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 19,101–19,200 of 56,201
Exploring Intrinsic Discrimination and Consistency for Weakly Supervised Object Localization
Wang, C., Xu, R., Xu, S., Meng, W., Wang, R., & Zhang, X. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3356174 · Cited by 12
“In this work, we reduce the geometric transformation to the similarity transformation [59] with specific rotations and scales.”
MethodMotion Segmentation Using Optical Flow for Pedestrian Detection from Moving Vehicle
Hariyono, J., Hoang, V.-D., & Jo, K.-H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11289-3_21 · Cited by 12
Deep-learning-based moving target detection for unmanned air vehicles
Yao, H., Yu, Q., Xing, X., et al. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8029186 · Cited by 12
IMPROVING RANSAC FEATURE MATCHING BASED ON GEOMETRIC RELATION
Elashry, A., van der Sluis, B., & Toth, C. K. (2021) · 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/isprs-archives-xliii-b2-2021-321-2021 · Cited by 12
Yamazaki, K. (2016) · Autonomous Robots · doi:10.1007/s10514-016-9559-z · Cited by 12
Sequence to Sequence Learning for Optical Character RecognitionPreprint
Sahu, D. K., & Sukhwani, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04176 · Cited by 16
Li, F., Feng, R., Han, W., & Wang, L. (2020) · Optics Express · doi:10.1364/oe.395866 · Cited by 12
Aspects of metric spaces in computation
Skala, M. (2008) · UWSpace (University of Waterloo) · Cited by 12
Multiple Stage Residual Model for Accurate Image Classification
Bai, S., Wang, X., Yao, C., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16865-4_28 · Cited by 12
ASSESSMENT OF SMARTPHONE-BASED TECHNOLOGY FOR REMOTE ENVIRONMENTAL MONITORING AND ITS DEVELOPMENT
Lee, S., Kim, J., Jin, C., et al. (2012) · Instrumentation Science & Technology · doi:10.1080/10739149.2012.700534 · Cited by 12
Zhang, J., Tsoi, A. C., & Lo, S. (2014) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2014.6889596 · Cited by 14
Research on the Application of Visual SLAM in Embedded GPU
Ma, T., Bai, N., Shi, W., Wu, X., Wang, L., Wu, T. T., & Zhao, C. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/6691262 · Cited by 12
Božić-Štulić, D., Kružić, S., Gotovac, S., et al. (2018) · Journal of Communications Software and Systems · doi:10.24138/jcomss.v14i1.441 · Cited by 12
Bastos, R., & Dias, M. S. (2008) · Repositório do ISCTE-IUL · Cited by 12
High-accuracy image matching using phase-only correlation and its application
Miura, M., Sakai, S., Aoyama, S., Ishii, J., Ito, K., & Aoki, T. (2012) · Society of Instrument and Control Engineers of Japan · Cited by 16
Human Action Recognition Using Salient Opponent-Based Motion Features
Shabani, A.-H., Zelek, J., & Clausi, D. A. (2010) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2010.54 · Cited by 12
Physical activity recognition based on motion in images acquired by a wearable camera
Zhang, H., Li, L., Jia, W., Fernstrom, J. D., Sclabassi, R. J., Mao, Z., & Sun, M. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.02.014 · Cited by 12
Tensorized Projection for High-Dimensional Binary Embedding
Hong, W., Meng, J., & Yuan, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11292 · Cited by 12
Contribution of UAS to the monitoring at the Lärchberg-Galgenwald landslide (Austria)
Seier, G., Sulzer, W., Lindbichler, P., et al. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1454627 · Cited by 12
EXMOVES: Mid-level Features for Efficient Action Recognition and Video Analysis
Tran, D., & Torresani, L. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0905-6 · Cited by 12
Yu, G., Bao, X., Xu, X. L., et al. (2022) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2022.168488 · Cited by 12
Zheng, Y.-T., Neo, S.-Y., Chua, T., et al. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282341 · Cited by 12
Jointly Deep Multi-View Learning for Clustering AnalysisPreprint
Lin, B., Xie, Y., Qu, Y., Li, C., & Liang, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.06220 · Cited by 13
Generation of Large Mosaic Images for Vegetation Monitoring Using a Small Unmanned Aerial Vehicle
Suzuki, T., AMANO, Y., HASHIZUME, T., et al. (2010) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2010.p0212 · Cited by 12
Touil, D. E., Terki, N., & Medouakh, S. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1364-z · Cited by 12
Colour Interest Points for Image Retrieval
Sebe, N., Gevers, T., & Hanbury, A. (2007) · Cited by 12
Hot spot detection and classification in LWIR videos for person recognition
Teutsch, M., & Müller, T. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2015754 · Cited by 12
Efficient Traffic Sign Detection Using Bag of Visual Words and Multi-scales SIFT
Nguyen, K.-D., Le, D.-D., & Duong, D. A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42051-1_54 · Cited by 12
Multi-Scale Attention Learning Network for Facial Expression Recognition
Dong, Q., Ren, W., Gao, Y. Q., Jiang, W., & Liu, H. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3336257 · Cited by 12
Large Margin Coupled Feature Learning for cross-modal face recognition
Jin, Y., Lu, J., & Ruan, Q. (2015) · International Conference on Biometrics · doi:10.1109/icb.2015.7139097 · Cited by 12
Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration
Lochman, Y., Dobosevych, O., Hryniv, R. O., & Pritts, J. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00293 · Cited by 12
Learning Second-Order Attentive Context for Efficient Correspondence Pruning
Ye, X., Zhao, W., Lu, H. F., & Cao, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25431 · Cited by 12
A survey on digital video stabilization
Sarıgül, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14726-1 · Cited by 13
Interactive learning of visually symmetric objects
Li, W. H., & Kleeman, L. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354616 · Cited by 12
Object Detection by Keygraph Classification
Hashimoto, M., & Junior, R. M. C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02124-4_23 · Cited by 12
Lyu, Y., Schiopu, I., Cornelis, B., & Munteanu, A. (2022) · Sensors · doi:10.3390/s22218439 · Cited by 16
IMPROVE: Collaborative Design Review in Mobile Mixed Reality
Santos, P., Stork, A., Gierlinger, T., et al. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73335-5_59 · Cited by 12
Hierarchical Scene Coordinate Classification and Regression for Visual Localization
Li, X., Wang, S., Zhao, Y., Verbeek, J. J., & Kannala, J. (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/cvpr42600.2020.01200 · Cited by 156
Efficient large-scale oblique image matching based on cascade hashing and match data scheduling
Zhang, Q., Zheng, S., Zhang, C., et al. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109442 · Cited by 12
Ghosh, D., Kaabouch, N., & Fevig, R. A. (2014) · Computer and Information Science · doi:10.5539/cis.v7n2p68 · Cited by 12
Image categorization through optimum path forest and visual words
Papa, J. P., & Rocha, A. D. R. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116475 · Cited by 12
Çelik, H. H., Hanjalić, A., & Hendriks, E. A. (2009) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.07.001 · Cited by 12
Transforming Architectural Digitisation: Advancements in AI-Driven 3D Reality-Based Modelling
Zhang, K., & Fassi, F. (2025) · Heritage · doi:10.3390/heritage8020081 · Cited by 18
Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots
Majid, Y., Yang, M. Z., Cai, Z., Li, L., & Wang, C. (2021) · Robotica · doi:10.1017/s0263574720001526 · Cited by 13
Manouchehri, N., Bouguila, N., & Fan, W. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01023-6 · Cited by 12
Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras With Human Semantics
Huang, B., Ju, J., Shu, Y., & Wang, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3328371 · Cited by 12
“The other is that the lack of sufficient local image features ( e . g ., SIFT [15]) can be used to constrain sparse camera geometries in a dynamic multi-person scene.”
Background3D Reconstruction of Plants Under Outdoor Conditions Using Image-Based Computer Vision
Paturkar, A., Gupta, G. S., & Bailey, D. G. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9187-3_25 · Cited by 12
Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage
Toubal, I. E., Al-Shakarji, N., Cornelison, D., et al. (2023) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2023.3288470 · Cited by 12
Unsupervised Spiking Instance Segmentation on Event Data Using STDP Features
Kirkland, P., Manna, D. L., Vicente, Á., & Caterina, G. D. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3191968 · Cited by 13
Zhang, X., Liao, P., Chen, G., Zhu, K., Tan, X., Wang, T., Li, X., & Wu, H. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102795 · Cited by 11
Pattern Recognition for Ecological Science and Environmental Monitoring: An Initial Report
MacLeod, N. (2007) · CRC Press eBooks · doi:10.1201/9781420008074-15 · Cited by 12
Rgbd object pose recognition using local-global multi-kernel regression
El-Gaaly, T., Torki, M., Elgammal, A., et al. (2012) · Cited by 12
Incomplete multi-view learning via half-quadratic minimization
Jiang, J., Tao, H., Fan, R., et al. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.043 · Cited by 12
FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net
Jafari, M., Li, R., Xing, Y., Auer, D. P., Francis, S., Garibaldi, J. M., & Chen, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34110-7_44 · Cited by 21
Optical and SAR Image Registration Based on Pseudo-SAR Image Generation Strategy
Hu, C., Zhu, R., Sun, X., Li, X., & Xiang, D. (2023) · Remote Sensing · doi:10.3390/rs15143528 · Cited by 12
Multidimensional Extra Evidence Mining for Image Sentiment Analysis
Zhang, H., Wu, J., Shi, H., Jiang, Z., Ji, D., Yuan, T., & Li, G. (2020) · IEEE Access · doi:10.1109/access.2020.2999128 · Cited by 12
Range–Visual–Inertial Odometry with Coarse-to-Fine Image Registration Fusion for UAV Localization
Hao, Y., He, M., Liu, Y., Liu, J., & Meng, Z. (2023) · Drones · doi:10.3390/drones7080540 · Cited by 13
A systematic comparison of the accuracy of monocular RGB tracking and LiDAR for neuronavigation
Khan, T., Biehl, J. T., Andrews, E. G., et al. (2022) · Healthcare Technology Letters · doi:10.1049/htl2.12036 · Cited by 12
Xin, X., Zhu Li, B., & Katsaggelos, A. K. (2013) · Signal Processing Image Communication · doi:10.1016/j.image.2012.11.003 · Cited by 12
A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification
Huang, C. C., Lee, C. E., & Thing, V. L. L. (2019) · International Journal of Digital Crime and Forensics · doi:10.4018/ijdcf.2020010102 · Cited by 12
Murtin, C. I., Frindel, C., Rousseau, D., & Ito, K. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.10.027 · Cited by 12
Optimal Paths in a Constrained Image Plane for Purely Image-Based Parking
Salaris, P., Belo, F. A. W., Fontanelli, D., et al. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4651050 · Cited by 12
Contextual local primitives for binary patent image retrieval
Bhatti, N., Hanbury, A., & Stöttinger, J. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4808-5 · Cited by 13
Shape grammar parsing : application to image-based modeling
Teboul, O. (2011) · doi:10.70675/36465d8bz8269z49e7z8f85zedde7e6d6011 · Cited by 12
Automatic Laser Control System for Selective Laser Sintering
Zhang, L., Phillips, T., Mok, A. K., et al. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2867007 · Cited by 12
Bi, H., Chang, H., Wang, X., et al. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3476089 · Cited by 12
Neidle, C., Nash, J., Michael, N., et al. (2009) · Cited by 12
Face recognition technology research and implementation based on mobile phone system
Li, H., & Zhu, X. (2016) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2016.7603310 · Cited by 12
Drift robust non-rigid optical flow enhancement for long sequences
Li, W., Cosker, D. P., & Brown, M. (2016) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169099 · Cited by 12
Face alignment by Component Adaptive Mechanism
Wan, J., Li, J., Chang, J., et al. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.068 · Cited by 12
Template-Based Monocular 3D Recovery of Elastic Shapes Using Lagrangian Multipliers
Haouchine, N., & Cotin, S. (2017) · 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.2017.381 · Cited by 12
Learning of Graphical Models and Efficient Inference for Object Class Recognition
Bergtholdt, M., Kappes, J. H., & Schnörr, C. (2006) · Lecture notes in computer science · doi:10.1007/11861898_28 · Cited by 12
A non-contact sleep posture sensing strategy considering three dimensional human body models
Ren, A., Dong, B., Lv, X., Zhu, T., Hu, F., & Yang, X. (2016) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2016.7924734 · Cited by 13
A Selective Weighted Late Fusion for Visual Concept Recognition
Liu, N., Dellandréa, E., Zhu, C., Bichot, C., & Chen, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33885-4_43 · Cited by 12
An Approach for Thyroid Nodule Analysis Using Thermographic Images
González, J. R., Rodrigues, É. O., Damião, C. P., et al. (2017) · Series in bioengineering · doi:10.1007/978-981-10-3147-2_26 · Cited by 12
“For this case, some techniques can be used to autonomously detect points in the images such as the Scale-Invariant Feature Transform (SIFT) [15], Af fi ne Scale-Invariant Feature Transform (ASIFT) [16], Speed Up Robust Features (SURF) [17], Harris Corner Detector (HARRIS) [18], etc.”
Dictionary Learning based Supervised Discrete Hashing for Cross-Media Retrieval
Wu, Y. L., Luo, X., Xu, X.-S., et al. (2018) · ACM International Conference on Multimedia Retrieval (ICMR) · doi:10.1145/3206025.3206045 · Cited by 12
Liu, S., Wang, H., Huang, J., et al. (2015) · International Journal of Coal Science & Technology · doi:10.1007/s40789-015-0087-9 · Cited by 12
Towards Improved Air Quality Monitoring Using Publicly Available Sky Images
Spyromitros-Xioufis, E., Moumtzidou, A., Papadopoulos, S., et al. (2018) · Springer eBooks · doi:10.1007/978-3-319-76445-0_5 · Cited by 12
A Portable Odor-Tracing Instrument
Wei, Y.-T., Meng, Q., Jing, Y., et al. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2508339 · Cited by 12
Zhou, H., Chang, C., Xia, Z., et al. (2025) · Sensors · doi:10.3390/s25123726 · Cited by 12
Multi-layer temporal graphical model for head pose estimation in real-world videos
Demirkus, M., Precup, D., Clark, J. J., et al. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025686 · Cited by 12
Deep probability multi-view feature learning for data clustering
Zhao, L., Wang, X., Liu, Z., et al. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119458 · Cited by 12
Padalkar, M. G., & Joshi, M. V. (2015) · Machine Vision and Applications · doi:10.1007/s00138-015-0661-6 · Cited by 12
Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning ApproachPreprint
Shin, D., He, S., Lee, G. M., et al. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2830377 · Cited by 12
Robust uncalibrated stereo rectification with constrained geometric distortions (USR-CGD)
Ko, H., Shim, H. S., Choi, O., et al. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.001 · Cited by 12
6DoF Object Pose Estimation via Differentiable Proxy Voting Regularizer
Yu, X. F., Zhuang, Z., Koniusz, P., et al. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.81 · Cited by 12
Selective, Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
Ilic, F., Zhao, H., Pock, T., & Wildes, R. P. (2024) · 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/cvpr52733.2024.01772 · Cited by 12
“[12, 39]) to learning-based convolutional [23] or transformers [14].”
BackgroundGood match exploration for infrared face recognition
Yang, C., Zhou, H., Sun, S., et al. (2014) · Infrared Physics & Technology · doi:10.1016/j.infrared.2014.07.011 · Cited by 12
Learning to Guide Local Feature Matches
Darmon, F., Aubry, M., & Monasse, P. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00123 · Cited by 12
WildFusion: Individual Animal Identification with Calibrated Similarity Fusion
Čermák, V., Picek, L., Adam, L., Neumann, L., & Matas, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92387-6_2 · Cited by 16
Grayscale-Invariant Reversible Data Hiding Based on Multiple Histograms Modification
Jhong, C.-L., & Wu, H.-L. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3164013 · Cited by 12
Appearance-based methods in re-identification: A brief review
Saghafi, M. A., Hussain, A., Saad, M. H. M., et al. (2012) · IEEE International Colloquium on Signal Processing and its Applications (CSPA) · doi:10.1109/cspa.2012.6194758 · Cited by 12
Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning
Kapse, S., Das, S., Zhang, J., Gupta, R., Saltz, J. H., Samaras, D., & Prasanna, P. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103070 · Cited by 14
Multi-View Stereo Reconstruction with High Dynamic Range Texture
Lu, F. H., Ji, X., Dai, Q., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19309-5_32 · Cited by 12
Combined feature based and shape based visual tracker for robot navigation
Deans, M., Kunz, C., Sargent, R., et al. (2005) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2005.1559326 · Cited by 12
Learning representative temporal features for action recognition
Javidani, A., & Mahmoudi-Aznaveh, A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11022-8 · Cited by 13
Hörster, E., Slaney, M., Ranzato, M., et al. (2009) · doi:10.1145/1631058.1631074 · Cited by 12
Affine non-local Bayesian image denoising algorithm
Xu, H., Jia, X., Cheng, L., & Huang, H. (2022) · The Visual Computer · doi:10.1007/s00371-021-02316-x · Cited by 12
EREL-Net: A Remedy for Industrial Bottle Defect Detection
Patel, N., Mukherjee, S., & Ying, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04375-9_39 · Cited by 12
The Functional Correspondence Problem
Lai, Z., Purushwalkam, S., & Gupta, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01548 · Cited by 13
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
