, including SIFT [32], MIDIR [33], MURF [26], ReCoNet [9], SuperFusion [7], IMF [53], RIFT [51] and crossRAFT [52].”
PCB Image Registration Based on Improved SURF Algorithm
Li, X., He, H., Huang, C., et al. (2022) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml57342.2022.10009764 · Cited by 4
Motion Segmentation by Model-Based Clustering of Incomplete Trajectories
Karavasilis, V., Blekas, K., & Nikou, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_10 · Cited by 4
Various Discriminatory Features for Eye Detection
Chen, S., & Liu, C. (2012) · Intelligent systems reference library · doi:10.1007/978-3-642-28457-1_9 · Cited by 4
Adaptive weight assignment scheme for multi-task learning
Huq, A., & Pervin, M. T. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i1.pp173-178 · Cited by 5
From Orientation to Functional Modeling for Terrestrial and UAV Images
Mayer, H. (2015) · Cited by 4
Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges.Preprint
Liu, Y. M., Zhang, X., Zhou, J., Li, X., Li, Y., & Zhao, G. (2021) · arXiv (Cornell University) · Cited by 4
Jing, W., Kang, P., Di, D., et al. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.12.021 · Cited by 4
Cross-Generating GAN for Facial Identity Preserving
Chai, W., Deng, W., & Shen, H. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00028 · Cited by 4
Computer Vision Corrections Enhance UAV-Based Retrievals in Shallow Waters
Scilla, D., Lopez, O. A., Nieuwenhuis, B. O., et al. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3587478 · Cited by 4
Dense Bathymetry in Turbid Coastal Zones Using Airborne Hyperspectral Images
Vargas, S. M., Delrieux, C. A., Blanco, K. L., et al. (2021) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.21-00015r2 · Cited by 4
Robust Learning-Based Annotation of Medical Radiographs
Tao, Y., Peng, Z., Jian, B., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-11769-5_8 · Cited by 4
Meta-classifiers for multimodal document classification
Chen, S. D., Monga, V., & Moulin, P. (2009) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2009.5293343 · Cited by 4
Ambai, M., & Yoshida, Y. (2009) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1571941.1572101 · Cited by 4
Fast Implementation of Scale Invariant Feature Transform Based on CUDA
Lü, M. (2013) · Applied Mathematics & Information Sciences · doi:10.12785/amis/072l49 · Cited by 4
Design and Implementation of Alignment Device for Proximity Lithography System
Luo, P., Peng, Y., Li, Y., et al. (2023) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc58219.2023.10326802 · Cited by 4
Fusing Dynamic Percepts and Symbols in Cognitive Systems
Felsberg, M., & Granlund, G. H. (2008) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 4
Local Consensus Enhanced Siamese Network with Reciprocal Loss for Two-view Correspondence Learning
Wang, L., Wu, J., Fang, X., et al. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612458 · Cited by 4
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
