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 21,301–21,400 of 56,201
Enabling low bitrate mobile visual recognition
Su, Y.-C., Chiu, T.-H., Chen, Y., Yeh, C.-Y., & Hsu, W. H. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502110 · Cited by 9
Subgraphs Matching-Based Side Information Generation for Distributed Multiview Video Coding
Xiong, H., Lv, H., Zhang, Y., et al. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2009/386795 · Cited by 10
Detailed analysis and evaluation of keypoint extraction methods
Wu, X., Shi, Z., & Zhong, Y. (2010) · International Conference on Computer Application and System Modeling · doi:10.1109/iccasm.2010.5620674 · Cited by 10
Radial deformation and failure of stabilised soft clay under uniaxial compression
Li, Z.-S., Zhang, Y., Janiszewski, M., et al. (2022) · SOILS AND FOUNDATIONS · doi:10.1016/j.sandf.2022.101213 · Cited by 10
Visual Data Exploration to Feature Space Definition
Brandoli, B., Eler, D. M., Paulovich, F. V., et al. (2010) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2010.13 · Cited by 10
Coarse-to-Fine Image DeHashing Using Deep Pyramidal Residual Learning
Wang, Y., Ward, R. K., & Wang, Z. J. (2019) · IEEE Signal Processing Letters · doi:10.1109/lsp.2019.2917073 · Cited by 10
An Efficient Dense Stereo Matching Method for Planetary Rover
Li, H., Chen, L., & Li, F. (2019) · IEEE Access · doi:10.1109/access.2019.2910194 · Cited by 10
Unsupervised Learning of Hierarchical Semantics of Objects (hSOs)
Parikh, D., & Chen, T. (2007) · 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.2007.383370 · Cited by 10
Brain tumor detection and classification using SIFT in MRI images
Altaei, M. S. M., & Kamil, S. Y. (2020) · AIP conference proceedings · doi:10.1063/5.0031014 · Cited by 10
Yin, J., Li, W., Shen, J.-H., Zhou, C., Li, S., Suo, J., Yang, J., Jia, R., & Lv, C. (2025) · Plants · doi:10.3390/plants14050675 · Cited by 12
“For example, SIFT (scale-invariant feature transform) [14,15] and HOG (histogram of oriented gradients) [16] are common feature extraction methods.”
Quantitative analysis of colorectal lesions observed on magnified endoscopy images
Onji, K., Yoshida, S., Tanaka, S., Kawase, R., Takemura, Y., Oka, S., Tamaki, T., Raytchev, B., Kaneda, K., Yoshihara, M., et al. (2011) · Journal of Gastroenterology · doi:10.1007/s00535-011-0459-x · Cited by 10
Application of fuzzy rule-based classifier to CBIR in comparison with other classifiers
Jaworska, T. (2014) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2014.6980818 · Cited by 10
Vision-based Runway Detection and Landing for Unmanned Aerial Vehicle Enhanced Autonomy
Tsapparellas, K., Jelev, N., Waters, J., et al. (2023) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma57826.2023.10215523 · Cited by 10
Key-Frame Extraction Using Weighted Multi-view Convex Mixture Models and Spectral Clustering
Ioannidis, A. I., Chasanis, V. T., & Likas, A. C. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.596 · Cited by 10
Qi, K., Yang, C., Hu, C., Zhai, H., Guan, Q., & Shen, S. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.022 · Cited by 11
Accurate 3D anthropometric measurement using compact multi-view imaging
Chen, Q., Song, L., Zhang, X., et al. (2025) · Measurement · doi:10.1016/j.measurement.2025.116777 · Cited by 10
Robust Feature Matching via Local Consensus
Chen, J., Yang, M., Peng, C., et al. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3151226 · Cited by 10
Fast and robust structure-based multimodal geospatial image matching
Ye, Y., Bruzzone, L., Shan, J., et al. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8128160 · Cited by 10
Augmented Feature Fusion for Image Retrieval System
Zhou, Y., Zeng, D., Zhang, S. S., et al. (2015) · ACM International Conference on Multimedia Retrieval (ICMR) · doi:10.1145/2671188.2749288 · Cited by 10
Chilukuri, P. K., Padala, P., Padala, P., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3052474 · Cited by 10
Long-Term Incremental Web-Supervised Learning of Visual Concepts via Random Savannas
Ewerth, R., Ballafkir, K., Muhling, M., et al. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2186956 · Cited by 10
Image summarization using topic modelling
Sharma, V., Kumar, A., Agrawal, N., et al. (2015) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa.2015.7412194 · Cited by 10
Self-Supervised Robust Feature Matching Pipeline for Teach and Repeat Navigation
Rouček, T., Amjadi, A. S., Rozsypálek, Z., Broughton, G., Blaha, J., Kusumam, K., & Krajník, T. (2022) · Sensors · doi:10.3390/s22082836 · Cited by 10
Architecture for multi-camera vision system for automated measurement of automotive components
Lins, R. G., & Kurka, P. R. G. (2013) · 2020 IEEE International Systems Conference (SysCon) · doi:10.1109/syscon.2013.6549931 · Cited by 10
Wu, Z., Wu, D., Li, N., et al. (2024) · Remote Sensing · doi:10.3390/rs17010109 · Cited by 10
Lee, S. M., Choi, B.-L., Baek, U. B., & Choi, B. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.112470 · Cited by 12
Blind Recognition of Touched Keys: Attack and CountermeasuresPreprint
Yue, Q., Ling, Z., Liu, B., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.4829 · Cited by 10
Learning Near Duplicate Image Pairs using Convolutional Neural Networks
Zhang, Y. (2018) · International Journal of Performability Engineering · doi:10.23940/ijpe.18.01.p18.168177 · Cited by 10
A deep network model based on subspaces: A novel approach for image classification
Gatto, B. B., de Souza, L. S., & Santos, E. M. D. (2017) · doi:10.23919/mva.2017.7986894 · Cited by 10
Similarity metric learning for sketch-based 3D object retrieval
Furuya, T., & Ohbuchi, R. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2171-3 · Cited by 10
Semi-supervised object recognition using flickr images
Chatzilari, E., Nikolopoulos, S., Papadopoulos, S., et al. (2011) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2011.5972550 · Cited by 10
Effective image database search via dimensionality reduction
Dahl, A. B., & Aanæs, H. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4562957 · Cited by 10
The Mask-SIFT cascading classifier for pornography detection
Steel, C. M. S. (2012) · World Congress on Internet Security · Cited by 10
BioFors: A Large Biomedical Image Forensics Dataset
Sabir, E., Nandi, S., AbdAlmageed, W., & Natarajan, P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01078 · Cited by 12
“Figure 10 shows the median number of keypoints identified in each image class using three off-the-shelf descriptor extractors: SIFT [30], ORB [38], BRIEF [9].”
MethodUrban, S., Weinmann, M., & Hinz, S. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.08.011 · Cited by 10
Fast Similarity Search for High-Dimensional Dataset
Wang, Q., & You, S. O. (2006) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2006.78 · Cited by 10
Ohno, K., Date, H., & Kanai, S. (2021) · International Journal of Automation Technology · doi:10.20965/ijat.2021.p0324 · Cited by 10
Online learning for automatic segmentation of 3D data
Tombari, F., Stefano, L. D., & Giardino, S. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094649 · Cited by 9
Locality-constrained max-margin sparse coding
Hsaio, W.-H., Liu, C., & Wu, W.-L. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.12.015 · Cited by 10
Classifying vehicles with convolutional neural network and feature encoding
Wang, S., Li, Z., Zhang, H., et al. (2016) · 2022 IEEE 20th International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin.2016.7819266 · Cited by 10
Iwamoto, K., Mase, R., & Nomura, T. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738600 · Cited by 10
Structure Preservation and Seam Optimization for Parallax-Tolerant Image Stitching
Wen, S., Wang, X., Zhang, W., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3194245 · Cited by 10
Real-time scene recognition on embedded system with SIFT keypoints and a new descriptor
Han, X., He, W., Yuan, K., et al. (2013) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2013.6618104 · Cited by 10
Asphalt Pavement Texture 3D Reconstruction Based on Binocular Vision System with SIFT Algorithm
Ran, M., Xiao, S., Zhou, X., et al. (2017) · International Conference on Smart Grid and Electrical Automation (ICSGEA) · doi:10.1109/icsgea.2017.114 · Cited by 10
A Reality Integrated BIM for Architectural Heritage Conservation
Apollonio, F. I., Gaiani, M., & Sun, Z. H. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-7314-2.ch006 · Cited by 10
A facial features detector integrating holistic facial information and part-based model
Mostafa, E. A., Ali, A., Shalaby, A. M., et al. (2015) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301324 · Cited by 10
Comparative proteomic analysis of pollen of Trifolium pratense, T. alexandrinum and T. repens
Treigytė, G., Zaikova, I., Matuzevičius, D., et al. (2014) · Zemdirbyste-Agriculture · doi:10.13080/z-a.2014.101.058 · Cited by 10
Recognition, object detection and segmentation of white background photos based on deep learning
Ning, X., Zhu, W. Q., & Chen, S. (2017) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2017.7967401 · Cited by 11
Robust hierarchical framework for image classification via sparse representation
Zuo, Y., & Zhang, B. (2011) · Tsinghua Science & Technology · doi:10.1016/s1007-0214(11)70003-7 · Cited by 10
ForestVO: Enhancing Visual Odometry in Forest Environments Through ForestGlue
Pritchard, T., Ijaz, S., Clark, R., & Koçer, B. B. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3557738 · Cited by 10
“Traditional methods, such as SIFT [31] and ORB [32] rely on handcrafted descriptors and heuristic approaches to extract local features, which are then matched based on Euclidean distance or other similarity metrics [27].”
3D surface reconstruction of smooth and textureless objects
Hafeez, J., Kwon, S., Lee, S.-H., et al. (2017) · doi:10.1109/etiict.2017.7977026 · Cited by 10
Multi-lingual text recognition from video frames
Sharma, N., Mandal, R., Sharma, R., et al. (2015) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2015.7333902 · Cited by 10
Topological map induction using neighbourhood information of places
Werner, F., Sitte, J., & Maire, F. (2012) · Autonomous Robots · doi:10.1007/s10514-012-9276-1 · Cited by 10
Mixture Distributions for Weakly Supervised Classification in Remote Sensing Images
Bordes, J.-B., & Véronique Prinet, A. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.77 · Cited by 10
Chellamuthu, K., Liu, J., Yao, J., et al. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950544 · Cited by 10
Beyond SIFT using binary features in Loop Closure Detection
Han, L., Zhou, G., Xu, L., & Fang, L. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206261 · Cited by 11
Emerging, Collective Intelligence for Personal, Organisational and Social Use
Diplaris, S., Sonnenbichler, A. C., Kaczanowski, T., et al. (2011) · Studies in computational intelligence · doi:10.1007/978-3-642-20344-2_20 · Cited by 10
Reliable feature point detection and object pose estimation using photometric quasi-invariant SIFT
Park, J., Park, K.-W., Baeg, S., et al. (2008) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2008.4694451 · Cited by 10
Li, H., Huang, D., Morvan, J., & Chen, L. (2011) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb.2011.6117555 · Cited by 11
Efficient Label Collection for Image Datasets via Hierarchical Clustering
Wigness, M., Draper, B. A., & Beveridge, J. R. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1039-1 · Cited by 11
A Combined Wiimote-Camera Tracking System for Small Aerial Vehicles
Vargas, H., Preza, E., & Velázquez, R. (2009) · Electronics, Robotics and Automotive Mechanics Conference · doi:10.1109/cerma.2009.79 · Cited by 10
Deformable Template Tracking in 1ms
Tan, D. J., Holzer, S., Navab, N., & Ilić, S. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.27 · Cited by 10
Object-Plane Co-Represented and Graph Propagation-Based Semantic Descriptor for Relocalization
Deng, Z., Zhang, Y., Wu, Y., Ge, Z., Hu, X., & Sun, W. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3191056 · Cited by 10
An Improved ORB Feature Point Matching Algorithm
Zhang, Y., Li, C., Cao, C., et al. (2018) · doi:10.1145/3297156.3297184 · Cited by 10
Unsupervised classification of dynamic obstacles in urban environments
Katz, R., Nieto, J. I., & Nebot, E. (2010) · Journal of Field Robotics · doi:10.1002/rob.20349 · Cited by 10
Matching of images with projective distortion using transform invariant low-rank textures
Zhang, Q., Li, Y., Blum, R. S., et al. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.04.007 · Cited by 10
Wei, Y., Kasireddy, V., & Akinci, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91635-4_3 · Cited by 11
Dou, J., & Li, J. (2015) · Optik · doi:10.1016/j.ijleo.2015.04.031 · Cited by 10
Robust Arm and Hand Tracking by Unsupervised Context Learning
Spruyt, V., Ledda, A., & Philips, W. R. (2014) · Sensors · doi:10.3390/s140712023 · Cited by 10
An, J., Meng, F., & Yan, Z.-J. (2021) · BioData Mining · doi:10.1186/s13040-021-00242-1 · Cited by 9
Chu, W.-T., & Lin, C. (2010) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2010.01.006 · Cited by 10
Zhao, B., Lin, S., Qi, X., et al. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-3276-1 · Cited by 10
RetractedImage Augmented Laser Scan Matching for Indoor Localization
Naikal, N., Kua, J., & Zakhor, A. (2009) · doi:10.21236/ada512518 · Cited by 10
Distance Measurement of Unmanned Aerial Vehicles Using Vision-Based Systems in Unknown Environments
Rahmaniar, W., Wang, W.-J., Caesarendra, W., et al. (2021) · Electronics · doi:10.3390/electronics10141647 · Cited by 10
Shoitan, R., Moussa, M. M., & Elshoura, S. M. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09258-x · Cited by 10
Distinctive texture features from perspective-invariant keypoints
Gossow, D., Weikersdorfer, D., München, T. U., Beetz, M., & Profile, S. (2012) · Cited by 10
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions
Noor, A., Li, K., Ammar, A., Koubâa, A., Benjdira, B., & Tovar, E. (2021) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi52975.2021.00037 · Cited by 11
Randomized Sub-Volume Partitioning for Part-Based 3D Model Retrieval
Furuya, T., Kurabe, S., & Ohbuchi, R. (2015) · Eurographics · doi:10.2312/3dor.20151050 · Cited by 10
Automatic image annotation using fuzzy association rules and decision tree
Li, Z., Li, L., Yan, K., et al. (2016) · Multimedia Systems · doi:10.1007/s00530-016-0530-9 · Cited by 10
Learning Deep Representations with Probabilistic Knowledge TransferPreprint
Passalis, N., & Tefas, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10837 · Cited by 16
Image stack alignment in full-field X-ray absorption spectroscopy using SIFT_PyOCL
Paleo, P., Pouyet, É., & Kieffer, J. (2014) · Journal of Synchrotron Radiation · doi:10.1107/s160057751400023x · Cited by 10
Video synchronization and its application to object transfer
Cao, X., Wu, L. Y., Xiao, J., et al. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.04.015 · Cited by 10
Robotic Mapping in the Real World : Performance Evaluation and System Integration
Schwertfeger, S. (2012) · Cited by 9
Sift features based object tracking with discrete wavelet transform
Yang, W., Fang, B., Tang, Y., et al. (2009) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2009.5207409 · Cited by 10
Multi-View Shape Estimation of Transparent Containers
Xompero, A., Sánchez-Matilla, R., Modas, A., et al. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054112 · Cited by 10
Mission-time 3D reconstruction with quality estimation
Istenič, K., Ila, V., Polok, L., Gracias, N., & García, R. (2017) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceanse.2017.8084708 · Cited by 10
An Image-Based Framework for Ocean Feature Detection and Analysis
Banesh, D., Petersen, M., Ahrens, J. P., Turton, T. L., Samsel, F., Schoonover, J., & Hamann, B. (2021) · Journal of Geovisualization and Spatial Analysis · doi:10.1007/s41651-021-00085-8 · Cited by 10
Moreira, D., Theisen, W., Scheirer, W. J., Bharati, A., Brogan, J., & Rocha, A. D. R. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-16-7621-5_15 · Cited by 10
Cut-and-Paste Dataset Generation for Balancing Domain Gaps in Object Instance Detection
Yun, W., Kim, T., Lee, J., Kim, J., & Kim, J. (2021) · IEEE Access · doi:10.1109/access.2021.3051964 · Cited by 12
A biomimetic chiral auxetic vertebral meta-shell
Sorrentino, A., Genovese, K., Nicolini, L., et al. (2024) · Smart Materials and Structures · doi:10.1088/1361-665x/ad7ca5 · Cited by 10
“The image processing procedure starts with the definition of a mask to delineate the ROI and the initialization of the image registration using the Scale Invariant Feature Transform algorithm [59].”
AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding
Yandex, A. B., & Lempitsky, V. (2017) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.523 · Cited by 10
A deep multi-feature distance metric learning method for pedestrian re-identification
Deng, X., Liao, K., Zheng, Y., et al. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10458-8 · Cited by 10
An open architecture to develop a handheld device for helping visually impaired people
Battaglia, F., & Iannizzotto, G. (2012) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2012.6311360 · Cited by 10
Content level access to digital library of India pages
Krishnan, P., Shekhar, R., & Jawahar, C. V. (2012) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/2425333.2425338 · Cited by 10
Elwan, M., Amein, A. S., Mousa, A., et al. (2022) · Security and Communication Networks · doi:10.1155/2022/5669069 · Cited by 10
Unsupervised Identification of Multiple Objects of Interest from Multiple Images: dISCOVER
Parikh, D., & Chen, T. (2007) · Springer eBooks · doi:10.1007/978-3-540-76390-1_48 · Cited by 10
Diminished Reality Via Multiple Hand-Held CAMERAS
Jarusirisawad, S., & Saitô, H. (2007) · ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) · doi:10.1109/icdsc.2007.4357531 · Cited by 10
A Self-Supervised Learning Manipulator Grasping Approach Based on Instance Segmentation
Shu, X., Liu, C., Li, T., Wang, C., & Chi, C. (2018) · IEEE Access · doi:10.1109/access.2018.2877796 · Cited by 9
Nejadasl, F. K., & Lindenbergh, R. (2014) · Sensors · doi:10.3390/s140916630 · Cited by 10
Urban Change Detection based on Local Features and Multiscale Fusion
Huo, C., Zhou, Z., Liu, Q., Cheng, J., Lu, H., & Chen, K. (2008) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2008.4779581 · Cited by 10
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
