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 34,201–34,300 of 56,201
Assessing photograph aesthetic quality with color based descriptor
Zhu, X., & Moh, T.-S. (2016) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2016.7568339 · Cited by 3
Vision based approach for active selection of robot’s localization action
Frontoni, E., Mancini, A., & Zingaretti, P. (2007) · 2006 14th Mediterranean Conference on Control and Automation · doi:10.1109/med.2007.4433677 · Cited by 3
One-step Local Feature Extraction using CNN
Zhou, Y., Zhu, Z., & Xin, B. (2020) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc48988.2020.9238094 · Cited by 3
Quadratic-chi similarity metric learning for histogram feature
Cai, X., Xiao, B., Wang, C., et al. (2011) · doi:10.1109/acpr.2011.6166698 · Cited by 3
A deconvolution approach to modelling surges in COVID-19 cases and deaths
Melnyk, A., Kozarov, L., & Wachsmann‐Hogiu, S. (2022) · Scientific Reports · doi:10.1038/s41598-023-29198-4 · Cited by 2
Learning Deep Representations via Contrastive Learning for Instance Retrieval
Wu, T., Luo, T., & Wunsch, D. C. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022224 · Cited by 3
Jegan, R., & Jayagowri, R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91916-6.00007-2 · Cited by 3
Front Vehicle Blind Spot Translucentization Based on Augmented Reality
Lin, C.-T., Lin, Y., Chen, L.-T., et al. (2013) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2013.6692210 · Cited by 3
Plasma membrane impacts of particulate matter on the blood-brain barrier
Guo, C., Mukhopadhyay, S., Zanetti-Domingues, L. C., Lees, R. M., Gonzalez, E. G., Bateman, B. C., Ward, A., Mee, L., Smith, R., Gant, T. W., et al. (2026) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2026.181348 · Cited by 5
Multi-scale Image Co-segmentation
Es-salhi, R., Daoudi, I., Weber, J., et al. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-287-990-5_30 · Cited by 3
A Mobile Augmented Reality System to Enjoy the Sagrada Familia
Gimeno-Sancho, J., Pardo-Carpia, F., Morillo, P., et al. (2010) · Eurographics · doi:10.2312/ega.20101008 · Cited by 3
Singh, S., Wang, Y., & Ding, L. (2013) · doi:10.1145/2501217.2501218 · Cited by 3
Atsumi, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41914-0_41 · Cited by 3
Pedestrian Detection Based on Gradient Direction Histogram
Mao, L., & Tang, L. (2022) · IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) · doi:10.1109/ipec54454.2022.9777626 · Cited by 3
Compass aided visual-inertial odometry
Wang, Y., Zhang, T., Wang, Y., Ma, J., Li, Y., & Han, J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.12.029 · Cited by 3
Automatic Hanging Point Learning from Random Shape Generation and Physical Function Validation
Takeuchi, K., Yanokura, I., Kakiuchi, Y., Okada, K., & Inaba, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561484 · Cited by 3
Dilmi, W., Ferik, S. E., Saif, A. A., et al. (2025) · Transportation research procedia · doi:10.1016/j.trpro.2025.03.113 · Cited by 3
Home Bridge - Smart Elderly Care System
Sitparoopan, T., Chellapillai, V., Arulmoli, J., Chandrasiri, S., & Kugathasan, A. (2021) · International Informatics and Software Engineering Conference (IISEC) · doi:10.1109/iisec54230.2021.9672411 · Cited by 3
An improved genetic algorithms-based seam carving method
Oliveira, S. A. F., & Neto, A. R. R. (2016) · International Journal of Innovative Computing and Applications · doi:10.1504/ijica.2016.080864 · Cited by 3
SOFTCBIR: Object Searching in Videos Combining Keypoint Matching and Graduated Assignment
Luo, M. R., DeMenthon, D., Yu, X., et al. (2006) · doi:10.21236/ada448477 · Cited by 3
Bimodal Biometric Method Fusing Hand Shape and Palmprint Modalities at Rank Level
Charfi, N., Trichili, H., & Solaiman, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67074-4_52 · Cited by 3
ARTEMIS.Ubimedia at TRECVID 2012: Instance Search Task.
Bursuc, A., Zaharia, T. B., Martinot, O., et al. (2012) · Cited by 3
Mobile Robot for Exploration and Manipulation Inside a Nuclear Power Plant
Ritzberger, N., Emenshuber, S., Schubert, G., et al. (2017) · Annals of DAAAM for ... & proceedings of the ... International DAAAM Symposium · doi:10.2507/28th.daaam.proceedings.012 · Cited by 3
FPSelector: A Flexible Path Selector for Mobile Augmented Reality Offloading
Zhu, Y., Huang, Y., Qiao, X., Liu, X., Su, X. J., Brunström, A., Alay, Ö., & Tarkoma, S. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3556473 · Cited by 4
“This operation may have variable data sizes and computation complexities, depending on the specific algorithm used, such as Scale-Invariant Feature Transform (SIFT) [16], Oriented FAST and Rotated BRIEF (ORB) [17], and Speeded Up Robust Features (SURF) [18].”
Denoising Reuse: Exploiting Inter-Frame Motion Consistency for Efficient Video Generation
Wang, C., Yan, S., Chen, Y., Wang, X., Wang, Y., Dong, M., Yang, X., Li, D., Zhu, R., Clifton, D. A., et al. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3548728 · Cited by 3
“Detector-based methods detect and describe key points, with traditional techniques like SIFT [45] and ORB [57] relying on hand-crafted designs.”
A Vision Based Method for Object Recognition
Zhang, Y., & Liang, J. (2016) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce.2016.40 · Cited by 3
Online fragments-based scale invariant electro-optic tracking with SIFT
Cui, X., Wu, Q.-Z., & Zhou, J. (2015) · Optik · doi:10.1016/j.ijleo.2015.04.071 · Cited by 3
Patil, A. N., Mohanty, S. N., & Khan, T. (2025) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558333174241009112642 · Cited by 3
Pharmaceutical Blister Pack Recognition using Local Features
Saitoh, K. (2013) · Machine Vision and Applications · Cited by 3
Image Retrieval based on VA-File and Multi-Resolution BOW
Lipin, W., & Pu, J. (2014) · Applied Mathematics & Information Sciences · doi:10.12785/amis/090152 · Cited by 3
Metric based on multi-order spaces for cross-modal retrieval
Zhang, L., Ma, B., Li, G., et al. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019409 · Cited by 3
Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
Wang, Y., Liu, Y., Huang, W., et al. (2023) · Applied Sciences · doi:10.3390/app132111890 · Cited by 3
Videos Semantic Indexing using Image Classification.
Yan, S., Lin, Y., Lv, F., et al. (2010) · Cited by 3
Yoon, S., Khan, F. M., & Brémond, F. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.187 · Cited by 3
Blink Detection Using 3D Cross Model
Xu, Y., Jiang, Y., & Sun, Y. (2012) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2012.181 · Cited by 3
An Improved Multi Biometric System for Authentication
Sharma, O. P., & Sivaramkumar, P. (2019) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.b3217.129219 · Cited by 3
Nested One-to-One Symmetric Classification Method on a Fuzzy SVM for Moving Vehicles
Qin, G., Huang, X., & Chen, Y. (2017) · Symmetry · doi:10.3390/sym9040048 · Cited by 3
A Deep Learning-Based Approach for Camera Motion Classification
Ouenniche, K., Țapu, R., & Zaharia, T. B. (2021) · European Workshop on Visual Information Processing · doi:10.1109/euvip50544.2021.9483961 · Cited by 3
Sasikala, D., & Neelaveni, R. (2010) · doi:10.1109/icccct.2010.5670603 · Cited by 3
The Parallel Distributed Image Search Engine (ParaDISE)Preprint
Markonis, D., Schaer, R., de Herrera, A. G. S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.05596 · Cited by 3
Safe Navigation on a Mobile Robot using Local and Temporal Visual Cues
Li, X., & Sridharan, M. (2010) · IOS Press eBooks · doi:10.3233/978-1-60750-613-3-121 · Cited by 3
Mamba-convolution hybrid network for underwater image enhancement
Chen, H., Wang, Y., Wu, L., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-15404-y · Cited by 3
“…non-local prior 37 for underwater depth estimation, utilize the Canny operator 38 for underwater edge detection, employ the SIFT keypoint detection 39 for underwater keypoint detection, adopt BASNet 40 for underwater saliency detection, and apply a superpixel-based clustering algorithm 41 for…”
Meaningful Bags of Words for Medical Image Classification and Retrieval
Foncubierta–Rodríguez, A., de Herrera, A. G. S., & Müller, H. (2015) · Springer eBooks · doi:10.1007/978-3-319-17963-6_5 · Cited by 3
Robotic and document analysis cross-fertilization: Improving place cells based robot navigation
Rojas-Castro, D. M., Revel, A., & Ménard, M. (2016) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2016.7838838 · Cited by 3
Development of a Low-Cost Augmented Reality Head-Mounted Display Prototype
D’Angelo, T., Delabrida, S. E., Oliveira, R., et al. (2017) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-5225-3290-3.ch001 · Cited by 3
Georeferenced Social Multimedia as Volunteered Geographic Information
Newsam, S. D., & Leung, D. (2018) · The Geojournal library · doi:10.1007/978-94-024-1531-5_12 · Cited by 3
Printed Circuit Board Defect Detection Based on Lightweight Deep Learning Fusion Model
Wang, Y., Chen, Z., Wang, J., et al. (2025) · Sensors · doi:10.3390/s25247403 · Cited by 3
“They primarily classify images using histograms of oriented gradients (HOG) [2] and scale invariant feature transform (SIFT) [3].”
Saavedra, J. M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12568-8_80 · Cited by 3
The Survey of Vision-based 3D Modeling Techniques
Ruan, M. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/910/1/012012 · Cited by 3
Development of a Mobile Robot System Based on the Soar Cognitive Architecture
Hanford, S. D., & Long, L. N. (2014) · Journal of Aerospace Information Systems · doi:10.2514/1.i010191 · Cited by 3
Rotation and Scaling Image Using PCA
Abass, H. H., & Al-Salbi, F. M. M. (2011) · Computer and Information Science · doi:10.5539/cis.v5n1p97 · Cited by 3
Edge-Based Loop Closure Detection in Visual SLAM
Mendieta, D. F., Capua, F. R., Tarrio, J., & Moreyra, M. L. (2018) · Argentine Conference on Automatic Control (AADECA) · doi:10.23919/aadeca.2018.8577273 · Cited by 3
Sparse summarization of robotic grasping data
Hjelm, M., Ek, C. H., Detry, R. J., Kjellström, H., & Kragić, D. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630707 · Cited by 3
Probabilistic models of perceptual features
Feldman, J. (2014) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199686858.013.049 · Cited by 4
Non-rigid image registration for visible color and thermal IR face
Yang, Y. (2016) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2016.7846571 · Cited by 3
Image segmentation using random features
Bull, G., Gao, J., & Antolovich, M. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2050885 · Cited by 3
Establishment and extension of a compact and robust binary feature descriptor for UAV image matching
Li, C.-L., Hu, C., Zhu, H., Tang, F., Zhao, L., Zhou, Y., & Zhang, S. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adaa93 · Cited by 3
“The most well-known descriptor in computer vision is scale-invariant feature transform (SIFT) [14].”
Multisensor navigation in urban environment
Monica, A. D., Ruotsalainen, L., & Dovis, F. (2018) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans.2018.8373448 · Cited by 3
A Survey on the Deep Learning-Based Mismatch Removal: Principles and Methods
Chen, S., Deng, C., Zhang, Y., Wang, Y., Zhang, Q., & Zhou, Z. (2023) · IEEE Access · doi:10.1109/access.2023.3320682 · Cited by 3
Ground target localization and recognition via descriptors fusion
Kamel, M. M., Taha, H. S., Salama, G. I., et al. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/610/1/012015 · Cited by 3
COOPERATIVE IMAGE ORIENTATION CONSIDERING DYNAMIC OBJECTS
Trusheim, P., Mehltretter, M., Rottensteiner, F., et al. (2022) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-1-2022-169-2022 · Cited by 3
Comparison of Feature Extraction Techniques for Watermark Synchronization
Lee, H.-Y., Lee, H.-K., & Lee, J. (2005) · Lecture notes in computer science · doi:10.1007/11554028_43 · Cited by 3
Image Mosaic Technology Based on an Optimized Method of SIFT Algorithm
杨, 程. (2015) · Journal of Image and Signal Processing · doi:10.12677/jisp.2015.44017 · Cited by 3
Explicit feature disentanglement for visual place recognition across appearance changes
Tang, L., Wang, Y., Tan, Q., & Xiong, R. (2021) · International Journal of Advanced Robotic Systems · doi:10.1177/17298814211037497 · Cited by 3
Elsaeed, E. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017915073 · Cited by 3
Image Retrieval Using Multi-layer Orientation Histograms
Li, X., Liu, G., & Lu, F. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11719-z · Cited by 3
Building pair-wise visual word tree for efficent image re-ranking
Zhang, S. S., Huang, Q., Lu, Y., et al. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5494964 · Cited by 3
3D Object Shape Categorization in Domestic Environments
Mueller, C. A. (2012) · Publication Server of Bonn-Rhein-Sieg University of Applied Sciences (Bonn-Rhein-Sieg University of Applied Sciences) · doi:10.18418/978-3-96043-009-4 · Cited by 3
Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors
Dong, G., Yan, H., Lv, G., et al. (2019) · Symmetry · doi:10.3390/sym11080998 · Cited by 3
Improved global context descriptor for describing interest regions
Liu, J., & Zeng, G. (2012) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-012-1244-6 · Cited by 3
High-recall calibration monitoring for stereo cameras
Moravec, J., & Šára, R. (2024) · Pattern Analysis and Applications · doi:10.1007/s10044-024-01264-1 · Cited by 3
“To reduce false matches, one can use the so-called Lowe’s ratio [28]: The descriptor distance to two nearest neighbours is computed, and the match ( i , j ) is removed from (4) if their ratio is lower than a given threshold.”
Analyzing Brain Morphology on the Bag-of-Features Manifold
Chauvin, L., Kumar, K., Desrosiers, C., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_4 · Cited by 3
Fast and scalable keypoint recognition and image retrieval using binary codes
Ventura, J., & Höllerer, T. (2011) · Proceedings · doi:10.1109/wacv.2011.5711573 · Cited by 3
From Machinery to Biology: A Review on Mapless Autonomous Underwater Navigation
Zhu, W., & Cui, W. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13112202 · Cited by 4
Xie, B., Liu, Y., Zhang, H., & Yu, J. Q. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.12.007 · Cited by 3
Decision Making using Data Fusion for Wheelchair Navigation
Cristian, R. I., Vladu, I. C., Ganea, E., et al. (2019) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc.2019.8885823 · Cited by 3
Coverless Steganography Based on Low Similarity Feature Selection in DCT Domain
Tan, L., Liu, J., Zhou, Y., et al. (2023) · Radioengineering · doi:10.13164/re.2023.0603 · Cited by 3
Real-Time and Robust Stereo Visual Navigation Localization Algorithm Based on ORB
Xu, Y. X., & Chen, F. (2012) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.241-244.478 · Cited by 3
Multi-sensory based novel household object categorization system by using interactive behaviours
Guan, H., & Zhang, J. (2016) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2016.7866570 · Cited by 3
Egocentric Hand Detection Via Dynamic Region GrowingPreprint
Huang, S., Wang, W., He, S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.03677 · Cited by 3
Multiscale binarised statistical image features for symmetric unconstrained face matching
Arashloo, S. R. (2014) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2014.6999748 · Cited by 3
The Compensated HS Optical Flow Estimation Based on Matching Harris Corner Points
Lu, Z.-Y., & Liu, W. (2010) · International Conference on Electrical and Control Engineering · doi:10.1109/icece.2010.562 · Cited by 5
Video picture-in-picture detection using spatio-temporal slicing
Qian, M., Mou, L., Li, J., et al. (2014) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2014.6890580 · Cited by 3
Dependence structure of Gabor wavelets for face recognition
Li, C., Xue, Y., & Huang, Y. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8280789 · Cited by 3
A graph-theoretic approach to classifier combination
Hou, J., Feng, Z., & Zhang, B. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288058 · Cited by 3
A GPU-assisted personal video organizing system
Mohiuddin, K. W., & Narayanan, P. J. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130289 · Cited by 3
SalsaNext+: A Multimodal-Based Point Cloud Semantic Segmentation With Range and RGB Images
Sánchez-García, F., Montiel-Marín, S., Antunes, M., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3559580 · Cited by 3
Liu, C., Hsaio, W.-H., Xiao, B., Chen, C., & Wu, W.-L. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.071 · Cited by 3
IMAGE MOSAICKING AND SEMI-AUTOMATIC CRACK EVALUATION FOR VISUAL INSPECTION OF CONCRETE STRUCTURES
Fujita, Y., TAGUCHI, T., & Hamamoto, Y. (2018) · Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) · doi:10.2208/jscejcei.74.18 · Cited by 3
View-based 3D model retrieval via supervised multi-view feature learning
Liu, A.-A., Shi, Y., Nie, W., et al. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5076-0 · Cited by 3
Peng, S., Huang, F., Qiang, X., et al. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18402-w · Cited by 3
“In 2004, Lowe proposed the scale invariant feature transform (SIFT) algorithm, which is based on scale-space, and it can scale, ro-tate, and affine an image to keep invariance [17].”
REAL-TIME OBJECT DETECTION USING TWO BACKGROUND MODELS UNDER SHAKING CAMERA
Kim, T.-H., & Jo, K.-H. (2009) · International Journal of Information Acquisition · doi:10.1142/s0219878909001783 · Cited by 3
A Rotation Invariant Descriptor Using Multi-directional and High-Order Gradients
Mo, H., Li, Q., Hao, Y., Zhang, H., & Li, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03398-9_32 · Cited by 3
Image-matching algorithm using key points with scalability and rotation of objects
Баумана, М. И. Н. Э., & Супрун, Д. Е. (2016) · Herald of the Bauman Moscow State Technical University Series Instrument Engineering · doi:10.18698/0236-3933-2016-5-86-98 · Cited by 3
Enhanced GPT Correlation for 2D Projection Transformation Invariant Template Matching
Wakahara, T., & Yamashita, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24947-6_36 · Cited by 3
A generic approach to classify sports video shots and its application in event detection
Li, L., Duan, L., Huang, Q., et al. (2009) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/1734605.1734654 · Cited by 3
Tarasiuk, P., Tomczyk, A., & Stasiak, B. (2020) · Applied Sciences · doi:10.3390/app10155186 · Cited by 3
Advanced feature point transformation of corner points for mobile object recognition
Yin, X., Kim, D.-H., Hong, C.-P., et al. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2241-6 · Cited by 3
Image retrieval via generalized I-divergence in the bag-of-visual-words framework
Rocha, B. M., Nogueira, E. A., Guliato, D., et al. (2014) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs.2014.7050090 · Cited by 3
Joint learning for image-based handbag recommendation
Wang, Y., Li, S., & Kot, A. C. (2015) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2015.7177520 · Cited by 3
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