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 9,701–9,800 of 56,200
Fisher Kernel for Deep Neural ActivationsPreprint
Yoo, D., Park, S., Lee, J., & Kweon, I. S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.1628 · Cited by 32
3D model search and pose estimation from single images using VIP features
Wu, C., Fraundorfer, F., Frahm, J., & Pollefeys, M. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563037 · Cited by 32
Monitoring tropical insects in the 21st century
Lamarre, G. P. A., Fayle, T. M., Segar, S. T., Laird‐Hopkins, B. C., Nakamura, A., Souto‐Vilarós, D., Watanabe, S., & Basset, Y. (2020) · Advances in ecological research/Advances in Ecological Research · doi:10.1016/bs.aecr.2020.01.004 · Cited by 37
Learning Robust Similarity Measures for 3D Partial Shape Retrieval
Liu, Y., Wang, X.-L., Wang, H., Zha, H., & Qin, H. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0298-x · Cited by 32
Local and Long-Range Collaborative Learning for Remote Sensing Scene Classification
Zhao, M., Meng, Q., Zhang, L., et al. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3265346 · Cited by 32
Multi-Oriented and Scale-Invariant License Plate Detection Based on Convolutional Neural Networks
Han, J. R., Yao, J., Zhao, J., Tu, J., & Liu, Y. (2019) · Sensors · doi:10.3390/s19051175 · Cited by 33
Actor-independent action search using spatiotemporal vocabulary with appearance hashing
Ji, R., Yao, H., & Sun, X. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.08.022 · Cited by 32
UAV applications to assess short-term dynamics of slow-moving landslides under dense forest cover
Ilinca, V., Sandric, I. C., Chiţu, Z., et al. (2022) · Landslides · doi:10.1007/s10346-022-01877-9 · Cited by 32
Unsupervised alignment of actions in video with text descriptions
Song, Y. C., Naim, I., Mamun, A. A., Kulkarni, K., Singla, P., Luo, J., Gildea, D., & Kautz, H. (2016) · International Joint Conference on Artificial Intelligence · Cited by 30
A Channel Attention-Based Method for Micro-Motor Armature Surface Defect Detection
Li, Y., Yang, M., Hua, J., Xu, Z., Wang, J., & Fang, X. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3159293 · Cited by 33
Echocardiogram view classification using edge filtered scale-invariant motion features
Kumar, R., Wang, F., Beymer, D., & Syeda-Mahmood, T. F. (2009) · 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.2009.5206838 · Cited by 32
de Ves, E., Acevedo, D. G., Ruedin, A., et al. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.03.004 · Cited by 32
Palmprint Verification using SIFT features
Badrinath, G. S., & Gupta, P. (2008) · doi:10.1109/ipta.2008.4743763 · Cited by 32
Computer Vision Applications In Construction And Asset Management Phases: A Literature Review
Jiang, Z., & Messner, J. (2023) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2023.009 · Cited by 39
A Saliency-Based Band Selection Approach for Hyperspectral Imagery Inspired by Scale Selection
Su, P., Liu, D., Li, X., et al. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2800034 · Cited by 32
Interactive Multimodal Visual Search on Mobile Device
Li, H., Wang, Y., Mei, T., Wang, J., & Li, S. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2234730 · Cited by 30
Anwar, A., Lin, W., Deng, X., Qiu, J., & Gao, H. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2881948 · Cited by 32
Model-based subspace clustering of non-Gaussian data
Boutemedjet, S., Ziou, D., & Bouguila, N. (2010) · Neurocomputing · doi:10.1016/j.neucom.2009.11.044 · Cited by 32
DERF: Distinctive Efficient Robust Features From the Biological Modeling of the P Ganglion Cells
Weng, D., Wang, Y., Gong, M., Tao, D., Wei, H., & Huang, D. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2409739 · Cited by 32
Feature-Area Optimization: A Novel SAR Image Registration Method
Liu, F., Bi, F., Chen, L., et al. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2507982 · Cited by 32
Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts
Park, S., Kim, S., Park, M., & Park, S.-K. (2009) · Information Sciences · doi:10.1016/j.ins.2009.06.030 · Cited by 32
Automatic content creation for multiview autostereoscopic displays using image domain warping
Farre, M. A., Wang, O., Lang, M., et al. (2011) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2011.6012182 · Cited by 32
Using iris and sclera for detection and classification of contact lenses
Gragnaniello, D., Poggi, G., Sansone, C., et al. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.10.009 · Cited by 32
Underwater Gesture Recognition Using Classical Computer Vision and Deep Learning Techniques
Martija, M. A. M., Dumbrique, J. I. S., & Jr Naval Prospero, C. (2020) · Journal of Image and Graphics · doi:10.18178/joig.8.1.9-14 · Cited by 33
Learning place-dependant features for long-term vision-based localisation
McManus, C., Upcroft, B., & Newman, P. M. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9463-y · Cited by 31
Duţă, I. C., Uijlings, J., Ionescu, B. E., Aizawa, K., Hauptmann, A. G., & Sebe, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4795-6 · Cited by 32
Pornographic Image Recognition via Weighted Multiple Instance Learning
Jin, X., Wang, Y., & Tan, X. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2864870 · Cited by 32
A Robust and Binarization-Free Approach for Text Line Detection in Historical Documents
Gruuening, T., Leifert, G., Strauß, T., et al. (2017) · doi:10.1109/icdar.2017.47 · Cited by 32
Hussain, L., Almaraashi, M., Aziz, W., Habib, N., & Abbasi, S.-R. S. (2021) · Waves in Random and Complex Media · doi:10.1080/17455030.2021.1905912 · Cited by 35
A Deep Clustering via Automatic Feature Embedded Learning for Human Activity Recognition
Wang, T., Ng, W. W. Y., Li, J., Wu, Q., Zhang, S., Nugent, C., & Shewell, C. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3057469 · Cited by 31
Using Self-Contradiction to Learn Confidence Measures in Stereo Vision
Mostegel, C., Rumpler, M., Fraundorfer, F., & Bischof, H. (2016) · 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.2016.441 · Cited by 31
3D Interest Point Detection via Discriminative Learning
Teran, L., & Mordohai, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_11 · Cited by 32
Xian, Y., Xiao, J., & Wang, Y. (2017) · Frontiers of Computer Science · doi:10.1007/s11704-016-6191-1 · Cited by 32
Image copy‐move forgery detection algorithm based on ORB and novel similarity metric
Tian, X., Zhou, G., & Xu, M. K. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1145 · Cited by 34
Fully convolutional networks for action recognition
Yu, S., Cheng, Y., Xie, L., & Li, S. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2017.0005 · Cited by 32
Blob Detection With Wavelet Maxima Lines
Damerval, C., & Meignen, S. (2006) · IEEE Signal Processing Letters · doi:10.1109/lsp.2006.879830 · Cited by 31
Zeng, Q., Adu, J., Liu, J., et al. (2019) · Journal of Real-Time Image Processing · doi:10.1007/s11554-019-00858-x · Cited by 32
Baumgartner, S. E., Sumter, S. R., Petkevič, V., & Wiradhany, W. (2022) · Social Science Computer Review · doi:10.1177/08944393211071068 · Cited by 32
Joint Coding-Transmission Optimization for a Video Surveillance System With Multiple Cameras
Wang, M., Cheng, B. H., & Yuen, C. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2748459 · Cited by 30
Barnes, N. M., Loy, G., & Shaw, D. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.09.008 · Cited by 32
A practical driver fatigue detection algorithm based on eye state
Liu, A., Li, Z., Wang, L., et al. (2010) · Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics · doi:10.1109/primeasia.2010.5604919 · Cited by 32
Motayyeb, S., Samadzedegan, F., Javan, F. D., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e14551 · Cited by 33
HOW FAR DO WE GET USING MACHINE LEARNING BLACK-BOXES?
Rocha, A. D. R., Papa, J. P., & Meira, L. A. A. (2012) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001412610010 · Cited by 32
An improved reduced feature-based copy-move forgery detection technique
Kumar, S., Mukherjee, S., & Pal, A. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12391-4 · Cited by 32
Song, Y., Cai, W., Huang, H., et al. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-173 · Cited by 32
An Efficient Human Identification Through Iris Recognition System
Garg, M., Arora, A. S., & Gupta, S. (2021) · Journal of Signal Processing Systems · doi:10.1007/s11265-021-01646-2 · Cited by 32
Detection of repeated structures in facade images
Wenzel, S., Drauschke, M., & Förstner, W. (2008) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661808030073 · Cited by 32
Key frames extraction using graph modularity clustering for efficient video summarization
Gharbi, H., Bahroun, S., Massaoudi, M., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952407 · Cited by 32
Perriollat, M., & Bartoli, A. (2012) · Computer Animation and Virtual Worlds · doi:10.1002/cav.1478 · Cited by 32
Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions
Dandrifosse, S., Carlier, A., Dumont, B., & Mercatoris, B. (2021) · Remote Sensing · doi:10.3390/rs13071380 · Cited by 32
A Coarse-to-Fine Approach for Remote-Sensing Image Registration Based on a Local Method
Lee, S. R. (2010) · International Journal on Smart Sensing and Intelligent Systems · doi:10.21307/ijssis-2017-416 · Cited by 32
Understanding the 3D layout of a cluttered room from multiple images
Bao, S. Y., Furlan, A., Fei-Fei, L., & Savarese, S. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836035 · Cited by 31
Automatic Co-registration of Satellite Time Series via Least Squares Adjustment
Barazzetti, L., Scaioni, M., & Gianinetto, M. (2014) · European Journal of Remote Sensing · doi:10.5721/eujrs20144705 · Cited by 31
Fully automatic vertebra detection in x-ray images based on multi-class SVM
Lecron, F., Benjelloun, M., & Mahmoudi, S. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911424 · Cited by 32
Large-margin determinantal point processes
Chao, W., Gong, B., Grauman, K., et al. (2015) · Uncertainty in Artificial Intelligence · Cited by 32
Unsupervised learning of hierarchical spatial structures in images
Parikh, D., Zitnick, C. L., & Chen, T. (2009) · 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.2009.5206549 · Cited by 32
A Bag of Features Approach for 3D Shape Retrieval
Keuper, J., Streicher, A., & Burkhardt, H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10331-5_4 · Cited by 32
An Effective Method Based on ACF for Aircraft Detection in Remote Sensing Images
Zhao, A., Fu, K., Sun, H., Sun, X., Li, F., Zhang, D., & Wang, H.-Q. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2677954 · Cited by 32
M-SIFT: A new method for Vehicle Logo Recognition
Psyllos, A., Anagnostopoulos, C., & Kayafas, E. (2012) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves.2012.6294277 · Cited by 32
SIFT-CCH: Increasing the SIFT distinctness by Color Co-occurrence Histograms
Ancuți, C., & Bekaert, P. (2007) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa.2007.4383677 · Cited by 31
Vehicles detection in Traffic Flow
Mo, G., & Zhang, S. (2010) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2010.5583178 · Cited by 32
Mining Event Structures from Web Videos
Wu, X. Y., Lu, Y., Peng, Q., et al. (2011) · IEEE Multimedia · doi:10.1109/mmul.2011.12 · Cited by 32
Hazzat, S. E., Merras, M., Akkad, N. E., Saaidi, A., & Satori, K. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6828-1 · Cited by 32
Araujo, R. F., Chambers, J. Q., Celes, C. H. S., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243079 · Cited by 32
Bridging the Gap Between Visual Servoing and Visual SLAM: A Novel Integrated Interactive Framework
Li, C., Zhang, X., Gao, H., Wang, R., & Fang, Y. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3067792 · Cited by 33
Probabilistic simultaneous pose and non-rigid shape recovery
Moreno-Noguer, F., & Porta, J. M. (2011) · doi:10.1109/cvpr.2011.5995532 · Cited by 32
Visual Motion Estimation and Terrain Modeling for Planetary Rovers
Se, S., Barfoot, T. D., & Jasiobedzki, P. (2005) · Cited by 32
Tie, Y., & Guan, L. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.11.026 · Cited by 32
Context based image analysis with application in dietary assessment and evaluation
Wang, Y., He, Y., Boushey, C. J., Zhu, F. M., & Delp, E. J. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5346-x · Cited by 31
3D geological modelling using laser and hyperspectral data
Nieto, J. I., Monteiro, S. T., & Viejo, D. (2010) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2010.5651553 · Cited by 32
Multi-modal brain fingerprinting: A manifold approximation based framework
Kumar, K., Toews, M., Chauvin, L., Olivier Colliot, A., & Desrosiers, C. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.006 · Cited by 35
Quasi-dense 3D reconstruction using tensor-based multiview stereo
Wu, T.-P., Yeung, S.-K., Jia, J., & Tang, C. (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.2010.5539796 · Cited by 32
Anand, V., Hashmi, M. F., & Keskar, A. G. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05476-6_54 · Cited by 32
Comparing pedestrians’ gaze behavior in desktop and in real environments
Dong, W., Liao, H., Liu, B., Zhan, Z., Liu, H., Meng, L., & Liu, Y. (2020) · Cartography and Geographic Information Science · doi:10.1080/15230406.2020.1762513 · Cited by 37
Desai, A. P., Peña‐Castillo, L., & Meruvia-Pastor, O. (2017) · Conference on Computer and Robot Vision (CRV) · doi:10.1109/crv.2017.16 · Cited by 32
Supervised super-vector encoding for facial expression recognition
Tariq, U., Yang, J., & Huang, T. S. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.05.011 · Cited by 32
Zagoris, K., Pratikakis, I., & Gatos, B. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2700721 · Cited by 32
An AIoT-Based Assistance System for Visually Impaired People
Li, J., Xie, L., Chen, Z., Shi, L., Chen, R., Ren, Y., Wang, L., & Lu, X. (2023) · Electronics · doi:10.3390/electronics12183760 · Cited by 34
An Aerial Mixed-Reality Environment for First-Person-View Drone Flying
Kim, D.-H., Go, Y.-G., & Choi, S.-M. (2020) · Applied Sciences · doi:10.3390/app10165436 · Cited by 32
Prototype system for feature extraction, classification and study of medical images
Oliva, J. T., Lee, H., Spolaôr, N., Coy, C. S. R., & Wu, F. C. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.07.008 · Cited by 33
Registration of Multiresolution Remote Sensing Images Based on L2-Siamese Model
Fan, R., Hou, B., Liu, J., et al. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3038922 · Cited by 32
Zhou, X., Zhang, Y., Li, N., et al. (2021) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2021.3056231 · Cited by 31
Appearance Based Indexing for Relocalisation in Real-Time Visual SLAM
Chekhlov, D., Mayol-Cuevas, W. W., & Calway, A. D. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.37 · Cited by 32
Novel Integration of Frame Rate Up Conversion and HEVC Coding Based on Rate-Distortion Optimization
Lu, G. S., Zhang, X., Chen, L., et al. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2767782 · Cited by 32
Extreme Rotation Estimation using Dense Correlation Volumes
Cai, R., Hariharan, B., Snavely, N., & Averbuch‐Elor, H. (2021) · 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/cvpr46437.2021.01433 · Cited by 33
Valassakis, E., Papagiannis, G., Di Palo, N., & Johns, E. (2022) · 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981982 · Cited by 32
Zhao, H., Liu, F., Zhang, H., & Liang, Z. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1615652 · Cited by 36
Krijgsman, D., van Leeuwen, M. B., van der Ven, J., Almeida, V., Vlutters, R., Halter, D., Kuppen, P. J. K., van de Velde, C. J. �., & Wimberger-Friedl, R. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3003475 · Cited by 35
Binary Online Learned Descriptors
Balntas, V., Tang, L., & Mikolajczyk, K. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2679193 · Cited by 32
Shared Gaussian Process Latent Variables Models
Ek, C. H. (2009) · Cited by 32
Spatio-temporal action localization and detection for human action recognition in big dataset
Megrhi, S., Jmal, M., Mseddi, W. S., & Beghdadi, A. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.10.016 · Cited by 32
Hussin, N. A. C., Jamil, N., Nordin, S., et al. (2013) · IEEE Conference on Open Systems · doi:10.1109/icos.2013.6735079 · Cited by 32
Local image representations using pruned salient points with applications to CBIR
Zhang, H., Rahmani, R., Cholleti, S. R., & Goldman, S. A. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180709 · Cited by 32
Robust Semantic Template Matching Using a Superpixel Region Binary Descriptor
Yang, H. S., Huang, C., Wang, F., Song, K., & Yin, Z. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2893743 · Cited by 32
Yuan, J., Guo, Z., Lv, L., et al. (2007) · Cited by 32
Adaptive robotic tool use under variable grasps
Hoffmann, H., Chen, Z., Earl, D., Mitchell, D., Salemi, B., & Sinapov, J. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.02.001 · Cited by 33
TOWARDS ROBUST HUMAN MILLIMETER WAVE IMAGING INSPECTION SYSTEM IN REAL TIME WITH DEEP LEARNING
Liu, C., Yang, M., & Sun, X. (2018) · Electromagnetic waves · doi:10.2528/pier18012601 · Cited by 33
3D Object Recognition Using Hyper-Graphs and Ranked Local Invariant Features
Xia, S., & Hancock, E. R. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89689-0_16 · Cited by 30
Defect Detection from UAV Images Based on Region-Based CNNs
Lan, M., Zhang, Y., Zhang, L., et al. (2018) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2018.00063 · Cited by 33
Emergence of a geometric pattern of cell fates from tissue-scale mechanics in the Drosophila eye
Gallagher, K. D., Mani, M., & Carthew, R. W. (2022) · eLife · doi:10.7554/elife.72806 · Cited by 30
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
