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 13,801–13,900 of 56,200
Correlation-based retrieval for heavily changed near-duplicate videos
Liu, J., Huang, Z. H., Shen, H. T., & Cui, B. (2011) · ACM Transactions on Information Systems · doi:10.1145/2037661.2037666 · Cited by 21
Nonparametric variational learning of multivariate beta mixture models in medical applications
Manouchehri, N., Bouguila, N., & Fan, W. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22506 · Cited by 21
To go deep or wide in learning?Preprint
Pandey, G., & Dukkipati, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.5634 · Cited by 24
Global Uncertainty-based Selection of Relative Poses for Multi Camera Calibration
Bajramovic, F., & Denzler, J. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.74 · Cited by 21
Deep Hashing Using Proxy Loss on Remote Sensing Image Retrieval
Xue, S., Liu, P., Wang, Y., Zhou, Q., & Wang, Z. (2021) · Remote Sensing · doi:10.3390/rs13152924 · Cited by 22
Agarwal, R., Shekhawat, N. S., Kumar, S., Nayyar, A., & Qureshi, B. (2021) · IEEE Access · doi:10.1109/access.2021.3135536 · Cited by 21
Kang, D., & Ma, L. (2021) · IEEE Access · doi:10.1109/access.2021.3110644 · Cited by 24
Ortis, A., Farinella, G. M., D’Amico, V., et al. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806311 · Cited by 21
Routier‐Kierzkowska, A., & Kwiatkowska, D. (2008) · Functional Plant Biology · doi:10.1071/fp08047 · Cited by 22
Fast and robust feature tracking for 3D reconstruction
Cao, M., Jia, W., Lv, Z., Li, Y., Xie, W., Zheng, L., & Liu, X. (2018) · Optics & Laser Technology · doi:10.1016/j.optlastec.2018.05.036 · Cited by 21
Intelligent Matching Method for Heterogeneous Remote Sensing Images Based on Style Transfer
Zhao, J., Yang, D., Li, Y., Xiao, P., & Yang, J. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3197748 · Cited by 24
Suaib, N. M., Marhaban, M. H., Saripan, M. I., & Ahmad, S. A. (2014) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tenconspring.2014.6863025 · Cited by 21
Inositol Polyphosphate Multikinase Is a Metformin Target That Regulates Cell Migration
Tu‐Sekine, B., Padhi, A., Jin, S., Kalyan, S., Singh, K., Apperson, M., Kapania, R., Hur, S. C., Nain, A., & Kim, S. F. (2019) · The FASEB Journal · doi:10.1096/fj.201900717rr · Cited by 20
Defect Detection and 3D Reconstruction of Complex Urban Underground Pipeline Scenes for Sewer Robots
Liu, R., Shao, Z., Sun, Q., & Yu, Z. (2024) · Sensors · doi:10.3390/s24237557 · Cited by 24
Bathymetric Retrieval Selectively Using Multiangular High-Spatial-Resolution Satellite Imagery
Cao, B., Deng, R., Zhu, S., et al. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3040186 · Cited by 21
Li, Y., & Zhang, D. (2025) · Processes · doi:10.3390/pr13020293 · Cited by 22
A Unified Approach for Registration and Depth in Depth from Defocus
Ben‐Ari, R. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.14 · Cited by 21
SIMPLIFIED BUILDING MODELS EXTRACTION FROM ULTRA-LIGHT UAV IMAGERY
Küng, O., Strecha, C., Fua, P., et al. (2012) · 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-xxxviii-1-c22-217-2011 · Cited by 21
LiDAR-enhanced Structure-from-Motion
Zhen, W., Hu, Y., Yu, H., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197030 · Cited by 21
Multi-script text versus non-text classification of regions in scene images
Sriman, B., & Schomaker, L. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.04.007 · Cited by 21
Fingerprint Local Invariant Feature Extraction on GPU with CUDA
Awad, A. I. (2013) · Cited by 21
Ghorbani, F., Ebadi, H., Sedaghat, A., & Pfeifer, N. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3151699 · Cited by 20
Probabilistic Region Matching in Narrow-Band Endoscopy for Targeted Optical Biopsy
Atasoy, S., Glocker, B., Giannarou, S., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_62 · Cited by 21
A camcorder for 3D underwater reconstruction of archeological objects
Meline, A., Triboulet, J. P., & Jouvencel, B. (2010) · OCEANS MTS/IEEE Conference (OCEANS) · doi:10.1109/oceans.2010.5664572 · Cited by 21
Metal surface defect detection using iterative thresholding technique
Senthikumar, M., Palanisamy, V., & Jaya, J. (2014) · International Conference on Current Trends in Engineering and Technology · doi:10.1109/icctet.2014.6966360 · Cited by 21
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings
Butnaru, A. M., & Ionescu, R. T. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.211 · Cited by 22
Global localization and relative pose estimation based on scale-invariant features
Kosecka, J., & Yang, X. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1333767 · Cited by 21
Dynamic Convolution-based Encoder-Decoder Framework for Image Captioning in Hindi
Mishra, S. K., Sinha, S., Saha, S., & Bhattacharyya, P. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3573891 · Cited by 24
Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis
Wang, Y.-K., Chen, H.-Y., & Chen, J.-R. (2019) · Electronics · doi:10.3390/electronics8070812 · Cited by 21
A Multi-modal Graphical Model for Scene Analysis
Namin, S. T., Najafi, M., Salzmann, M., & Petersson, L. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.139 · Cited by 20
Enhancing lecture video navigation with AI generated summaries
Rahman, M. R., Koka, R. S., Shah, S. K., Solorio, T., & Subhlok, J. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11866-7 · Cited by 21
Individuals Identification Based on Palm Vein Matching under a Parallel Environment
Hernández-García, R., Barrientos, R. J., Rojas, C., & Mora, M. (2019) · Applied Sciences · doi:10.3390/app9142805 · Cited by 21
Realtime Camera Tracking in the MATRIS Project
Chandaria, J., Thomas, G., Bartczak, B., et al. (2007) · SMPTE Motion Imaging Journal · doi:10.5594/j11426 · Cited by 21
Unified representation and registration of heterogeneous sets of geometric primitives
Nardi, F., Corte, B. D., & Grisetti, G. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2891989 · Cited by 21
Local Depth Patterns for Tracking in Depth Videos
Awwad, S., Hussein, F., & Piccardi, M. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806295 · Cited by 21
Towards Effective Image Classification Using Class-Specific Codebooks and Distinctive Local Features
Altintakan, U. L., & Yazıcı, A. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2388312 · Cited by 21
Object Detection in High-Resolution Remote Sensing Images Based on a Hard-Example-Mining Network
Zhang, L., Wang, Y., & Huo, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3038673 · Cited by 24
Irulappa-Pillai-Vijayakumar, D. B., Renaud, J.-P., Morneau, F., et al. (2019) · Remote Sensing · doi:10.3390/rs11080991 · Cited by 21
Three-level cascade of random forests for rapid human detection
Ko, B. C., Kim, D.-Y., Jung, J., & Nam, J.-Y. (2013) · Optical Engineering · doi:10.1117/1.oe.52.2.027204 · Cited by 20
Hsieh, S.-H., Li, Y., Wang, W., et al. (2018) · Sensors · doi:10.3390/s18030795 · Cited by 21
Towards Lipreading Sentences with Active Appearance Models
Sterpu, G., & Harte, N. (2017) · doi:10.21437/avsp.2017-14 · Cited by 21
Zhang, C., Wang, Y., Liu, H., Sun, Y., & Wang, S. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3317828 · Cited by 22
Automated accurate registration method between UAV image and Google satellite map
Yuan, Y., Huang, W., Wang, X., et al. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7729-7 · Cited by 21
Zhu, C., Wu, C., Li, Y., et al. (2022) · Applied Sciences · doi:10.3390/app12063088 · Cited by 21
Collaborating between Local and Global Learning for Distributed Online Multiple Tasks
Jin, X. Y., Luo, P., Zhuang, F., He, J., & He, Q. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806553 · Cited by 18
Robust model-based 3D object recognition by combining feature matching with tracking
Kim, S., Kweon, I.-S., & Kim, I. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2003.1241907 · Cited by 21
An Efficient Pest Classification In Smart Agriculture Using Transfer Learning
Nguyen, T. T., Vien, Q., & Sellahewa, H. (2021) · EAI Endorsed Transactions on Industrial Networks and Intelligent Systems · doi:10.4108/eai.26-1-2021.168227 · Cited by 23
High-Resolution Plant Shape Measurements from Multi-view Stereo Reconstruction
Klodt, M., & Cremers, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16220-1_13 · Cited by 21
MegaStitch: Robust Large-Scale Image Stitching
Zarei, A., Gonzalez, E. M., Merchant, N. C., Pauli, D., Lyons, E. H., & Barnard, K. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3141907 · Cited by 19
A review of feature detection and match algorithms for localization and mapping
Li, S. (2017) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/231/1/012003 · Cited by 20
Zefri, Y., Sebari, I., Hajji, H., et al. (2021) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2021.100576 · Cited by 20
Evaluation of Acceleration Algorithm for Biometric Identification
Egawa, S., Awad, A. I., & Baba, K. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-30567-2_19 · Cited by 20
Cesana, M., Redondi, A. E. C., Tiglao, N. M. C., Grilo, A., Barceló-Ordinas, J. M., Alaei, M., & Todorova, P. (2012) · Next Generation Internet Networks (NGI) · doi:10.1109/ngi.2012.6252168 · Cited by 20
Wang, B., Wang, Z., Sun, X., He, Q., Wang, H., & Fu, K. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3263149 · Cited by 20
A Review of Codebook Models in Patch-Based Visual Object Recognition
Ramanan, A., & Niranjan, M. (2011) · Journal of Signal Processing Systems · doi:10.1007/s11265-011-0622-x · Cited by 20
Simultaneous image tagging and geo-location prediction within hypergraph ranking framework
Pliakos, K., & Kotropoulos, C. L. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854936 · Cited by 20
Bayesian prior models for vehicle make and model recognition
Sarfraz, M. S., Saeed, A., Khan, M. H., et al. (2009) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1145/1838002.1838041 · Cited by 20
Virtual View Specification and Synthesis for Free Viewpoint Television
Li, W., Zhou, J., Li, B., et al. (2009) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2009.2014021 · Cited by 20
An Evolutionary Shadow Correction Network and a Benchmark UAV Dataset for Remote Sensing Images
Luo, S., Li, H., Li, Y., Shao, C., Shen, H., & Zhang, L. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3295450 · Cited by 22
Dental image enhancement network for early diagnosis of oral dental disease
Khan, R., Akbar, S., Khan, A. H., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-30548-5 · Cited by 20
Wang, X., Niu, P., Yang, H., & Chen, L. (2012) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2012.05.008 · Cited by 20
Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference
Song, Y., Cai, W., Eberl, S., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_24 · Cited by 20
Estimation of Soil Surface Roughness Using Stereo Vision Approach
Azizi, A., Abbaspour‐Gilandeh, Y., Gundoshmian, T. M., et al. (2021) · Sensors · doi:10.3390/s21134386 · Cited by 20
BDMUIE: Underwater image enhancement based on Bayesian diffusion model
Chen, L., Xu, Z., Wei, C. Y., & Xu, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129274 · Cited by 23
Active-semantic localization with a single consumer-grade camera
Yi, C., Suh, I. H., Lim, G. H., & Choi, B.-U. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5346258 · Cited by 20
A Multi-Camera Visual Surveillance System for Tracking of Reoccurrences of People
Pham, T. V., Worring, M., & Smeulders, A. W. M. (2007) · ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) · doi:10.1109/icdsc.2007.4357520 · Cited by 20
Solano‐Correa, Y. T., Bovolo, F., & Bruzzone, L. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2914397 · Cited by 20
He, L., Yang, J., Kong, B., et al. (2017) · Applied Sciences · doi:10.3390/app7060517 · Cited by 20
Self-calibration dense bundle adjustment of multi-view Worldview-3 basic images
Pan, H., Huang, T., Zhou, P., et al. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.04.013 · Cited by 20
Automatic Calibration of an Industrial RGB-D Camera Network Using Retroreflective Fiducial Markers
Aalerud, A., Dybedal, J., & Hovland, G. E. (2019) · Sensors · doi:10.3390/s19071561 · Cited by 20
Li, J., Wang, P., Zhou, Y., Liang, H., & Luan, K. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.620257 · Cited by 19
Scale invariant image matching using triplewise constraint and weighted voting
Pang, Y., Shang, M., Yuan, Y., et al. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.11.008 · Cited by 20
Zagoris, K., Pratikakis, I., Antonacopoulos, A., Gatos, B., & Papamarkos, N. (2012) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2012.207 · Cited by 22
Text detection in stores using a repetition prior
Xiong, B., & Grauman, K. L. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477575 · Cited by 20
Raja, K. B., Ramachandra, R., & Busch, C. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103979 · Cited by 20
Apollonio, F. I., Ballabeni, M., Bertacchi, S., et al. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-5-w1-329-2017 · Cited by 20
Coding video sequences of visual features
Baroffio, L., Cesana, M., Redondi, A. E. C., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738390 · Cited by 20
Democratizing photogrammetry: an accuracy perspective
Shan, J., Li, Z., Lercel, D. J., et al. (2023) · Geo-spatial Information Science · doi:10.1080/10095020.2023.2178336 · Cited by 20
Cutugno, M., Robustelli, U., & Pugliano, G. (2022) · Drones · doi:10.3390/drones6090242 · Cited by 22
Visual discomfort prediction on stereoscopic 3D images without explicit disparities
Chen, J., Zhou, J. W., Sun, J., et al. (2016) · Signal Processing Image Communication · doi:10.1016/j.image.2016.11.006 · Cited by 20
Toward real-time human detection and tracking in diverse environments
Koenig, N. (2007) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2007.4354077 · Cited by 20
Zhang, A.-Q., Zhao, H.-P., Li, F., Liang, P., Gao, J., & Cheng, M. Y. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.969707 · Cited by 20
Assessing geoaccuracy of structure from motion point clouds from long-range image collections
Nilosek, D., Walvoord, D. J., & Salvaggio, C. (2014) · Optical Engineering · doi:10.1117/1.oe.53.11.113112 · Cited by 20
Sketch based image retrieval via image-aided cross domain learning
Lei, J., Zheng, K., Zhang, H., et al. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296970 · Cited by 20
Nurhudatiana, A., & Kong, A. W. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2387575 · Cited by 20
An empirical Bayes approach to contextual region classification
Lazebnik, S., & Raginsky, M. (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.2009.5206690 · Cited by 20
Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey
Dhiman, N. K., Deodhare, D., & Khemani, D. (2015) · Sadhana · doi:10.1007/s12046-015-0402-6 · Cited by 20
Vehicle and license plate recognition with novel dataset for toll collection
Usama, M., Anwar, H., Anwar, S., et al. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01443-8 · Cited by 20
Illumination invariant feature point matching for high-resolution planetary remote sensing images
Wu, B., Zeng, H., & Hu, H. (2018) · Planetary and Space Science · doi:10.1016/j.pss.2018.01.007 · Cited by 20
Li, P., Chen, P., & Zhang, D. (2022) · Remote Sensing · doi:10.3390/rs14102424 · Cited by 22
A Probabilistic Feature Map-Based Localization System Using a Monocular Camera
Kim, H., Lee, D., Oh, T., et al. (2015) · Sensors · doi:10.3390/s150921636 · Cited by 20
Video emotion recognition in the wild based on fusion of multimodal features
Chen, S., Li, X., Jin, Q., Zhang, S., & Qin, Y. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997629 · Cited by 20
Bag of morphological words for content-based geographical retrieval
Aptoula, E. (2014) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2014.6849837 · Cited by 19
Resolution- Independent Characteristic Scale Dedicated to Satellite Images
Luo, B., Aujol, J., Gousseau, Y., Ladjal, S., & Maı̂tre, H. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.906004 · Cited by 20
Vision based localization system for AUV docking on subsea intervention panels
Palmer, T., Ribas, D., Ridao, P., et al. (2009) · doi:10.1109/oceanse.2009.5278216 · Cited by 20
Vision and sonar sensor fusion for mobile robot localization in aliased environments
Zingaretti, P., & Frontoni, E. (2006) · doi:10.1109/mesa.2006.296971 · Cited by 20
Spatio-temporal deep learning for improved face presentation attack detection
Khan, S., Siddique, T. H. M., Ibrahim, M. S., et al. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113059 · Cited by 20
A real-time face detection and recognition system
Shi, K.-Q., Pang, S.-R., & Yu, F.-Q. (2012) · 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet) · doi:10.1109/cecnet.2012.6201994 · Cited by 19
Zhou, T., Hasheminasab, S. M., Ravi, R., et al. (2020) · Remote Sensing · doi:10.3390/rs12142268 · Cited by 20
Polar Scale-Invariant Feature Transform for Synthetic Aperture Radar Image Registration
Zeng, L., Zhou, D., Liang, J., et al. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2698450 · Cited by 20
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
