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 24,401–24,500 of 56,201
Zhang, Y., Tian, Y., Yang, G., Li, Z., Luo, M., Li, E., & Jing, F. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3455851 · Cited by 8
“Many handcrafted local features (e.g., SIFT [7], BRIEF [9], ORB [8], etc.) have been widely used in camera SLAM tasks [10], including visual odometry, loop closure detection, and relocalization.”
MethodLi, J., Tseng, K.-K., Hsieh, Z. Y., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0113132 · Cited by 8
An Efficient and Effective Image Decolorization Algorithm Based on Cumulative Distribution Function
Wu, T., Eising, C., Glavin, M., et al. (2024) · Journal of Imaging · doi:10.3390/jimaging10030051 · Cited by 8
“Also, the impact of the image decolorization process on the performance of feature extraction or other computer vision algorithms (e.g., HoG [2], SIFT [3]) is of interest. and each channel image measure.”
MethodLi, L., Wang, P., Wang, L., et al. (2024) · Optics Express · doi:10.1364/oe.519957 · Cited by 8
Preference elicitation: Obtaining gestural guidelines for PACS in neurosurgery
Madapana, N., Gonzalez, G., Taneja, R., et al. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.07.013 · Cited by 8
The planar two point algorithm
Booij, O., & Živković, Z. (2009) · UvA-DARE (University of Amsterdam) · Cited by 8
SSGM: Spatial Semantic Graph Matching for Loop Closure Detection in Indoor Environments
Tang, Y., Wang, M., Deng, Y., et al. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342317 · Cited by 8
Combining multiple sensor modalities for a localisation robust to smoke
Brunner, C. J., Peynot, T., & Vidal‐Calleja, T. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094614 · Cited by 13
Searching Complex Data Without an Index.
Satyanarayanan, M., Sukthankar, R., Goode, A., et al. (2010) · INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING · doi:10.47164/ijngc.v1i2.17 · Cited by 8
A New Shape Feature for Vehicle Classification in Thermal Video Sequences
Yang, D., & Park, H. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2452780 · Cited by 8
Wang, C., Lee, Y., Khalil, M.-A., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-15962-5 · Cited by 8
Local geometric consistency constraint for image retrieval
Xie, H., Gao, K., Zhang, Y., et al. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6115596 · Cited by 8
Krüger, J., Schultz, S., Heinz Handels, A., & Ehrhardt, J. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102839 · Cited by 8
Clustering-Based Descriptors for Fingerprint Indexing and Fast Retrieval
He, S., Zhang, C., & Hao, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12307-8_33 · Cited by 8
Lim, H., & Park, H. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2299771 · Cited by 8
Second-Order Configuration of Local Features for Geometrically Stable Image Matching and Retrieval
Wu, X., & Kashino, K. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2382985 · Cited by 8
Identifying a Joint in Medical Ultrasound Images Using Trained Classifiers
Wereszczyński, K., Segen, J., Kulbacki, M., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11331-9_75 · Cited by 8
Konstantakopoulos, F. S., Georga, E. I., Klampanas, K., Dimitris, R., Ioannou, N., & Fotiadis, D. I. (2019) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2019.00091 · Cited by 8
3D Landmarks Extraction from a Range Imager Data for SLAM
Wang, J., Hu, G., Huang, S., et al. (2009) · UTS ePRESS (University of Technology Sydney) · Cited by 8
Fast stereo visual odometry based on LK optical flow and ORB-SLAM2
Tang, C.-Y., Zhao, X., Chen, J.-F., Chen, L., & Zhou, Y. (2020) · Multimedia Systems · doi:10.1007/s00530-020-00662-9 · Cited by 13
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
Avraham, G., Straub, J., Shen, T., Yang, T.-Y., Germain, H., Sweeney, C., Balntas, V., Novotný, D., DeTone, D., & Newcombe, R. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00554 · Cited by 8
Resource- and content-aware, scalable stitching framework for remote sensing images
Eken, S., Mert, Ü., Koşunalp, S., et al. (2019) · Arabian Journal of Geosciences · doi:10.1007/s12517-019-4388-y · Cited by 8
Zhang, C., Wang, H., Li, H., et al. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7978392 · Cited by 8
Zheng, L., Liu, Y., Li, J., & Xu, W. (2017) · SIAM Journal on Imaging Sciences · doi:10.1137/16m1090478 · Cited by 8
Mobile Camera Localization Using Aerial-view Images
Toriya, H., Kitahara, I., & Ohta, Y. (2014) · IPSJ Transactions on Computer Vision and Applications · doi:10.2197/ipsjtcva.6.111 · Cited by 8
Esfahani, M. A., Wang, H., Bashari, B., Wu, K., & Yuan, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107424 · Cited by 8
Incremental Model Selection for Detection and Tracking of Planar Surfaces
Prankl, J., Zillich, M., Leibe, B., et al. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.87 · Cited by 8
Ghorbani, F., Ebadi, H., & Sedaghat, A. (2019) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-019-00944-4 · Cited by 8
Edge-Based Template Matching with a Harmonic Deformation Model
Hofhauser, A., Steger, C. T., & Navab, N. (2009) · Communications in computer and information science · doi:10.1007/978-3-642-10226-4_14 · Cited by 8
Semi-automated analysis of dot blots using ImageJ/Fiji
Klemm, A. H. (2020) · F1000Research · doi:10.12688/f1000research.27179.1 · Cited by 8
Asgarizadeh, M., & Pourghassem, H. (2013) · Signal Image and Video Processing · doi:10.1007/s11760-013-0431-8 · Cited by 8
A CNN based Hybrid approach towards automatic image registration
Arun, P. V., & Katiyar, S. K. (2013) · Geodezja i Kartografia · doi:10.2478/geocart-2013-0005 · Cited by 8
Zhao, Z., Patrinely, J. R., Saknīte, I., et al. (2021) · Microcirculation · doi:10.1111/micc.12725 · Cited by 8
Robust Registration of Aerial Image Sequences
Olson, C. F., Ansar, A. I., & Padgett, C. W. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10520-3_30 · Cited by 8
Manouchehri, N., & Bouguila, N. (2021) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon48115.2021.9589943 · Cited by 8
Log-likelihood score level fusion for improved cross-sensor smartphone periocular recognition
Alonso‐Fernandez, F., Raja, K. B., Busch, C., et al. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081211 · Cited by 8
The eye says it all: Periocular region methodologies
Ambika, D. R., Radhika, K. R., & Seshachalam, D. (2012) · doi:10.1109/icmcs.2012.6320240 · Cited by 8
FAST and FLANN for feature matching based on SURF
Huang, S., Sun, G., & Li, M. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9601366 · Cited by 8
Scalable recognition and tracking for mobile augmented reality
Ha, J., Cho, K., & Yang, H. S. (2010) · doi:10.1145/1900179.1900213 · Cited by 8
Spatial Statistics of Visual Keypoints for Texture Recognition
Nguyen, H.-G., Fablet, R., & Boucher, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_55 · Cited by 12
Hsieh, C. H., Lee, J.-D., & Wu, C. (2017) · Neuropsychiatry · doi:10.4172/neuropsychiatry.1000298 · Cited by 8
EMDQ: Removal of Image Feature Mismatches in Real-Time
Zhou, H., & Jayender, J. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3134456 · Cited by 8
SAR IMAGE MATCHING METHOD BASED ON IMPROVED SIFT FOR NAVIGATION SYSTEM
Ren, S., Chang, W., & Liu, X. (2011) · Progress In Electromagnetics Research M · doi:10.2528/pierm11042705 · Cited by 8
A mutual local-ternary-pattern based method for aligning differently exposed images
Wu, S., Yang, L., Xu, W., et al. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.07.010 · Cited by 8
Ling, H., Sun, Q., Ren, Z., Liu, Y., Wang, H., & Wang, Z. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547979 · Cited by 8
An end-to-end annotation-free machine vision system for detection of products on the rack
Santra, B., Shaw, A. K., & Mukherjee, D. P. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01186-6 · Cited by 9
Peng, B., Zhang, W., Hu, Y., et al. (2022) · Remote Sensing · doi:10.3390/rs14246291 · Cited by 8
Gender Recognition Based on Sift Features
Yousefi, S., & Zahedi, M. (2011) · International Journal of Artificial Intelligence & Applications · doi:10.5121/ijaia.2011.2308 · Cited by 8
Geo-clustering of Images with Missing GeoTags
Singh, V., Venkatesha, S., & Singh, A. K. (2010) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2010.76 · Cited by 8
An algorithm of image mosaic based on binary tree and eliminating distortion error
Qu, Z., Wei, X.-M., & Chen, S.-Q. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210354 · Cited by 8
Zhang, B., Dong, Y., Qi, X., Liu, Q., & Zheng, X. (2024) · Measurement · doi:10.1016/j.measurement.2024.114976 · Cited by 8
Deep Reverse Attack on SIFT Features With a Coarse-to-Fine GAN Model
Li, X., Zhu, G., Wang, S., Zhou, Y., & Zhang, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3367808 · Cited by 8
“1109/TCSVT.2024.3367808 coding algorithms in computer vision, scale invariant feature transform (SIFT) [6] exhibits excellent matching performance when images undergo transformation or rotation [7].”
BackgroundInfluentialGuo, X., Hou, B., Yang, C., Ma, S., Ren, B., Wang, S., & Jiao, L. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103244 · Cited by 9
A Comprehensive Survey on Visual Perception Methods for Intelligent Inspection of High Dam Hubs
Peng, Z., Li, L., Liu, D., Zhou, S., & Liu, Z. (2024) · Sensors · doi:10.3390/s24165246 · Cited by 9
“Yasin et al. [104] investigated a night vision obstacle detection and avoidance method based on bio-inspired visual sensors.”
MethodA study of the feature‐based digital image stabilization system
Hsieh, S., & Kao, C. (2010) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2010.9671651 · Cited by 8
Research on Video Image Stitching Technology Based on SURF
Zhang, Y., Yang, L., & Wang, Z. (2012) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2012.235 · Cited by 8
Offline Signature Verification using Neural Networks
Singh, J., & Sharma, M. (2012) · i-manager s Journal on Information Technology · doi:10.26634/jit.1.4.2015 · Cited by 8
Emerging Human-Toy Interaction Techniques with Augmented and Mixed Reality
Tang, J. K. T., & Tewell, J. (2015) · International series on computer entertainment and media technology · doi:10.1007/978-3-319-21323-1_5 · Cited by 8
Res2Net-based multi-scale and multi-attention model for traffic scene image classification
Gao, G., Guo, Y., Zhou, L., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300017 · Cited by 8
Multi-source adaptation embedding with feature selection by exploiting correlation information
Tao, J., Zhou, D., & Zhu, B. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.12.016 · Cited by 8
Functional Imaging of the Ocular Fundus Using an 8-Band Retinal Multispectral Imaging System
de Carvalho, E. R., Hoveling, R. J. M., Noorden, C. J. F. V., et al. (2020) · Instruments · doi:10.3390/instruments4020012 · Cited by 8
Towards applying image retrieval approach for finding semantic locations in autonomous vehicles
Unar, S., Su, Y., Xiu, Z., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16387-6 · Cited by 8
Wong, C.-C., Vong, C., Jiang, X., & Zhou, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3178879 · Cited by 8
Interactive 3-D indoor modeler for virtualizing service fields
Ishikawa, T., Thangamani, K., Kourogi, M., et al. (2011) · Virtual Reality · doi:10.1007/s10055-011-0202-1 · Cited by 8
Single-pixel compressive imaging based on random DoG filtering
Abedi, M., Sun, B. S., & Zheng, Z. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107746 · Cited by 8
Latulippe, M., Drouin, A., Giguère, P., et al. (2013) · International Joint Conference on Artificial Intelligence · Cited by 8
Foreign Object Detection of Electric Transmission Line with Dynamic Federated Learning
Haotian, S., Li, T., Wang, P., Xu, L., & Hongwei, Z. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/791/1/012159 · Cited by 8
Towards Edge-Aware Spatio-Temporal Filtering in Real-Time
Schaffner, M., Scheidegger, F., Cavigelli, L., Kaeslin, H., Luca Benini, C., & Smolić, A. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2757259 · Cited by 8
SAGS: Structure-Aware 3D Gaussian Splatting
Ververas, E., Potamias, R. A., Song, J., Deng, J., & Zafeiriou, S. P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72655-2_13 · Cited by 9
Vision-based navigation with efficient scene recognition
Kim, J., Park, C., & Kweon, I. S. (2011) · Intelligent Service Robotics · doi:10.1007/s11370-011-0091-x · Cited by 8
Cedillo-Hernández, M., Garcia-Ugalde, F. J., Cedillo-Hernández, A., et al. (2014) · International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) · doi:10.1109/icmeae.2014.16 · Cited by 8
Mobile robot vision-based navigation using self-organizing and incremental neural networks
Tangruamsub, S., Tsuboyama, M., Kawewong, A., & Hasegawa, O. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178739 · Cited by 8
Wang, Z., Yue, J., Han, J., et al. (2020) · Optik · doi:10.1016/j.ijleo.2020.164488 · Cited by 8
Hajebi, B., & Hajebi, P. (2021) · Journal on Computing and Cultural Heritage · doi:10.1145/3459607 · Cited by 8
Introducing locally affine-invariance constraints into lunar surface image correspondence
Zhang, Y.-R., Yang, X., Qiao, H., Liu, Z., & Liu, C. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.082 · Cited by 8
Object Detection in Clustered Scene Using Point Feature Matching for Non-repeating Texture Pattern
Santra, S., Mukherjee, P., Sardar, P., et al. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-981-32-9346-5_7 · Cited by 8
Adapting Traditional Macro and Micro Photography for Scientific Gigapixel Imaging
Longson, J., Cooper, G., Gibson, R., et al. (2018) · KiltHub Repository · doi:10.1184/r1/6709385 · Cited by 8
Camera calibration using identical objects
Wang, R., Jiang, G., Quan, L., et al. (2011) · Machine Vision and Applications · doi:10.1007/s00138-010-0316-6 · Cited by 8
Rana, A., Lowe, A. C.-Y., Lithgow, M. Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.01593 · Cited by 8
A Comparative Evaluation of Feature Detectors on Historic Repeat Photography
Gat, C., Albu, A. B., Germán, D. M., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24031-7_70 · Cited by 8
Robust Global Motion Estimation with Matrix Completion
Arrigoni, F., Rossi, B., Malapelle, F., Fragneto, P., & Fusiello, A. (2014) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xl-5-63-2014 · Cited by 8
Content-Based Video Copy Detection: PRISMA at TRECVID 2010
Barrios, J. M., & Bustos, B. (2010) · Cited by 8
Do We Need Binary Features for 3D Reconstruction?
Fan, B., Kong, Q., Sui, W., et al. (2016) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.144 · Cited by 8
Multi-layer feature extractions for image classification — Knowledge from deep CNNs
Ueki, K., & Kobayashi, T. (2015) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2015.7313924 · Cited by 10
An intelligent vehicle tracking technology based on SURF feature and Mean-shift algorithm
Liu, Y., Zhong-li, W., & Cai, B. (2014) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2014.7090500 · Cited by 8
Matsukawa, T., & Kurita, T. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.07.018 · Cited by 8
Imaging the Vascular Network of the Human Spleen from Immunostained Serial Sections
Ulrich, C., Lobachev, O., Steiniger, B., & Guthe, M. (2014) · Eurographics Workshop on Visual Computing for Biomedicine · doi:10.2312/vcbm.20141185 · Cited by 10
He, F., & Habib, A. F. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.06.011 · Cited by 8
Sound Localization from Motion: Jointly Learning Sound Direction and Camera Rotation
Chen, Z., Qian, S., & Owens, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00726 · Cited by 9
A vector SIFT operator for interest point detection in hyperspectral imagery
Dorado-Muñoz, L. P., Vélez-Reyes, M., Mukherjee, A., et al. (2010) · Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers.2010.5594965 · Cited by 8
Distributed Face Recognition in Wireless Sensor Networks
Lin, Q., Yang, J., Zhang, B., et al. (2014) · International Journal of Distributed Sensor Networks · doi:10.1155/2014/175864 · Cited by 8
Depth Recovery of Complex Surfaces from Texture-less Pair of Stereo Images
Kumar, S., Kumar, M., Raman, B., et al. (2009) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.316 · Cited by 8
Díaz‐Ricaurte, J. C., Guevara-Molina, E. C., & Serrano, F. C. (2019) · Journal of Natural History · doi:10.1080/00222933.2019.1668490 · Cited by 8
Unbiased Decoding of Biologically Motivated Visual Feature Descriptors
Felsberg, M., Öfjäll, K., & Lenz, R. (2015) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00020 · Cited by 9
Integral P-channels for fast and robust region matching
Pagani, A., Stricker, D., & Felsberg, M. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414467 · Cited by 8
SIFT algorithm-based 3D pose estimation of femur
Zhang, X., Zhu, Y., Li, C., Zhao, J., & Li, G. (2014) · Bio-Medical Materials and Engineering · doi:10.3233/bme-141103 · Cited by 8
Utilizing internet photos for indoor mapping and localization - opportunities and challenges
Dong, J., Xiao, Y., Ou, Z., et al. (2015) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2015.7179457 · Cited by 8
Häme, Y., Angelini, E. D., Parikh, M. A., et al. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163828 · Cited by 8
Trajectory-Aligned Space-Time Tokens for Few-Shot Action Recognition
Kumar, P., Padmanabhan, N., Luo, L., Rambhatla, S. S., & Shrivastava, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72764-1_27 · Cited by 9
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
