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 3,760
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 52,801–52,900 of 56,204
Object recognition using force data clustering and HMM based shape recognition
Khandani, M. K. (2010)
Weakly supervised segment annotation via expectation kernel density estimationPreprint
Wang, L.-T., Li, Q., & Lu, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.06228
3D environment modeling and location tracking using off-the-shelf components
Luke, R. H. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2225491
An Efficient Projective Rectification for Trinocular Stereo Vision
Zhu, Q. X., & Luo, D. (2013) · International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) · doi:10.1109/pdcat.2013.34
Enhancement of the Fynbos Leaf Optical Recognition Application (FLORA-E)
Makumborenga, R. (2021) · Open University of Cape Town (University of Cape Town)
Corner-based keypoints for scale-Invariant detection of partially visible objects
Sluzek, A. S. (2006) · Machine Graphics and Vision
Multiple-window Bag of Features for Road Environment Recognition
Morita, S., Tan, J. K., Kim, H., et al. (2014) · Journal of Robotics Networking and Artificial Life · doi:10.2991/jrnal.2014.1.2.13
Multi-Scale Multi-Level Generative Model in Scene Classification
Xie, W., Xu, D., Tang, Y., et al. (2011) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e94.d.167
G-MAST3R-SFM: Graph-Based View Pruning and Multi-Stage Optimization for Robust SFM
Watanabe, T., Ito, S., Takama, N., et al. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630231
Research on Image Classification Based on Deep Learning
Gao, H. (2026) · International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) · doi:10.1109/aiita69518.2026.11567286
“Typical approaches included Scale-Invariant Feature Transform (SIFT) for keypoint description [1] , Histogram of Oriented Gradients (HOG) for pedestrian detection, Local Binary Patterns (LBP) for texture modeling, and multi-scale feature analysis based on wavelet transforms.”
Representation Learning with Adversarial Latent Autoencoders
Pidhorskyi, S. (2020) · doi:10.33915/etd.7810
Mohimont, L. (2022) · theses.fr (ABES) · doi:10.70675/b9bee43bz21b3z445az86e0z04cd381835b5
Insatiate boosted forest: Towards data exploitation in object detection
Ghorban, F., Su, Y., Meuter, M., & Kummert, A. (2017) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2017.8117026
A Machine Learning Model of Microscopic Agglutination Test for Diagnosis of LeptospirosisPreprint
Oyamada, Y., Ozuru, R., Masuzawa, T., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.08.410712
Depth-Reasoning-Driven Decision for Autonomous Traversal Flight of Quadrotors in Unmapped 3D SpacesPreprint
YongBin, S. U. N., Su, R., & Duan, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.5998398
Torres, D. P., & Vázquez, H. A. M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-37444-8_34
Error reduction for large rotational motion estimation of autonomous vehicle
Hoang, V.-D., Le, M.-H., Hernández, D. C., et al. (2014) · doi:10.1109/mecatronics.2014.7018591
Performance Evaluation of Scale Invariant Feature Transform
Mittal, A., & Kaur, N. (2014)
Fung, W. T., & Lam, K. (2022) · HKIE Transactions · doi:10.33430/v29n1thie-2020-0002
AR Superimposed Display System with Video Synchronization
Gotoh, Y., & Ikeda, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-11768-7_13
Song, B., & Yang, Q. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/aea4df
Babini, A., Frascella, S., Sech, G., et al. (2025) · Heritage · doi:10.3390/heritage8100417
FMCW Radar on LiDAR Map Localization in Structual Urban EnvironmentsPreprint
Ma, Y., Li, H., Zhao, X., et al. (2023) · doi:10.22541/au.168639860.03952631/v1
Malagi, V. P., Gayatri, V. V., Rangarajan, K., et al. (2013) · Lecture notes in electrical engineering · doi:10.1007/978-81-322-1143-3_18
Riccio, D., Casanova, A., & Fenu, G. (2014) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-4666-5966-7.ch009
Image Processing Techniques for Skin Diseases
Guo, S., Song, X., & Han, L. (2025) · Springer eBooks · doi:10.1007/978-981-96-6135-0_1
RST-invariant sketch retrieval based on circular description
Zhao, H., Kong, X., Fu, H., et al. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351754
Guye, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01242
He, X., Shi, X., Song, J., et al. (2026) · Infrastructures · doi:10.3390/infrastructures11070243
“The Scale-Invariant Feature Transform (SIFT) [43] is a scale-space extrema detection algorithm that can extract and locate feature points.”
Measures for the evaluation of segmentation methods used in model based people tracking methods
John, G., WEST, G. A. W., & Lazarescu, M. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202579
He, B., Wu, T., Zhang, Y., Luo, H., Chen, H., & Jia, F. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109664
Many-Inbetweens Browsing: Exploring Pre-Generated Inbetweens for 2D Animation
Sakamiya, J., Maejima, A., Koyama, Y., et al. (2025) · SIGGRAPH Asia Posters · doi:10.1145/3757374.3771510
“We use the scale-invariant feature transform (SIFT) [Lowe 2004] key-points as tracked feature points obtained from the start keyframe; these are selected according to their difference of Gaussians response magnitude because high-contrast areas enable reliable and stable tracking.”
Adding Context Information to Video Analysis for Surveillance Applications
Javanbakhti, S., Bao, X., Creusen, I. M., Hazelhoff, L., Sanberg, W. P., van de Wouw, D. W. J. M., Dubbelman, G., Zinger, S., & de With, P. H. N. (2015) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-4666-8850-6.ch005
Praphaphankul, N., Prayoonwet, W., Tiprak, K., et al. (2026) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2026.2698097
MSM: a scaling-based feature matching algorithm for images with large-scale differences
Ge, Q., Du, X., Xu, C., et al. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2543562
“…matching method and optimization scheme allow the algorithm to be superimposed with other feature matching algorithms, such as Ratio-Test(RT) (Lowe 2004), GMS (Bian et al. 2017), LPM (Ma et al. 2019) and MAGSAC (Barath, Matas, and Noskova 2019) etc. can still be utilized while applying the…”
InfluentialVisual event recognition in videos
Chan, K. P. M. (2012) · DR-NTU (Nanyang Technological University)
Wang, X., Zhang, Z., Zhang, M., et al. (2024) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit58233.2024.10540696
“The classic methods are SIFT [28] , SURF [29] , ORB [30] , and KAZE [31] , all of which have passed the test of time.”
MethodMultiple feature similarity based for image retrieval
Gengning, Z., Zhang, Y., Wang, J., et al. (2015) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2015.7341031
Multiplexed genetic tags for electron and fluorescence microscopy
Berezin, O., Piovesan, A., Graf, R., Samara, E., Sigmund, F., & Westmeyer, G. G. (2025) · Nature Protocols · doi:10.1038/s41596-025-01260-7
INDEXING OF SATELLITE IMAGES OF DIFFERENT SPATIAL RESOLUTIONS USING MULTI FEATURES
Luo, B., Jiang, S., Zhang, L., 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-xxxix-b3-231-2012
Optic Disc and Retinal Strain Due to Vertical Duction
Shetye, A., Meng, Q., Parunakian, E., et al. (2026) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.67.11.15
“A rigid affine transformation matrix was obtained using the scale-invariant feature transform (SIFT) algo-rithm, 32 which detects distinctive keypoints and descriptors for robust alignment despite varying noise and illumination.”
Quweider, M. K., Lei, H., Zhang, L., et al. (2018) · International Conference on Data Intelligence and Security (ICDIS) · doi:10.1109/icdis.2018.00038
An efficient approach for video retrieval by spatio-temporal features
Kumar, G. S. N., & Reddy, V. S. K. (2020) · International Journal of Knowledge-based and Intelligent Engineering Systems · doi:10.3233/kes-190420
Real-time high resolution 2D-plus-depth video generation using time-of-flight sensor
Zhang, K., Sun, L., & Yang, S. (2015) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2808492.2808527
Enhancing Online Security with Image-based Captchas
Powell, B. M. (2018) · doi:10.33915/etd.7119
Reuse your features: unifying retrieval and feature-metric alignment
Morlana, J., & Montiel, J. M. M. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160501 · Cited by 2
A Comparative Study of Rotation Angle Estimation Methods Based on Complex Moments
Lee, J., & Kim, W.-Y. (2012) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e95.d.1485
Wang, J., Kang, W., Zhou, W., et al. (2022) · International Conference on Electronic Information Engineering and Computer Communication (EIECC) · doi:10.1117/12.2634414
A CUR Decomposition-Based Mix-Order Framework for Large-Scale Hypergraph Matching
Zheng, Q., Zhang, M., & Yan, H. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3659463
“Traditional approaches, like scale-invariant feature transform (SIFT), define a measurement of the similarities between two features and match features in the first image to their nearest neighbor in the second with k nearestneighbor( k NN)[23].”
TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS
Zheng, E. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/2k5x-9r30
SLVVA: Scalable Land Viability via Vision-Language Architecture
Porwal, V., Scott, S. D., Bruce, N. D. B., & Biswas, A. (2024) · doi:10.21428/d82e957c.ba48518e
2D and 3D Geometric Attributes Estimation in Images via deep learning
Qiu, X. (2021) · theses.fr (ABES) · doi:10.70675/8a226ac9zb095z400fzbc92zf6461be079a6
VSS: A Storage System for Video Analytics [Technical Report]Preprint
Haynes, B. M., Daum, M., He, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.16604
A Boundary-based Marker Binary Coding Method for Augmented Reality Games
Yun, Y.-S., & Kim, T. (2010) · Journal of Korea Game Society
Inspection with Robotic Microscopic Imaging
Pedersen, L., Deans, M., Kunz, C., et al. (2005) · doi:10.2514/6.2005-6719
Yuxuan, 胡. H., Jinhong, 王. W., Xiang, 陈. C., et al. (2025) · Acta Optica Sinica · doi:10.3788/aos251120
Local Area Transform for Cross-Modality Correspondence Matching and Deep Scene RecognitionPreprint
Ryu, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00927
Medina, J., Duarte, K., & Upegui, E. (2019) · LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI) · doi:10.18687/laccei2019.1.1.159
Visual tracking via sparsity pattern learning
Wang, Y., Liu, Y., Li, Z., et al. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900046
A Minimal Solution for Binocular Camera Relative Pose Estimation Based on the Gravity Prior
Chen, D., Yan, K., Zhang, H., & Yu, Z. (2025) · Remote Sensing · doi:10.3390/rs17152560
Spatiotemporal Activity Based Moving Target Matching through Long Occlusions and Merge-Split
Du, Y., Li, W., & Cao, J. (2015) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2015.258
Image-based 3D Shape Reconstruction of Byzantine and Paleochristian Monuments
Symeonidis, C., Karakostas, I., Martinopoulou, E., et al. (2019) · Digital Presentation and Preservation of Cultural and Scientific Heritage · doi:10.55630/dipp.2019.9.1
Matsuzaki, Y., Okayasu, K., Imanari, T., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02199
Structure-Aware Deep Image Alignment for Automatic Wayside Rail Freight Vision
Shi, Y., Lyu, M., Yang, J., et al. (2026) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2026.3662026
A method of projective transformations graph adjustment for planar object panorama stitching
Savelyev, B., Mamay, I. B., Nikolaev, D., et al. (2019) · International Conference on Machine Vision · doi:10.1117/12.2522776
Faster-FIIS-GMS: a novel object detection framework for instance search
Liu, X., Zhang, T., & Shen, C. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15616-2
3D Multiscale Characterization of Discontinuities in Underwater Wet Welds
Paciornik, S., Silva, L. F., dos Santos, V. R., et al. (2015) · Microscopy and Microanalysis · doi:10.1017/s1431927615012878
A Note on Processing Time of Biometric Identification
Egawa, S., Awad, A. I., & Baba, K. (2015)
Pruning near-duplicate images for mobile landmark identification: A graph theoretical approach
Danışman, T., Martinet, J., & Bilasco, I. M. (2015) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2015.7153635
王, 益. (2026) · Journal of Image and Signal Processing · doi:10.12677/jisp.2026.151011
Texture Classification via Local Feature Representation of Multi-order Gradients
Wei, H., Liu, Q., Wang, Y., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-03731-8_79
Ponakala, R., & Dailey, M. N. (2019) · indiarxiv (OSF Preprints) · doi:10.35543/osf.io/sebr8
Wan, L., Liu, J., Cupkova, D., & Bard, J. (2026) · Springer eBooks · doi:10.1007/978-3-032-22607-5_23
Kilic, K. D., Ozyazici, K., Kisaoglu, H., et al. (2026) · Microscopy Research and Technique · doi:10.1002/jemt.70124
Bi, F., Lei, M., Qin, Y., et al. (2019) · The Journal of Engineering · doi:10.1049/joe.2019.0711
Calibration technology on binocular CCD ranging system based on improving Tsai two-step method
Lijuan, Z., Li, D., Zhao, H. Y., et al. (2012) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt.2012.6525931
Wang, Z., Zhu, J., Xue, Y., et al. (2015) · BMC Medical Imaging · doi:10.1186/s12880-015-0087-7
A new compositional technique for hand posture recognition
Simion, G., Gui, V., & Oteşteanu, M. (2009) · Annual Conference on Computers
Fast and compact visual codebook for large-scale object retrieval
Cen, S., Dong, Y., Bai, H., et al. (2013) · IEEE International Conference on Broadband Network & Multimedia Technology (IC-BNMT) · doi:10.1109/icbnmt.2013.6823910
Zhang, G., & Chen, Y. (2022) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-030-96110-7_1
Robust 3D object recognition based on improved Hough voting
Sa, J., Ru, T., Zhang, X., et al. (2026) · Machine Vision and Applications · doi:10.1007/s00138-026-01876-z
“Without loss of generality, this paper adopts the most commonly used nearest neighbor distance ratio (NNDR) feature matching strategy [ 25 ].”
Image filtering to increase the efficiency in the construction of underwater photomosaics
Bonin-Font, F., Castelló, P. B., Martín-Abadal, M., & Codina, G. A. O. (2026) · Frontiers in Marine Science · doi:10.3389/fmars.2026.1834681
Zou, Y., Chen, Y., & Zheng, Y. (2010)
Zhang, Z., Li, H., Xu, T., et al. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3773769 · Cited by 3
“SIFT [30] is used as a local feature description operator.”
A smart dialogue box for the hearing impaired
Limpiti, T., Khamyan, S., Chumsaeng, S., et al. (2019) · International Conference on Engineering, Applied Sciences, and Technology (ICEAST) · doi:10.1109/iceast.2019.8802596
The QUAKES‐I Stereoimaging Instrument for Measuring Surface Topography and Land Surface Processes
Donnellan, A., Padgett, C. W., Green, J., et al. (2025) · Earth and Space Science · doi:10.1029/2024ea004001
“Keypoints are distinctive invariant features that can be matched between multiple images (Lowe, 2004).”
Weakly supervised construction of a repository of iconic images
Weiland, L. R., Effelsberg, W., & Ponzetto, S. P. (2014) · doi:10.3115/v1/w14-5410
Viewpoint Planning for Environment Information Summarization with Time Constraint
Masuzawa, H., & Miura, J. (2010) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.28.938
Discovering Class-Specific Informative Patches and Its Application in Landmark Charaterization
Gao, S., Cheng, X., & Chia, L.-T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-11301-7_24
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