The DOI matches its Crossref record; 44 of the 50 references it cites were found in a registry or an index.
Distinctive Image Features from Scale-Invariant Keypoints
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Moreover, the efficiency of HF-net2 was tested and compared with several wellknown handcrafted algorithms for keypoint detection and description including Harris [4], FAST [6], SIFT [9] and ORB [8].”
From Keypoint Detection and Description through Deep Learning in Unstructured Environments (2023) · cites this paper as [9]
In the citing paper
“By including a background removal step sim-330 ilar to the WGE in [9], which makes use of a Prewitt operator in combination with a gradient filter, the authors could also demonstrate the robustness of their IP with respect to the Earth in the background.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Feature fusion was then used to synthesise the detected points into polylines which resemble parts of the spacecraft body.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Alternatively, Capuano et al. [32] introduced a new IP scheme in which three different parallel processing streams, which use the Shi-Tommasi (ST) corners detector, the HT, and the Line Segment Detector (LSD), are exploited in order to filter three sets of points and improve the robustness of the feature detection.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Furthermore, the scenarios with the Earth horizon were tackled by tuning the threshold of gradient filter to a more selective value.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Robustness proven”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Although pyramid decomposition has been widely used in image compression [8], computer vision [9], etc., its use in image hashing, as proposed in this paper, has proved to be every effective.”
From A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids (2019) · cites this paper as [9]
In the citing paper
“The latter, introduced by Lowe [2], have been shown to outperform the others [7].”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“This can be explained by the fact that they capture a substantial amount of information about the spatial intensity patterns, while at the same time being robust to small deformations or localisation errors.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under partial occlusion and view point changes.”
From 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch (2016) · cites this paper as Lowe 2004
In the citing paper
“Traditional methods may require complex feature matching algorithms (e.g., SIFT, SURF) or additional classifiers (e.g., SVM), whereas the MobileNetV2 network model directly utilizes features extracted by the deep learning model to perform comparison, which simplifies the process [27,28].”
From Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology (2025) · cites this paper as [27]
Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Self-citationA Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Generalizing the Hough transform to detect arbitrary shapes
Ballard, D. H. (1981) · Pattern Recognition · doi:10.1016/0031-3203(81)90009-1 · Cited by 4,434
Robust wide-baseline stereo from maximally stable extremal regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,760
An Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802
Koenderink, J. J. (1984) · Biological Cybernetics · doi:10.1007/bf00336961 · Cited by 2,452
Witkin, A. (1987) · Elsevier eBooks · doi:10.1016/b978-0-08-051581-6.50036-2 · Cited by 2,360
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1998) · Journal of the ACM · doi:10.1145/293347.293348 · Cited by 2,290
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211479 · Cited by 2,030
Research works printed but not linked: 3
- Harris, C. and Stephens, M. 1988. Acombined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.
- Mikolajczyk, K. 2002. Detection of local features invariant to affine transformations, Ph.D. thesis, Institut National Polytechnique de Grenoble, France.
- Torr, P. 1995. Motion segmentation and outlier detection, Ph.D. Thesis, Dept. of Engineering Science, University of Oxford, UK.
Cited by
Sorted by citations
Items 1,901–2,000 of 56,204
Multiscale CNN Based on Component Analysis for SAR ATR
Li, Y., Du, L., & Wei, D. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3100137 · Cited by 169
Entropy and Laplacian images: Structural representations for multi-modal registration
Wachinger, C., & Navab, N. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.03.001 · Cited by 162
Brunier, G., Fleury, J., Anthony, E. J., et al. (2016) · Geomorphology · doi:10.1016/j.geomorph.2016.02.025 · Cited by 162
Convolutional neural network-based image representation for visual loop closure detection
Hou, Y., Zhang, H., & Zhou, S. (2015) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2015.7279659 · Cited by 173
Video Modeling With Correlation Networks
Wang, H., Tran, D., Torresani, L., & Feiszli, M. (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/cvpr42600.2020.00043 · Cited by 158
Deep Learning Enhanced Mobile-Phone Microscopy
Rivenson, Y., Koydemir, H. C., Wang, H., Wei, Z., Ren, Z., Günaydın, H., Zhang, Y., Göröcs, Z., Liang, K., Tseng, D. K., et al. (2018) · ACS Photonics · doi:10.1021/acsphotonics.8b00146 · Cited by 187
Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions
Rocco, I., Arandjelović, R., & Šivic, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_35 · Cited by 180
Fast realistic multi-action recognition using mined dense spatio-temporal features
Gilbert, A., Illingworth, J., & Bowden, R. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459335 · Cited by 162
Low Resolution Face Recognition Across Variations in Pose and Illumination
Mudunuri, S. P., & Biswas, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2469282 · Cited by 162
Handwritten word-spotting using hidden Markov models and universal vocabularies
Rodríguez-Serrano, J. A., & Perronnin, F. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.02.005 · Cited by 163
A CNN-Based Defect Inspection Method for Catenary Split Pins in High-Speed Railway
Zhong, J., Liu, Z., Han, Z., Han, Y., & Zhang, W. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2871353 · Cited by 160
Panchal, P., Panchal, S. S., & Shah, S. K. (2013) · International Journal of Innovative Research in Computer and Communication Engineering · Cited by 161
Large Scale Visual Geo-Localization of Images in Mountainous Terrain
Baatz, G., Saurer, O., Köser, K., & Pollefeys, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_37 · Cited by 161
Adaptive-weighting discriminative regression for multi-view classification
Yang, M., Deng, C. D., & Nie, F. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.015 · Cited by 161
COMPARING SEVERAL IMPLEMENTATIONS OF TWO RECENTLY PUBLISHED FEATURE DETECTORS
Bauer, J., Sünderhauf, N., & Protzel, P. (2007) · IFAC Proceedings Volumes · doi:10.3182/20070903-3-fr-2921.00027 · Cited by 160
Artificial intelligence surgery: how do we get to autonomous actions in surgery?
Gumbs, A. A., Frigerio, I., Spolverato, G., Croner, R., Illanes, A., Chouillard, E., & Elyan, E. (2021) · Sensors · doi:10.3390/s21165526 · Cited by 161
Crowd Density Estimation Using Texture Analysis and Learning
Wu, X., Liang, G., Lee, K. K., & Xu, Y. (2006) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2006.340379 · Cited by 163
Scale-Invariant Features for 3-D Mesh Models
Darom, T., & Keller, Y. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2183142 · Cited by 158
Observability analysis and active control for airborne SLAM
Bryson, M., & Sukkarieh, S. (2008) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2008.4517003 · Cited by 159
Towards Online Iris and Periocular Recognition Under Relaxed Imaging Constraints
Tan, C.-W., & Kumar, A. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2260165 · Cited by 161
Sparse Contextual Activation for Efficient Visual Re-Ranking
Bai, S., & Bai, X. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2514498 · Cited by 158
Overview of Terrain Relative Navigation Approaches for Precise Lunar Landing
Johnson, A. E., & Montgomery, J. F. (2008) · 2008 IEEE Aerospace Conference · doi:10.1109/aero.2008.4526302 · Cited by 169
Scalable Image Coding for Humans and Machines
Choi, H., & Bajić, I. V. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3160602 · Cited by 163
UAS‐based remote sensing of fluvial change following an extreme flood event
Tamminga, A., Eaton, B. C., & Hugenholtz, C. H. (2015) · Earth Surface Processes and Landforms · doi:10.1002/esp.3728 · Cited by 160
Learning to Zoom: A Saliency-Based Sampling Layer for Neural Networks
Recasens, A., Kellnhofer, P., Stent, S., Matusik, W., & Torralba, A. B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_4 · Cited by 158
A review on computer vision systems in monitoring of poultry: A welfare perspective
Okinda, C., Nyalala, I., Korohou, T., Okinda, C., Wang, J., Achieng, T., Wamalwa, P., Mang, T., & Shen, M. (2020) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2020.09.002 · Cited by 179
Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction
Simo‐Serra, E., & Ishikawa, 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.2016.39 · Cited by 151
Human Pose Estimation from Monocular Images: A Comprehensive Survey
Gong, W., Zhang, X., Gonzàlez, J., Sobral, A. C., Bouwmans, T., Tu, C., & Zahzah, E.-H. (2016) · Sensors · doi:10.3390/s16121966 · Cited by 159
Computer vision based target-free 3D vibration displacement measurement of structures
Shao, Y., Li, L., Li, J., An, S., & Hao, H. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.113040 · Cited by 161
Transformer with Transfer CNN for Remote-Sensing-Image Object Detection
Li, Q., Chen, Y., & Zeng, Y. (2022) · Remote Sensing · doi:10.3390/rs14040984 · Cited by 176
Coverless steganography based on image retrieval of DenseNet features and DWT sequence mapping
Liu, Q., Xiang, X., Qin, J., Tan, Y., Tan, J., & Luo, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105375 · Cited by 165
Good Practice in Large-Scale Learning for Image Classification
Akata, Z., Perronnin, F., Harchaoui, Z., & Schmid, C. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.146 · Cited by 158
Image Search with Selective Match Kernels: Aggregation Across Single and Multiple Images
Tolias, G., Avrithis, Y., & Jeǵou, H. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0810-4 · Cited by 150
Detection of motorcyclists without helmet in videos using convolutional neural network
Vishnu, C., Singh, D., Mohan, C. K., & Babu, S. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966233 · Cited by 164
Enhancing underwater image via color correction and Bi-interval contrast enhancement
Zhang, W., Dong, L., Zhang, T., & Xu, W. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.116030 · Cited by 160
From Handcrafted to Deep Features for Pedestrian Detection: A Survey
Cao, J., Pang, Y., Xie, J., Khan, F. S., & Shao, L. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3076733 · Cited by 169
Vision‐aided inertial navigation for pin‐point landing using observations of mapped landmarks
Trawny, N., Mourikis, A. I., Roumeliotis, S. I., Johnson, A., & Montgomery, J. F. (2007) · Journal of Field Robotics · doi:10.1002/rob.20189 · Cited by 158
Jeǵou, H., Douze, M., & Schmid, C. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459419 · Cited by 161
Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models
Chai, Y., Liu, H., & Xu, J. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.07.043 · Cited by 159
Markov Weight Fields for face sketch synthesis
Zhou, H., Kuang, Z., & Wong, K. K. (2012) · 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.2012.6247788 · Cited by 159
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
Wang, J., & Yagi, Y. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.914150 · Cited by 161
Recent Advance in Content-based Image Retrieval: A Literature SurveyPreprint
Zhou, W., Li, H., & Tian, Q. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06064 · Cited by 199
SCRAMSAC: Improving RANSAC's efficiency with a spatial consistency filter
Sattler, T., Leibe, B., & Kobbelt, L. P. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459459 · Cited by 160
Discriminative Mixture-of-Templates for Viewpoint Classification
Gu, C., & Ren, X. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_30 · Cited by 164
Self-supervising Fine-Grained Region Similarities for Large-Scale Image Localization
Ge, Y., Wang, H., Zhu, F., Zhao, R., & Li, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58548-8_22 · Cited by 179
A Serial Image Copy-Move Forgery Localization Scheme With Source/Target Distinguishment
Chen, B., Tan, W., Coatrieux, G., Zheng, Y., & Shi, Y.-Q. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3026868 · Cited by 159
Detecting and Aligning Faces by Image Retrieval
Shen, X., Lin, Z., Brandt, J., & Wu, Y. N. (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.2013.444 · Cited by 149
Affinity Learning with Diffusion on Tensor Product Graph
Yang, X., Prasad, L., & Latecki, L. J. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.60 · Cited by 156
VAIS: A dataset for recognizing maritime imagery in the visible and infrared spectrums
Zhang, M. M., Choi, J., Daniilidis, K., Wolf, M. T., & Kanan, C. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301291 · Cited by 167
Constructing Category Hierarchies for Visual Recognition
Marszałek, M., & Schmid, C. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88693-8_35 · Cited by 156
Phishing or Not Phishing? A Survey on the Detection of Phishing Websites
Zieni, R., Massari, L., & Calzarossa, M. C. (2023) · IEEE Access · doi:10.1109/access.2023.3247135 · Cited by 164
FIRE: Fundus Image Registration dataset
Hernandez-Matas, C., Zabulis, X., Triantafyllou, Α., et al. (2017) · Modeling and Artificial Intelligence in Ophthalmology · doi:10.35119/maio.v1i4.42 · Cited by 158
Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
Zhu, L., Huang, Z. H., Li, Z., Xie, L., & Shen, H. T. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2797248 · Cited by 158
Pranata, Y. D., Wang, K.-C., Wang, J., Idram, I., Lai, J., Liu, J., & Hsieh, I.-H. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.02.006 · Cited by 159
Tayara, H., Soo, K. G., & Chong, K. T. (2017) · IEEE Access · doi:10.1109/access.2017.2782260 · Cited by 162
Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification
Luo, Y., Tao, D., Xu, C., Xu, C., Liu, H., & Wen, Y. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2238682 · Cited by 153
A deep learning framework for matching of SAR and optical imagery
Hughes, L. H., Marcos, D., Lobry, S., Tuia, D., & Schmitt, M. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.09.012 · Cited by 162
Multi-image Matching via Fast Alternating Minimization
Zhou, X., Zhu, M., & Daniilidis, K. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.459 · Cited by 152
Vast-scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment
Sibley, G., Christopher Mei, A., Reid, I. D., & Newman, P. M. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910369268 · Cited by 157
Multiview Matrix Completion for Multilabel Image Classification
Luo, Y., Liu, T., Tao, D., & Xu, C. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2421309 · Cited by 151
Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities
Morshed, M. G., Sultana, T., Alam, A., & Lee, Y.-K. (2023) · Sensors · doi:10.3390/s23042182 · Cited by 170
Top-Down Visual Saliency via Joint CRF and Dictionary Learning
Yang, J., & Yang, M. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2547384 · Cited by 156
A deep learning approach for complex microstructure inference
Durmaz, A. R., Müller, M. J., Lei, B., Thomas, A., Britz, D., Holm, E. A., Eberl, C., Mücklich, F., & Gumbsch, P. (2021) · Nature Communications · doi:10.1038/s41467-021-26565-5 · Cited by 164
Markerless motion capture of interacting characters using multi-view image segmentation
Liu, Y., Stoll, C., Gall, J., et al. (2011) · doi:10.1109/cvpr.2011.5995424 · Cited by 157
Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges
Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2993803 · Cited by 157
Khaliq, A., Ehsan, S., Chen, Z., Milford, M. J., & McDonald-Maier, K. D. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2956352 · Cited by 157
Deep Multimodal Learning for Affective Analysis and Retrieval
Pang, L., Zhu, S., & Ngo, C. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2482228 · Cited by 153
Peng, X., Feng, J., Zhou, J. T., Lei, Y., & Yan, S. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2968848 · Cited by 162
Yang, D., Rao, G., Martinez, J. J., Veeraraghavan, A., & Rao, A. U. K. (2015) · Medical Physics · doi:10.1118/1.4934373 · Cited by 160
POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
Zheng, C., Mendieta, M., & Chen, C. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00339 · Cited by 170
Keyframe-based monocular SLAM: design, survey, and future directions
Younes, G., Asmar, D. C., Shammas, E. A., & Zelek, J. (2016) · Robotics Auton. Syst. · doi:10.1016/j.robot.2017.09.010 · Cited by 168
Hierarchical annotation of medical images
Dimitrovski, I., Kocev, D., Loškovska, S., & Džeroski, S. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.026 · Cited by 155
Image matching using local symmetry features
Hauagge, D. C., & Snavely, N. (2012) · 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.2012.6247677 · Cited by 154
2D3D-Matchnet: Learning To Match Keypoints Across 2D Image And 3D Point Cloud
Feng, M., Hu, S., Ang, M. H., & Lee, G. H. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794415 · Cited by 165
High-Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction
Huang, F. S., Huang, S., Ker, J.-W., & Chen, Y. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2162760 · Cited by 156
Automatic Chinese food identification and quantity estimation
Chen, M.-Y., Yang, Y.-H., Ho, C.-J., Wang, S.-H., Liu, S.-M., Chang, E., Yeh, C.-H., & Ouhyoung, M. (2012) · SIGGRAPH Asia Technical Briefs · doi:10.1145/2407746.2407775 · Cited by 159
3D LIDAR-Camera Extrinsic Calibration Using an Arbitrary Trihedron
Gong, X., Lin, Y., & Liu, J. (2013) · Sensors · doi:10.3390/s130201902 · Cited by 153
Only look once, mining distinctive landmarks from ConvNet for visual place recognition
Chen, Z., Maffra, F., Sa, I., & Chli, M. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202131 · Cited by 153
Fully affine invariant SURF for image matching
Pang, Y., Li, W., Yuan, Y., & Pan, J. S. (2012) · Neurocomputing · doi:10.1016/j.neucom.2011.12.006 · Cited by 156
Mesas‐Carrascosa, F. J., Torres‐Sánchez, J., Clavero-Rumbao, I., García-Ferrer, A., Peña, J. M., Borra‐Serrano, I., & Granados, F. L. (2015) · Remote Sensing · doi:10.3390/rs71012793 · Cited by 157
Global Localization from Monocular SLAM on a Mobile Phone
Ventura, J., Arth, C., Reitmayr, G., & Schmalstieg, D. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.27 · Cited by 152
Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking
Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1076-4 · Cited by 156
Blusseau, S., Velasco-Forero, S., Angulo, J., et al. (2026) · Image Processing On Line · doi:10.5201/ipol · Cited by 156
Latent Semantic Minimal Hashing for Image Retrieval
Lu, X., Zheng, X., & Li, X. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2627801 · Cited by 148
Chen, L., Tang, W. J., John, N. W., Wan, T. R., & Zhang, J. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.02.006 · Cited by 152
Fast Multi-View Clustering via Nonnegative and Orthogonal Factorization
Yang, B. Q., Zhang, X., Nie, F., Wang, F., Yu, W., & Wang, R. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3045631 · Cited by 167
Image saliency by isocentric curvedness and color
Valenti, R., Sebe, N., & Gevers, T. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459240 · Cited by 155
LATCH: Learned arrangements of three patch codes
Levi, G., & Hassner, T. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477723 · Cited by 164
Nonlinear Mean Shift over Riemannian Manifolds
Subbarao, R., & Meer, P. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0195-8 · Cited by 154
Nonnegative Matrix Factorization with Earth Mover's Distance Metric for Image Analysis
Sandler, R., & Lindenbaum, M. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.18 · Cited by 153
Sparse representation and learning in visual recognition: Theory and applications
Cheng, H., Liu, Z., Yang, L., & Chen, X. (2012) · Signal Processing · doi:10.1016/j.sigpro.2012.09.011 · Cited by 158
Automated Chest X-Ray Screening: Can Lung Region Symmetry Help Detect Pulmonary Abnormalities?
Santosh, K., & Antani, S. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2775636 · Cited by 155
SFNet: Learning Object-Aware Semantic Correspondence
Lee, J., Kim, D., Ponce, J., & Ham, B. (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.2019.00238 · Cited by 154
Vollgger, S. A., & Cruden, A. R. (2016) · Journal of Structural Geology · doi:10.1016/j.jsg.2016.02.012 · Cited by 154
A system for high-volume acquisition and matching of fresco fragments
Brown, B. J., Toler‐Franklin, C., Nehab, D., et al. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360683 · Cited by 156
Huang, L., Chen, C., Li, W., & Du, Q. (2016) · Remote Sensing · doi:10.3390/rs8060483 · Cited by 155
Robust FFT-Based Scale-Invariant Image Registration with Image Gradients
Tzimiropoulos, G., Argyriou, V., Zafeiriou, S. P., et al. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.107 · Cited by 155
A Set of Selected SIFT Features for 3D Facial Expression Recognition
Berretti, S., Bimbo, A. D., Pala, P., Amor, B. B., & Daoudi, M. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.1002 · Cited by 156
A comparative study of face landmarking techniques
Çeliktutan, O., Ulukaya, S., & Sankur, B. (2013) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2013-13 · Cited by 151
A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting
Esmaeili, M. M., Fatourechi, M., & Ward, R. K. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2097593 · Cited by 153
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
