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 5,601–5,700 of 56,204
Convolutional Hough Matching Networks
Min, J., & Cho, M. (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/cvpr46437.2021.00296 · Cited by 61
ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data
Patel, L., Kraft, P., Guestrin, C., & Zaharia, M. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3654923 · Cited by 66
“…structured attributes and query predicates: for each base vector, we assign a random integer in the range 1 − 12 to represent structured attributes; and for each query vector, the associated query predicate performs an exact match with a randomly chosen integer in the attribute value domain.”
Weakly Supervised Discriminative Localization and Classification: A Joint Learning Process
Nguyen, M. H., Torresani, L., la Torre, F. D., & Rother, C. (2009) · Defense Technical Information Center (DTIC) · doi:10.21236/ada507101 · Cited by 62
Learning to Compose Hypercolumns for Visual Correspondence
Min, J., Lee, J., Ponce, J., & Cho, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_21 · Cited by 61
Paul, S., & Pati, U. C. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2918211 · Cited by 59
Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review
Galvao, L. G., Abbod, M., Kalganova, T., Palade, V., & Huda, M. N. (2021) · Sensors · doi:10.3390/s21217267 · Cited by 59
Shape from Contour: Computation and Representation
Elder, J. H. (2018) · Annual Review of Vision Science · doi:10.1146/annurev-vision-091517-034110 · Cited by 60
A Robust Point-Matching Algorithm for Remote Sensing Image Registration
Zhang, K., Li, X., & Zhang, J. (2013) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2013.2267771 · Cited by 59
Guided Image Contrast Enhancement Based on Retrieved Images in Cloud
Wang, S., Gu, K., Ma, S., et al. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2510326 · Cited by 59
A Novel Blending Technique for Underwater Gigamosaicing
Prados, R., García, R., Gracias, N., Escartin, J., & Neumann, L. (2012) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2012.2204152 · Cited by 58
An evaluation of bags-of-words and spatio-temporal shapes for action recognition
de Campos, T. E., Barnard, M., Mikolajczyk, K., et al. (2011) · Proceedings · doi:10.1109/wacv.2011.5711524 · Cited by 59
Pandya, R., Valzania, L., Dorchies, F., Xia, F., Hugh, J. M., Mathieson, A., Tan, H. J., Parton, T. G., Godeffroy, L., Mazloomian, K., et al. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01466-4 · Cited by 71
Guided Feature Selection for Deep Visual Odometry
Xue, F., Wang, Q., Wang, X., Dong, W., Wang, J., & Zha, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20876-9_19 · Cited by 58
Descriptor Learning Using Convex Optimisation
Simonyan, K., Vedaldi, A., & Zisserman, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_18 · Cited by 60
RGB-D object pose estimation in unstructured environments
Choi, C., & Christensen, H. I. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2015.09.020 · Cited by 58
Emotion recognition from facial expressions using hybrid feature descriptors
Kalsum, T., Anwar, S. M., Majid, M., Khan, B., & Ali, S. M. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2017.0499 · Cited by 57
A Video based Vehicle Detection, Counting and Classification System
Memon, S., Bhatti, S., Thebo, L. A., Talpur, M. M. B., & Memon, M. A. (2018) · International Journal of Image Graphics and Signal Processing · doi:10.5815/ijigsp.2018.09.05 · Cited by 60
A One-Year Introductory Robotics Curriculum for Computer Science Upperclassmen
Correll, N., Wing, R. M., & Coleman, D. (2012) · IEEE Transactions on Education · doi:10.1109/te.2012.2220774 · Cited by 58
Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting
Khaki, S., Pham, H., Han, Y., et al. (2020) · Iowa State University Digital Repository (Iowa State University) · Cited by 59
Gilliot, J.-M., Michelin, J., Hadjard, D., et al. (2020) · Precision Agriculture · doi:10.1007/s11119-020-09764-w · Cited by 59
Visual Aware Hierarchy Based Food Recognition
Mao, R., He, J., Shao, Z., Yarlagadda, S. K., & Zhu, F. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68821-9_47 · Cited by 73
A 125 GOPS 583 mW Network-on-Chip Based Parallel Processor With Bio-Inspired Visual Attention Engine
Kim, K., Lee, S., Kim, J.-Y., et al. (2009) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2008.2007157 · Cited by 59
Quasi-Dense Wide Baseline Matching Using Match Propagation
Kannala, J., & Brandt, S. S. (2007) · 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.2007.383247 · Cited by 57
Daliri, M. R. (2011) · Journal of Medical Systems · doi:10.1007/s10916-011-9738-6 · Cited by 59
View and Time Interpolation in Image Space
Stich, T., Linz, C., Albuquerque, G., et al. (2008) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2008.01323.x · Cited by 59
Layered Graph Matching with Composite Cluster Sampling
Lin, L., Liu, X., & Zhu, S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.150 · Cited by 59
Bag of Words and Local Spectral Descriptor for 3D Partial Shape Retrieval
Lavoué, G. (2011) · HAL · doi:10.2312/3dor/3dor11/041-048 · Cited by 59
Key.Net: Keypoint Detection by Handcrafted and Learned CNN FiltersPreprint
Barroso-Laguna, A., Riba, E., Ponsa, D., & Mikolajczyk, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.00889 · Cited by 76
Landing a UAV on a runway using image registration
Miller, A., Shah, M. A., & Harper, D. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543206 · Cited by 59
Image-based modeling of built environment from an unmanned aerial system
Rodríguez‐Gonzálvez, P., González‐Aguilera, D., Lopez-Jimenez, G., et al. (2014) · Automation in Construction · doi:10.1016/j.autcon.2014.08.010 · Cited by 59
Lightweight smart glass system with audio aid for visually impaired people
Feng, L., Zhai, G., & Lin, W. (2015) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2015.7372720 · Cited by 59
Regularized Max Pooling for Image Categorization
Hoai, M. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.32 · Cited by 58
Image features for visual teach-and-repeat navigation in changing environments
Krajník, T., Cristóforis, P. D., Kusumam, K., Neubert, P., & Duckett, T. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.11.011 · Cited by 56
A new pedestrian detection method based on combined HOG and LSS features
Yao, S., Pan, S., Wang, T., Zheng, C.-H., Shen, W., & Chong, Y. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.08.080 · Cited by 59
A Massively Parallel Deep Rule-Based Ensemble Classifier for Remote Sensing Scenes
Gu, X., Angelov, P. P., Zhang, C., et al. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2787421 · Cited by 59
Sanjana, S., Sanjana, S., Shriya, V. R., Vaishnavi, G. K., & Kodipalli, A. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00493-7 · Cited by 59
View Confusion Feature Learning for Person Re-Identification
Liu, F., & Zhang, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00674 · Cited by 65
A Sim-to-Real Object Recognition and Localization Framework for Industrial Robotic Bin Picking
Li, X., Cao, R., Feng, Y., Chen, K., Yang, B., Fu, C., Li, Y., Dou, Q., Liu, Y., & Heng, P. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3149026 · Cited by 59
Three dimensional objects recognition & pattern recognition technique; related challenges: A review
Rani, S., Lakhwani, K., & Kumar, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12412-2 · Cited by 62
Unsupervised Adversarial Instance-Level Image Retrieval
Bai, C., Li, H., Zhang, J., Huang, L., & Zhang, L. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3065578 · Cited by 59
Joint Optical Flow and Temporally Consistent Semantic Segmentation
Hur, J., & Roth, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46604-0_12 · Cited by 64
Human skeletons and change detection for efficient violence detection in surveillance videos
Garcia-Cobo, G., & SanMiguel, J. C. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103739 · Cited by 60
Ramsden, H. L., Sürmeli, G., McDonagh, S., & Nolan, M. F. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004032 · Cited by 63
Feature extraction for image recognition and computer vision
Jiang, X. (2009) · IEEE International Conference on Computer Science and Information Technology · doi:10.1109/iccsit.2009.5235014 · Cited by 58
Estimating 3-D Point-of-Regard in a Real Environment Using a Head-Mounted Eye-Tracking System
Takemura, K., Takahashi, K., Takamatsu, J., & Ogasawara, T. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2318324 · Cited by 56
Context-Dependent Kernels for Object Classification
Sahbi, H., Audibert, J.-Y., & Keriven, R. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.198 · Cited by 58
Peng, S., Kim, D., Lee, S., & Lim, M. (2010) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2010.10.005 · Cited by 58
In‐water visual ship hull inspection using a hover‐capable underwater vehicle with stereo vision
Hong, S., Chung, D., Kim, J., Kim, Y., Kim, A., & Yoon, H. K. (2018) · Journal of Field Robotics · doi:10.1002/rob.21841 · Cited by 57
Predicting Actions from Static Scenes
Vu, T.-H., Olsson, C., Laptev, I., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_28 · Cited by 58
Video Stabilization for Strict Real-Time Applications
Dong, J., & Liu, H. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2589860 · Cited by 58
Image Classification by Selective Regularized Subspace Learning
Luo, C., Ni, B., Yan, S., & Wang, M. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2495248 · Cited by 59
Accurate Human Navigation Using Wearable Monocular Visual and Inertial Sensors
Tian, Y., Hamel, W. R., & Tan, J. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2277514 · Cited by 58
VIDEO ANALYSIS USING CORNER MOTION STATISTICS
Albiol, A., Silla, M. J., Albiol, A., & Mossi, J. M. (2010) · Cited by 78
Object Recognition Using Deep Convolutional Features Transformed by a Recursive Network Structure
Bui, H. M., Lech, M., Cheng, E., Neville, K. L., & Burnett, L. (2016) · IEEE Access · doi:10.1109/access.2016.2639543 · Cited by 62
A privacy-preserving content-based image retrieval method based on deep learning in cloud computing
Ma, W., Zhou, T., Qin, J., et al. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117508 · Cited by 58
SYM-FISH: A Symmetry-Aware Flip Invariant Sketch Histogram Shape Descriptor
Cao, X., Zhang, H., Liu, S., et al. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.46 · Cited by 58
Pill-ID: Matching and retrieval of drug pill images
Lee, Y., Park, U., Jain, A. K., et al. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.08.022 · Cited by 58
Context-Aware Embeddings for Automatic Art Analysis
García, N., Renoust, B., & Nakashima, Y. (2019) · International Conference on Multimedia Retrieval · doi:10.1145/3323873.3325028 · Cited by 60
Object level deep feature pooling for compact image representation
Mopuri, K. R., & Babu, R. V. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301273 · Cited by 62
Javadnejad, F., Gillins, D. T., Parrish, C. E., & Slocum, R. K. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1641241 · Cited by 58
Multilayered Image Processing for Multiscale Harris Corner Detection in Digital Realization
Hsiao, P., Lu, C.-L., & Fu, L. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2010.2040556 · Cited by 58
Scene Classification Based on the Sparse Homogeneous–Heterogeneous Topic Feature Model
Zhu, Q., Zhong, Y., Wu, S., et al. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2781712 · Cited by 58
A Review on Progress in Semantic Image Segmentation and Its Application to Medical Images
Kar, M. K., Nath, M. K., & Neog, D. R. (2021) · SN Computer Science · doi:10.1007/s42979-021-00784-5 · Cited by 65
A Local Basis Representation for Estimating Human Pose from Cluttered Images
Agarwal, A., & Triggs, B. (2006) · Lecture notes in computer science · doi:10.1007/11612032_6 · Cited by 60
A Joint-Training Two-Stage Method For Remote Sensing Image Captioning
Ye, X., Wang, S., Gu, Y., Wang, J., Wang, R., Hou, B., Giunchiglia, F., & Jiao, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3224244 · Cited by 71
Hybrid Image-Retrieval Method for Image-Splicing Validation
Pham, N. T., Lee, J. W., Kwon, G., & Park, C. (2019) · Symmetry · doi:10.3390/sym11010083 · Cited by 60
HW/SW Codesign and FPGA Acceleration of Visual Odometry Algorithms for Rover Navigation on Mars
Lentaris, G., Stamoulias, I., Soudris, D., & Lourakis, M. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2452781 · Cited by 58
Jiang, L., Xu, C., Wang, X., Luo, B., & Wang, H. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2751476 · Cited by 58
Panorama Mosaic Optimization for Mobile Camera Systems
Ha, S., Koo, H. I., Lee, S., et al. (2007) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2007.4429204 · Cited by 58
Partial-Duplicate Image Retrieval via Saliency-Guided Visual Matching
Li, L., Jiang, S., Zha, Z.-J., et al. (2013) · IEEE Multimedia · doi:10.1109/mmul.2013.15 · Cited by 58
Reconstruction of volumetric ultrasound panorama based on improved 3D SIFT
Ni, D., Chui, Y. P., Qu, Y., et al. (2009) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2009.05.006 · Cited by 58
Visual SLAM with an Omnidirectional Camera
Rituerto, A., Puig, L., & Guerrero, J. J. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.94 · Cited by 56
Cell clusters adopt a collective amoeboid mode of migration in confined nonadhesive environments
Pagès, D.-L., Dornier, E., Seze, J. D., Gontran, E., Maitra, A., Maciejewski, A., Wang, L., Luan, R., Cartry, J., Canet-Jourdan, C., et al. (2022) · Science Advances · doi:10.1126/sciadv.abp8416 · Cited by 70
Global Correlation Descriptor: A novel image representation for image retrieval
Feng, L., Wu, J., Liu, S., & Zhang, H. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.09.002 · Cited by 58
Motion Correction in Optical Resolution Photoacoustic Microscopy
Zhao, H., Chen, N., Li, T., Zhang, J., Lin, R., Gong, X., Song, L., Liu, Z., & Liu, C. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2893021 · Cited by 57
Review of Geometric and Radiometric Analyses of Paintings
Fabio Remondino, A., Rizzi, A., Barazzetti, L., Scaioni, M., Fassi, F., Brumana, R., & Pelagotti, A. (2011) · The Photogrammetric Record · doi:10.1111/j.1477-9730.2011.00664.x · Cited by 58
Autonomous Landing of MAVs on an Arbitrarily Textured Landing Site Using Onboard Monocular Vision
Yang, S., Scherer, S., Schauwecker, K., & Zell, A. (2013) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-013-9906-7 · Cited by 58
Location coding for mobile image retrieval
Tsai, S. S., Chen, D. M., Gabriel Takacs, A., Chandrasekhar, V., Singh, J., & Girod, B. (2009) · EAI International Conference on Mobile Multimedia Communications (MobiMedia) · doi:10.4108/icst.mobimedia2009.7406 · Cited by 57
Contextual Query Expansion for Image Retrieval
Xie, H., Zhang, Y., Tan, J., Guo, L., & Li, J. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2305909 · Cited by 57
Binary Codes Embedding for Fast Image Tagging with Incomplete Labels
Wang, Q., Shen, B., Wang, S., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_28 · Cited by 58
Louault, K., Porras, T. B., Lee, M.-H., et al. (2022) · OncoImmunology · doi:10.1080/2162402x.2022.2146860 · Cited by 58
Dynamic texture recognition based on completed volume local binary pattern
Tiwari, D., & Tyagi, V. (2015) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-015-0319-6 · Cited by 58
Saliency guided local and global descriptors for effective action recognition
Abdulmunem, A. A., Lai, Y., & Sun, X. (2016) · Computational Visual Media · doi:10.1007/s41095-016-0033-9 · Cited by 58
Face recognition based on the fusion of global and local HOG features of face images
Tan, H., Yang, B., & Ma, Z. (2013) · IET Computer Vision · doi:10.1049/iet-cvi.2012.0302 · Cited by 58
A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words
Mehmood, Z., Anwar, S. M., Ali, N., Habib, H. A., & Rashid, M. (2016) · Mathematical Problems in Engineering · doi:10.1155/2016/8217250 · Cited by 58
An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images
Wu, J., Xiao, P., Huang, H., et al. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3184930 · Cited by 58
A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification
Zheng, Y., Cao, Y., & Chang, C.-H. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2926777 · Cited by 60
Sparse Matching of Salient Facial Curves for Recognition of 3-D Faces With Missing Parts
Berretti, S., Bimbo, A. D., & Pala, P. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2235833 · Cited by 58
MapAnything: Universal Feed-Forward Metric 3D Reconstruction; map-anything.github.io
Keetha, N., Müller, N., Schönberger, J. L., Porzi, L., Zhang, Y., Fischer, T., Knapitsch, A., Zauss, D., Weber, E., Antunes, N., et al. (2026) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv69130.2026.00054 · Cited by 95
“To make the problem tractable, classic approaches de-compose it into distinct tasks, such as feature detection [34] and matching [48], two-view pose estimation [40], camera calibration [63] and resectioning [49], rotation [14] and translation averaging [42], bundle adjustment (BA) [59], multi-view…”
Dictionary-free categorization of very similar objects via stacked evidence trees
Martínez-Muñoz, G., Larios, N., Mortensen, E. N., et al. (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.2009.5206574 · Cited by 58
From omnidirectional images to hierarchical localization
Murillo, A. C., Sagüés, C., Guerrero, J. J., Goedemé, T., Tuytelaars, T., & Gool, L. V. (2006) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2006.12.004 · Cited by 57
Discriminative generalized hough transform for object dectection
Okada, R. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459441 · Cited by 57
Geometric Optimum Experimental Design for Collaborative Image Retrieval
Zhang, L., Wang, L., Lin, W., et al. (2013) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2013.2276172 · Cited by 58
Deep Convolutional Neural Network Based Object Detector for X-Ray Baggage Security Imagery
Liu, J., Leng, X., & Liu, Y. (2019) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2019.00262 · Cited by 58
Finding perfect rendezvous on the go
Liu, H., Mei, T., Luo, J., et al. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2393357 · Cited by 58
Query adaptative locality sensitive hashing
Jeǵou, H., Amsaleg, L., Schmid, C., & Gros, P. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4517737 · Cited by 58
Image scene geometry recognition using low-level features fusion at multi-layer deep CNN
Khan, A. H., Chefranov, A. G., & Demirel, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.085 · Cited by 59
de Alcantara, M. F., Cao, Y., Liu, C., Liu, B., Brunette, M., Zhang, N., Sun, T., Zhang, P., Chen, Q., Li, Y., et al. (2017) · Smart Health · doi:10.1016/j.smhl.2017.04.003 · Cited by 62
Locally Adaptive Color Correction for Underwater Image Dehazing and Matching
Ancuti, C. O., Ancuți, C., Vleeschouwer, C. D., & García, R. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.136 · Cited by 60
Multitask painting categorization by deep multibranch neural network
Bianco, S., Mazzini, D., Napoletano, P., & Schettini, R. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.036 · Cited by 61
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
