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 10,001–10,100 of 56,200
Object-based incremental registration of terrestrial point clouds in an urban environment
Ge, X., & Hu, H. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.01.020 · Cited by 31
3D navigation based on a visual memory
Remazeilles, A., Chaumette, F., & Gros, P. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2006.1642112 · Cited by 31
Machine Learning and Image Processing Methods for Cetacean Photo Identification: A Systematic Review
Maglietta, R., Carlucci, R., Fanizza, C., & Dimauro, G. (2022) · IEEE Access · doi:10.1109/access.2022.3195218 · Cited by 31
A Simple and Efficient Image Stabilization Method for Coastal Monitoring Video Systems
Padilla, I. R., Castelle, B., Marieu, V., et al. (2019) · Remote Sensing · doi:10.3390/rs12010070 · Cited by 31
Kalia, R., Lee, K., Samir, B., Je, S.-K., & Oh, W.-G. (2011) · Korea-Japan Joint Workshop on Frontiers of Computer Vision · doi:10.1109/fcv.2011.5739756 · Cited by 31
An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks
Wang, R., Xiao, X., Guo, B., Qin, Q., & Chen, R. (2018) · Sensors · doi:10.3390/s18071985 · Cited by 31
DLANet: A manifold-learning-based discriminative feature learning network for scene classification
Feng, Z., Jin, L., Tao, D., & Huang, S. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.01.043 · Cited by 29
Deep and Structured Robust Information Theoretic Learning for Image Analysis
Deng, Y. C., Bao, F., Deng, X., Wang, R., Kong, Y., & Dai, Q. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2588330 · Cited by 30
Detecting individuals' spatial familiarity with urban environments using eye movement data
Liao, H., Zhao, W., Zhang, C., Dong, W., & Huang, H. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101758 · Cited by 31
Umapathy, S., Krishnan, P. T., & Sangamithirai, K. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102233 · Cited by 33
Zhang, W., Shao, J., Jin, S., Luo, L., Ge, J., Peng, X., & Zhou, G. (2021) · Forests · doi:10.3390/f12030269 · Cited by 30
WaveLBP based hierarchical features for image classification
Song, T., & Li, H. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.04.020 · Cited by 31
The influence of hubness on nearest-neighbor methods in object recognition
Tomasev, N., Brehar, R., Mladenić, D., & Nedevschi, S. (2011) · 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing · doi:10.1109/iccp.2011.6047899 · Cited by 40
Secure Surfing: Privacy-Preserving Speeded-Up Robust Feature Extractor
Wang, Q., Hu, S., Wang, J., & Ren, K. (2016) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2016.84 · Cited by 33
Identification of Stable Areas in Unreferenced Laser Scans for Deformation Measurement
Wujanz, D., Krueger, D., & Neitzel, F. (2016) · The Photogrammetric Record · doi:10.1111/phor.12152 · Cited by 32
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
Ng, T., Kim, H. J., Lee, V. T., DeTone, D., Yang, T.-Y., Shen, T., Ilg, E., Balntas, V., Mikolajczyk, K., & Sweeney, C. (2022) · 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/cvpr52688.2022.01246 · Cited by 33
A passive blind forgery detection technique to identify frame duplication attack
kharat, P. K. D., & Chougule, S. R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08272-y · Cited by 31
Grid-based visual terrain classification for outdoor robots using local features
Khan, Y. N., Komma, P., Bohlmann, K., et al. (2011) · doi:10.1109/civts.2011.5949534 · Cited by 31
Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images
Kim, K., Son, K., & Palmore, G. T. R. (2015) · Scientific Reports · doi:10.1038/srep17062 · Cited by 31
Memory Based Online Learning of Deep Representations from Video Streams
Pernici, F., Bartoli, F., Bruni, M., & Bimbo, A. D. (2018) · 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.2018.00247 · Cited by 31
Large scale image copy detection evaluation
Thomée, B., Huiskes, M. J., Bakker, E. M., & Lew, M. S. (2008) · Multimedia Information Retrieval · doi:10.1145/1460096.1460108 · Cited by 31
Label diagnosis through self tuning for web image search
Wang, J., Jiang, Y., & Chang, S. (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.2009.5206729 · Cited by 31
Combining multiple kernels for efficient image classification
Siddiquie, B., Vitaladevuni, S. N. P., & Davis, L. S. (2009) · doi:10.1109/wacv.2009.5403040 · Cited by 31
Multimedia event recounting with concept based representation
Yu, Q., Liu, J., Cheng, H., Divakaran, A., & Sawhney, H. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2396386 · Cited by 29
Coherent Phrase Model for Efficient Image Near-Duplicate Retrieval
Hu, Y., Cheng, X., Chia, L.-T., Xie, X., Rajan, D., & Tan, A. (2009) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2009.2032676 · Cited by 30
Mei, T., Li, L., Hua, X., & Li, S. (2012) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2071396.2071402 · Cited by 29
Obtaining Urban Waterlogging Depths from Video Images Using Synthetic Image Data
Jiang, J., Qin, C., Yu, J., et al. (2020) · Remote Sensing · doi:10.3390/rs12061014 · Cited by 31
Moving Object Detection and Tracking in Forward Looking Infra-Red Aerial Imagery
Bhattacharya, S., Idrees, H., Saleemi, I., Ali, S., & Shah, M. A. (2011) · Springer eBooks · doi:10.1007/978-3-642-11568-4_10 · Cited by 31
Structure-from-motion for MAV image sequence analysis with photogrammetric applications
Schönberger, J. L., Fraundorfer, F., & Frahm, J. (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-3-305-2014 · Cited by 31
Rain removal via shrinkage of sparse codes and learned rain dictionary
Son, C., & Zhang, X.-P. S. (2016) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2016.7574752 · Cited by 31
Yu, D., Yang, F., Yang, C., Leng, C., Cao, J., Wang, Y., & Tian, J. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2465855 · Cited by 31
Huang, X., Li, Z., Zhang, M., & Gao, S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.994950 · Cited by 31
Identification of ancient coins based on fusion of shape and local features
Huber-Mörk, R., Zambanini, S., Zaharieva, M., et al. (2010) · Machine Vision and Applications · doi:10.1007/s00138-010-0283-y · Cited by 31
Planar Affine Rectification from Change of Scale
Chum, O., & Matas, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19282-1_28 · Cited by 31
Exploiting relationship between attributes for improved face verification
Song, F., Tan, X., & Chen, S. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2014.02.010 · Cited by 31
Automatic prediction of presentation style and student engagement from videos
Thomas, C., Sarma, K. A. V. P., Gajula, S. S., & Jayagopi, D. B. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100079 · Cited by 32
Exploiting sublimated deep features for image retrieval
Liu, G., Li, Z., Yang, J.-Y., & Zhang, D. D. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110076 · Cited by 33
EFIS—Evolving Fuzzy Image Segmentation
Othman, A. A., Tizhoosh, H. R., & Khalvati, F. (2013) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2246761 · Cited by 30
Analysis of major rock slides that occurred during the 2016–2017 Central Italy seismic sequence
Forte, G., Verrucci, L., Giulio, A. D., et al. (2021) · Engineering Geology · doi:10.1016/j.enggeo.2021.106194 · Cited by 31
Multi-scale gist feature manifold for building recognition
Zhao, C., Liu, C., & Lai, Z. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.03.035 · Cited by 31
Convolutional Fisher Kernels for RGB-D Object Recognition
Cheng, Y., Cai, R., Zhao, X., & Huang, K. (2015) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2015.23 · Cited by 33
A three-point minimal solution for panoramic stitching with lens distortion
Jin, H. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587686 · Cited by 31
Enhancing Surveillance Systems with YOLO Algorithm for Real-Time Object Detection and Tracking
Anish, A., Sharan, R., Malini, A. H., et al. (2023) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs58579.2023.10404710 · Cited by 31
Non-negative matrix factorisation for object class discovery and image auto-annotation
Tang, J., & Lewis, P. H. (2008) · doi:10.1145/1386352.1386370 · Cited by 31
LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs
Cheng, Z., Esteves, C., Jampani, V., Kar, A., Maji, S., & Makadia, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01679 · Cited by 34
Epipolar Visual Servoing for Multirobot Distributed Consensus
Montijano, E., Thunberg, J., Xiaoming Hu, A., & Sagüés, C. (2013) · IEEE Transactions on Robotics · doi:10.1109/tro.2013.2271101 · Cited by 31
Structure-guided feature and cluster contrastive learning for multi-view clustering
Shu, Z., Li, B., Mao, C., Gao, S., & Yu, Z. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127555 · Cited by 31
Regression-based methods for face alignment: A survey
Gogić, I., Ahlberg, J., & Pandzic, I. S. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107755 · Cited by 31
Deterministic Model Fitting by Local-Neighbor Preservation and Global-Residual Optimization
Xiao, G., Ma, J., Wang, S., & Chen, C. W. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3023576 · Cited by 31
The University of Amsterdam’s Concept Detection System at ImageCLEF 2009
van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15751-6_32 · Cited by 30
Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation
Manish, R., Hasheminasab, S. M., Liu, J., Koshan, Y., Mahlberg, J., Lin, Y.-C., Ravi, R., Zhou, T., McGuffey, J., Wells, T., et al. (2022) · Remote Sensing · doi:10.3390/rs14010231 · Cited by 32
Im2Sketch: Sketch generation by unconflicted perceptual grouping
Qi, Y., Guo, J. F., Song, Y.-Z., et al. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.023 · Cited by 31
Color Targets: Fiducials to Help Visually Impaired People Find Their Way by Camera Phone
Coughlan, J. M., & Manduchi, R. (2007) · EURASIP Journal on Image and Video Processing · doi:10.1155/2007/96357 · Cited by 31
Geometrized Transformer for Self-Supervised Homography Estimation
Liu, J., & Li, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00876 · Cited by 32
Variable-Length Signature for Near-Duplicate Image Matching
Liu, L., Lu, Y., & Suen, C. Y. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2400229 · Cited by 31
Explore-Exploit Graph Traversal for Image Retrieval
Chang, C., Yu, G., Liu, C., & Volkovs, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00965 · Cited by 28
Ding, J., Yan, Z., & We, X. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10040234 · Cited by 31
Li, Q., Mou, L., Hua, Y., et al. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3109844 · Cited by 31
Perceptual Loss for Robust Unsupervised Homography Estimation
Koguciuk, D., Arani, E., & Zonooz, B. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00483 · Cited by 32
Visual Place Recognition Using Landmark Distribution Descriptors
Panphattarasap, P., & Calway, A. D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54190-7_30 · Cited by 34
Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories
Fenzi, M., Leal-Taixé, L., Rosenhahn, B., et al. (2013) · 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.2013.103 · Cited by 31
Sarkhel, R., & Nandi, A. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/466 · Cited by 31
Power pylon detection and monocular depth estimation from inspection UAVs
Araar, O., Aouf, N., & Dietz, J. L. V. (2015) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-11-2014-0419 · Cited by 31
Senthilnath, J., Kalro, N. P., & Benediktsson, J. A. (2014) · Applied Mathematics and Computation · doi:10.1016/j.amc.2014.03.070 · Cited by 31
Combined feature evaluation for adaptive visual object tracking
Han, Z., Ye, Q., & Jiao, J. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.09.004 · Cited by 31
Feature Detector Using Adaptive Accelerated Segment Test
Biadgie, Y., & Sohn, K.-A. (2014) · International Conference on Information Science and Applications · doi:10.1109/icisa.2014.6847403 · Cited by 31
Realidad virtual y realidad aumentada
Guerrero, M. J. A., Manresa-Yee, C., Sansó, R. M., & Vénere, M. (2011) · doi:10.35537/10915/18399 · Cited by 31
Cullen, N. D., Verma, A. K., & Bourke, M. C. (2018) · Earth Surface Dynamics · doi:10.5194/esurf-6-1023-2018 · Cited by 31
Active Colorization for Cartoon Line Drawings
Chen, S.-Y., Zhang, J.-Q., Gao, L., et al. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3009949 · Cited by 31
Comparative study of copy-move forgery detection techniques
Elaskily, M. A., Aslan, H. K., Elshakankiry, O. A., et al. (2017) · doi:10.1109/accs-peit.2017.8303041 · Cited by 31
Haouchine, N., Roy, F., Untereiner, L., et al. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759099 · Cited by 31
Autonomous Vehicle Navigation Using Vision and Mapless Strategies: A Survey
Güzel, M. S. (2013) · Advances in Mechanical Engineering · doi:10.1155/2013/234747 · Cited by 31
A biased sampling strategy for object categorization
Yang, L., Zheng, N., Yang, J., Chen, M., & Chen, H. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459349 · Cited by 31
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Zhou, L., Zhu, S., Luo, Z.-X., Shen, T., Zhang, R., Zhen, M., Fang, T., & Quan, L. (2018) · European Conference on Computer Vision · doi:10.1007/978-3-030-01267-0_31 · Cited by 49
Real‐time geometry‐aware augmented reality in minimally invasive surgery
Chen, L. F., Tang, W. J., & John, N. W. (2017) · Healthcare Technology Letters · doi:10.1049/htl.2017.0068 · Cited by 30
Target detection in SAR images using SIFT
Agrawal, A., Mangalraj, P., & Bisherwal, M. A. (2015) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2015.7394426 · Cited by 31
PAIGE: PAirwise Image Geometry Encoding for improved efficiency in Structure-from-Motion
Schönberger, J. L., Berg, A. C., & Frahm, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298703 · Cited by 33
FLARE: Feed-Forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
Zhang, S., Wang, J., Xu, Y., Xue, N., Rupprecht, C., Zhou, X., Shen, Y., & Wetzstein, G. (2025) · 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/cvpr52734.2025.02043 · Cited by 37
“We therefore reported its optimal results.”
Wang, P., Sang, Y., Shao, L., et al. (2018) · Acta Geotechnica · doi:10.1007/s11440-018-0676-z · Cited by 31
Visual tracking via incremental self-tuning particle filtering on the affine group
Li, M., Chen, W. R., Huang, K., & Tan, T. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539815 · Cited by 31
BSV-PAGS: Blockchain-based special vehicles priority access guarantee scheme
Wang, Y., Yu, J., Yan, B., Wang, G., & Shan, Z. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.07.012 · Cited by 30
CSPS: An Adaptive Pooling Method for Image Classification
Wang, J., Wang, W., Wang, R., & Gao, W. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2544099 · Cited by 32
DietCam: Multi-view regular shape food recognition with a camera phone
Kong, F., He, H., Raynor, H. A., & Tan, J. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2014.05.012 · Cited by 31
Review of chart recognition in document images
Liu, Y., Lu, X., Qin, Y., et al. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2008467 · Cited by 31
Srivastava, D., Singh, S. S., Rajitha, B., Verma, M., Kaur, M., & Lee, H.-N. (2023) · IEEE Access · doi:10.1109/access.2023.3308911 · Cited by 41
Image registration based on SIFT features and adaptive RANSAC transform
Hossein‐Nejad, Z., & Nasri, M. (2016) · doi:10.1109/iccsp.2016.7754318 · Cited by 31
Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description
Florea, C., Ţoca, C., & Gieseke, F. C. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.69 · Cited by 30
Data Acquisition for 3D Geometric Recording: State of the Art and Recent Innovations
Georgopoulos, A., & Stathopoulou, E. K. (2017) · Quantitative methods in the humanities and social sciences · doi:10.1007/978-3-319-65370-9_1 · Cited by 30
Hu, H., Ding, Y., Zhu, Q., Wu, B., Xie, L., & Chen, M. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.03.019 · Cited by 30
Bias reduction for stereo based motion estimation with applications to large scale visual odometry
Dubbelman, G., & Groen, F. C. A. (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.2009.5206519 · Cited by 30
Fast and robust geometric correction for mosaicking UAV images with narrow overlaps
Kim, J., Taejung Kim, A., Shin, D.-S., & Kim, S. (2017) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1294779 · Cited by 30
Lin, X., Zhu, Z., Yu, X., Ji, X., Luo, T., Xi, X., Zhu, M., & Liang, Y. (2022) · Remote Sensing · doi:10.3390/rs14030621 · Cited by 33
Identifying spatial relations in images using convolutional neural networks
Haldekar, M., Ganesan, A., & Oates, T. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966308 · Cited by 37
Liu, S., Chen, R., Ye, M., Luo, J., Yang, D., & Dai, M. (2024) · Sensors · doi:10.3390/s24144666 · Cited by 42
“Classical methods involve manually extracting geometric, edge, and texture-level features using classic image classification algorithms [10–13], followed by the classification of images using classifiers [14].”
MethodVisual Information Retrieval using Java and LIRE
Lux, M., & Marques, O. (2013) · Synthesis lectures on information concepts, retrieval, and services · doi:10.2200/s00468ed1v01y201301icr025 · Cited by 32
Mining GPS traces and visual words for event classification
Yuan, J., Luo, J., Kautz, H., et al. (2008) · doi:10.1145/1460096.1460099 · Cited by 31
Violence detection in videos using interest frame extraction and 3D convolutional neural network
Mahmoodi, J., Nezamabadi–pour, H., & Abbasi‐Moghadam, D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12532-9 · Cited by 31
A novel image retrieval method based on multi-features fusion
Niu, D., Zhao, X., Lin, X., & Zhang, C. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.115911 · Cited by 30
Online adaptive hidden Markov model for multi-tracker fusion
Vojir, T., Matas, J., & Nosková, J. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.05.007 · Cited by 30
Lighting invariant urban street classification
Upcroft, B., McManus, C., Churchill, W., Maddern, W., & Newman, P. M. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907082 · Cited by 31
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
