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
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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 3,760
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 2,360
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 2,030
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 2,901–3,000 of 56,204
Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds
Zaharescu, A., Boyer, E., & Horaud, R. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-012-0528-5 · Cited by 110
Active segmentation with fixation
Mishra, A., Aloimonos, Y., & Fah, C. L. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459254 · Cited by 113
RF-Net: An End-To-End Image Matching Network Based on Receptive Field
Shen, X., Wang, C., Li, X. S., Yu, Z., Li, J., Wen, C., Cheng, M., & He, Z. (2019) · 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.2019.00832 · Cited by 115
Wang, Z., Kieu, H., Nguyen, H., & Le, M. (2014) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2014.04.002 · Cited by 112
Zhao, M., Tang, B., & Tan, Q. (2016) · Measurement · doi:10.1016/j.measurement.2015.11.047 · Cited by 112
Object recognition using local invariant features for robotic applications: A survey
Loncomilla, P., Ruíz-del-Solar, J., & Martínez, L. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.05.021 · Cited by 112
Han, W., Chen, J., Wang, L., Feng, R., Li, F., Wu, L., Tian, T., & Yan, J. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3041450 · Cited by 119
Deep learning for processing and analysis of remote sensing big data: a technical review
Zhang, X., Zhou, Y., & Luo, J. (2021) · Big Earth Data · doi:10.1080/20964471.2021.1964879 · Cited by 122
Intervention AUVs: The Next Challenge
Ridao, P., Carreras, M., Ribas, D., Sanz, P. J., & Oliver, G. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140824-6-za-1003.02819 · Cited by 112
Fundamental matrix for cameras with radial distortion
Barreto, J. P., & Daniilidis, K. (2005) · doi:10.1109/iccv.2005.103 · Cited by 112
Scale-space texture description on SIFT-like textons
Xu, Y., Huang, S., Ji, H., & Fermuller, C. M. (2012) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.05.003 · Cited by 114
OV$^{2}$SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications
Ferrera, M., Eudes, A., Moras, J., Sanfourche, M., & Besnerais, G. L. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3058069 · Cited by 77
Scalable detection of partial near-duplicate videos by visual-temporal consistency
Tan, H.-K., Ngo, C.-W., Hong, R., & Chua, T.-S. (2009) · Proceedings of the 17th ACM international conference on Multimedia · doi:10.1145/1631272.1631295 · Cited by 110
Choi, C., & Christensen, H. I. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364912437213 · Cited by 112
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Cheung, W., & Hamarneh, G. (2007) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2007.356953 · Cited by 112
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Liu, J., & Shah, M. A. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408866 · Cited by 111
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Yang, M., & Yu, K. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116276 · Cited by 112
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Tsai, C.-L., Li, C.-Y., Yang, G., & Lin, K.-S. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2030324 · Cited by 111
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Karuppasamy, M., Nejadasl, F. K., Vulović, M., Koster, A. J., & Ravelli, R. B. G. (2011) · Journal of Synchrotron Radiation · doi:10.1107/s090904951100820x · Cited by 112
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Chen, J., Dowman, I. J., Li, S., Li, Z., Madden, M., Mills, J. P., Nicolas Paparoditis, A., Rottensteiner, F., Sester, M., Toth, C. K., et al. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.09.008 · Cited by 109
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Riemenschneider, H., Bódis-Szomorú, A., Weissenberg, J., & Gool, L. V. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_34 · Cited by 115
Traditional saliency reloaded: A good old model in new shape
Frintrop, S., Werner, T. A., & García, G. M. (2015) · 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.2015.7298603 · Cited by 110
Combined 2D–3D categorization and classification for multimodal perception systems
Márton, Z.-C., Pangercic, D., Blodow, N., & Beetz, M. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911415897 · Cited by 111
Object Detection in Traffic Videos: A Survey
Ghahremannezhad, H., Shi, H., & Liu, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3258683 · Cited by 126
A Survey on Waste Detection and Classification Using Deep Learning
Abdu, H., & Noor, M. H. M. (2022) · IEEE Access · doi:10.1109/access.2022.3226682 · Cited by 115
Fan, R., Ozgunalp, U., Wang, Y., Liu, M., & Pitas, I. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3060461 · Cited by 111
Pairwise geometric matching for large-scale object retrieval
Li, X., Larson, M., & Hanjalić, A. (2015) · 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.2015.7299151 · Cited by 106
Object-sensitive Deep Reinforcement Learning
Li, Y., Sycara, K. P., & Iyer, R. (2018) · EPiC series in computing · doi:10.29007/xtgm · Cited by 110
Census-independent population mapping in northern Nigeria
Weber, E. M., Seaman, V. Y., Stewart, R., Bird, T. J., Tatem, A. J., McKee, J. J., Bhaduri, B., Moehl, J., & Reith, A. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.09.024 · Cited by 113
Kashif, M. I., Deserno, T. M., Haak, D., & Jonas, S. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.11.006 · Cited by 111
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Zhang, L., Zhen, X., & Shao, L. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2328894 · Cited by 110
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Krause, J., Gebru, T., Deng, J., Li, L.-J., & Fei-Fei, L. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.15 · Cited by 112
Sun, Y., Zhang, X., Xin, Q., & Huang, J. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.06.005 · Cited by 110
Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection
Zhang, K., Li, T., Shen, S., Liu, B., Chen, J., & Liu, Q. (2020) · 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/cvpr42600.2020.00907 · Cited by 115
Automatic Personality and Interaction Style Recognition from Facebook Profile Pictures
Celli, F., Bruni, E., & Lepri, B. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654977 · Cited by 110
Unsupervised learning of visual taxonomies
Bart, E., Porteous, I., Perona, P., & Welling, M. (2008) · 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.2008.4587620 · Cited by 110
Ghost detection and removal for high dynamic range images: Recent advances
Srikantha, A., & Désiré Sidibé, A. (2012) · Signal Processing Image Communication · doi:10.1016/j.image.2012.02.001 · Cited by 107
PlaceAvoider: Steering First-Person Cameras away from Sensitive Spaces
Templeman, R., Korayem, M., Crandall, D., & Kapadia, A. C. (2014) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2014.23014 · Cited by 110
The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification
Zhao, B., Zhong, Y., Zhang, L., & Huang, B. (2016) · Remote Sensing · doi:10.3390/rs8020157 · Cited by 110
An architecture of optimised SIFT feature detection for an FPGA implementation of an image matcher
Yao, L., Feng, H., Zhu, Y., Jiang, Z., Zhao, D., & Feng, W. (2009) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2009.5377651 · Cited by 111
A Constant-Time Efficient Stereo SLAM System
Christopher Mei, A., Sibley, G., Cummins, M., Newman, P. M., & Reid, I. D. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.54 · Cited by 108
Histopathological Whole Slide Image Analysis Using Context-Based CBIR
Zheng, Y., Jiang, Z., Zhang, H., Xie, F., Ma, Y., Shi, H., & Zhao, Y. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2796130 · Cited by 109
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Bai, X., Wang, B., Yao, C., Liu, W., & Tu, Z. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2170082 · Cited by 124
Deep Multi-View Feature Learning for Person Re-Identification
Tao, D., Guo, Y., Yu, B., Pang, J., & Yu, Z. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2726580 · Cited by 109
Real-time small traffic sign detection with revised faster-RCNN
Han, C., Gao, G., & Zhang, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6428-0 · Cited by 112
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Nekrasov, V., Shen, C., & Reid, I. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03272 · Cited by 133
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Di Nuovo, A., Broz, F., Wang, N., Belpaeme, T., Cangelosi, A., Jones, R., Esposito, R., Cavallo, F., & Dario, P. (2018) · Intelligent Service Robotics · doi:10.1007/s11370-017-0237-6 · Cited by 111
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Adel, E., Elmogy, M. M., & El-Bakry, H. M. (2014) · International Journal of Computer Applications · doi:10.5120/17374-7818 · Cited by 111
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Otani, M., Nakashima, Y., Rahtu, E., Heikkilä, J., & Yokoya, N. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54193-8_23 · Cited by 131
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Tuytelaars, T. (2010) · 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.2010.5539911 · Cited by 114
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Liu, Z., Shang, Y., Li, T., Chen, G., Wang, Y., Hu, Q., & Zhu, P. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3234822 · Cited by 113
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Haala, N., Cramer, M., Weimer, F., et al. (2012) · 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-xxxviii-1-c22-7-2011 · Cited by 111
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Liu, S., Graham, S. L., Schulz, A., Kalloniatis, M., Zangerl, B., Cai, W., Gao, Y., Chua, B., Arvind, H., Grigg, J., et al. (2018) · Ophthalmology Glaucoma · doi:10.1016/j.ogla.2018.04.002 · Cited by 107
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Zhang, H., Gao, W., Chen, X., & Zhao, D. (2006) · Image and Vision Computing · doi:10.1016/j.imavis.2005.11.010 · Cited by 109
Yu, Z., Tan, E.-L., Ni, D., Qin, J., Chen, S., Li, S., Lei, B., & Wang, T. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2705031 · Cited by 109
Recent Advances in Object Detection in the Age of Deep Convolutional Neural NetworksPreprint
Agarwal, S., du Terrail, J. O., & Jurie, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03193 · Cited by 145
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Li, S., Yu, B., Wu, W., Su, S., & Ji, R. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.06.086 · Cited by 108
Faster human activity recognition with SVM
Chathuramali, K. G. M., & Rodrigo, R. P. (2012) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter.2012.6421415 · Cited by 109
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Guo, Y., Ding, G., Liu, L., Han, J., & Shao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2652730 · Cited by 108
Real-time visual loop-closure detection
Angeli, A., Doncieux, S., Meyer, J.-A., & Filliat, D. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543475 · Cited by 111
Automatic fruit classification using random forest algorithm
Zawbaa, H. M., Hazman, M., Abbass, M., & Hassanien, A. E. (2014) · International Conference on Hybrid Intelligent Systems (HIS) · doi:10.1109/his.2014.7086191 · Cited by 113
P3: Toward Privacy-Preserving Photo SharingPreprint
Ra, M.-R., Govindan, R., & Ortega, A. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.5062 · Cited by 104
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Zeng, H., Cao, Z., Lei Zhang, A., & Bovik, A. C. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2941778 · Cited by 108
Zhang, X., Yang, Y., Han, Z., Wang, H., & Gao, C. (2013) · ACM Computing Surveys · doi:10.1145/2522968.2522978 · Cited by 117
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Hua, Y., Mou, L., & Zhu, X. X. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2963364 · Cited by 128
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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 ›
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