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 2,030
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 8,201–8,300 of 56,203
An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced Medical Images
Zhu, D., Zhu, H., Wang, X., Lu, R., & Feng, D. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3149847 · Cited by 40
Fast Transport Optimization for Monge Costs on the Circle
Delon, J., Salomon, J., & Sobolevskii, A. N. (2010) · SIAM Journal on Applied Mathematics · doi:10.1137/090772708 · Cited by 39
Contextual Exemplar Classifier-Based Image Representation for Classification
Zhang, C., Huang, Q., & Tian, Q. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2527380 · Cited by 38
Mapping Indoor Spaces by Adaptive Coarse-to-Fine Registration of RGB-D Data
dos Santos, D. R., Basso, M. A., Khoshelham, K., de Oliveira, E., Pavan, N. L., & George Vosselman, A. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2508880 · Cited by 38
Deep Feature Extraction for Panoramic Image Stitching
Hoang, V.-D., Tran, D.-P., Nguyen, G. N., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-42058-1_12 · Cited by 39
Wang, Z. I., Li, C., Mo, Y., et al. (2022) · Displays · doi:10.1016/j.displa.2022.102359 · Cited by 39
Payá, L., Gil, A., & Reinoso, Ó. (2017) · Journal of Sensors · doi:10.1155/2017/3497650 · Cited by 39
Classification Method of Significant Rice Pests Based on Deep Learning
Li, Z., Jiang, X., Jia, X., Duan, X., Wang, Y., & Mu, J. (2022) · Agronomy · doi:10.3390/agronomy12092096 · Cited by 42
Supervised descriptor learning for multi-output regression
Zhen, X., Wang, Z., Yu, M., et al. (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.2015.7298725 · Cited by 39
Automatic human identification based on dental X-ray radiographs using computer vision
Heinrich, A., Güttler, F. V., Schenkl, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-60817-6 · Cited by 39
Model test study on monitoring dynamic process of slope failure through spatial sensor network
Lü, P., Wu, H., Qiao, G., et al. (2015) · Environmental Earth Sciences · doi:10.1007/s12665-015-4369-8 · Cited by 39
When IoT met Augmented Reality
Park, Y., Yun, S., & Kim, K.-H. (2019) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3307334.3326079 · Cited by 39
Lejsek, H., Jónsson, B. Þ., & Amsaleg, L. (2011) · International Conference on Multimedia Retrieval · doi:10.1145/1991996.1992050 · Cited by 38
Photogrammetric water level determination using smartphone technology
Elias, M., Kehl, C., & Schneider, D. G. (2019) · The Photogrammetric Record · doi:10.1111/phor.12280 · Cited by 37
Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
Fónod, R., Cho, H., Yeo, H., & Geroliminis, N. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105205 · Cited by 55
“Various keypoint detectors such as scale-invariant feature transform (SIFT) [87], root SIFT (RSIFT) [88], binary robust invariant scalable keypoints (BRISK) [89], KAZE [90], accelerated-KAZE (A-KAZE) [91], and oriented FAST and rotated BRIEF (ORB) [92] are commonly used.”
A novel passive forgery detection algorithm for video region duplication
Su, L., & Li, C. (2017) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-017-0496-6 · Cited by 39
Exploiting Hierarchical Activations of Neural Network for Image Retrieval
Li, Y., Kong, X., Zheng, L., & Tian, Q. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2967197 · Cited by 36
Object Matching Using Boundary Descriptors
Arandjelović, O. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.85 · Cited by 40
Human hand recognition from robotic skin measurements in human-robot physical interactions
Albini, A., Denei, S., & Cannata, G. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206300 · Cited by 40
Rotation, scaling, and translation resilient watermarking for images
Lin, Y.-T., Huang, C. Y., & Lee, G. (2011) · IET Image Processing · doi:10.1049/iet-ipr.2009.0264 · Cited by 40
Recent Advances in Zero-shot RecognitionPreprint
Fu, Y., Xiang, T., Jiang, Y., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.04837 · Cited by 32
Large-Scale Image Retrieval Based on Compressed Camera Identification
Valsesia, D., Coluccia, G., Bianchi, T., & Magli, E. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2455417 · Cited by 36
Jaklič, A., Erič, M., Mihajlović, I., Stopinšek, Ž., & Solina, F. (2015) · Journal of Archaeological Science · doi:10.1016/j.jas.2015.08.007 · Cited by 39
Fularz, M., Kraft, M., Schmidt, A., & Kasinski, A. (2015) · International Journal of Advanced Robotic Systems · doi:10.5772/61434 · Cited by 36
Precise DEM extraction from Svalbard using 1936 high oblique imagery
Girod, L., Nielsen, N. I., Couderette, F., et al. (2018) · Geoscientific instrumentation, methods and data systems · doi:10.5194/gi-7-277-2018 · Cited by 39
A Closed-Form Solution to Tensor Voting: Theory and Applications
Wu, T.-P., Yeung, S.-K., Jia, J., et al. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.250 · Cited by 39
Deep FisherNet for Object ClassificationPreprint
Tang, P., Wang, X., Shi, B., Bai, X., Liu, W., & Zhuowen Tu, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.00182 · Cited by 37
SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images
Urschler, M., Bauer, J., Ditt, H., et al. (2006) · Lecture notes in computer science · doi:10.1007/11889762_7 · Cited by 39
Learning Predictive Features in Affordance based Robotic Perception Systems
Fritz, G., Paletta, L., Breithaupt, R., et al. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.281720 · Cited by 39
Stratified pooling based deep convolutional neural networks for human action recognition
Yu, S., Cheng, Y., Su, S., Cai, G., & Li, S. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3768-5 · Cited by 39
A nearest-neighbor search model for distance metric learning
Ruan, Y., Xiao, Y., Hao, Z., & Liu, B. (2020) · Information Sciences · doi:10.1016/j.ins.2020.11.054 · Cited by 40
Combining modality-specific extreme learning machines for emotion recognition in the wild
Kaya, H., & Salah, A. A. (2015) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-015-0175-6 · Cited by 39
Sclera recognition using dense-SIFT
Das, A., Pal, U., Ferrer, M. A., et al. (2013) · doi:10.1109/isda.2013.6920711 · Cited by 39
Deep image clustering by fusing contrastive learning and neighbor relation mining
Xu, C., Lin, R., Cai, J., & Wang, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107967 · Cited by 39
Image representations beyond histograms of gradients: The role of Gestalt descriptors
Bileschi, S., & Wolf, L. (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.2007.383122 · Cited by 39
Graph commute times for image representation
Behmo, R., Nikos Paragios, A., & Véronique Prinet, A. (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.2008.4587840 · Cited by 39
Generalizable Human Pose Triangulation
Bartol, K., Bojanić, D., & Petković, T. (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/cvpr52688.2022.01075 · Cited by 40
Real-time micro-scale temperature imaging at low cost based on fluorescent intensity ratio
Xiong, J., Zhao, M., Han, X., et al. (2017) · Scientific Reports · doi:10.1038/srep41311 · Cited by 39
Simultaneous localization and mapping with learned object recognition and semantic data association
Rogers, J. G., Trevor, A. J. B., Nieto-Granda, C., et al. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6095152 · Cited by 39
Structured Weak Semantic Space Construction for Visual Categorization
Zhang, C., Cheng, J., & Tian, Q. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2728060 · Cited by 38
Image processing and machine learning for fully automated probabilistic evaluation of medical images
Šajn, L., & Kukar, M. (2010) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2010.06.021 · Cited by 38
Efficient k-NN based HEp-2 cells classifier
Stoklasa, R., Majtner, T., & Svoboda, D. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.09.021 · Cited by 39
Robust Loop Closure Detection based on Bag of SuperPoints and Graph Verification
Yue, H., Miao, J., Yu, Y., Chen, W., & Wen, C. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967726 · Cited by 39
Product counting using images with application to robot-based retail stock assessment
Kejriwal, N., Garg, S., & Kumar, S. (2015) · IEEE International Conference on Technologies for Practical Robot Applications · doi:10.1109/tepra.2015.7219676 · Cited by 39
Localizing the object contact through matching tactile features with visual map
Luo, S., Mou, W., Althoefer, K. A., & Liu, H. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139743 · Cited by 51
PlaceRaider: Virtual Theft in Physical Spaces with SmartphonesPreprint
Templeman, R., Rahman, Z., Crandall, D., & Kapadia, A. C. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1209.5982 · Cited by 76
Scalable Image Retrieval by Sparse Product Quantization
Ning, Q., Zhu, J., Zhong, Z., et al. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2625260 · Cited by 39
Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization
Zhang, X., Xiong, H., Zhou, W., & Tian, Q. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2509425 · Cited by 39
Augmented reality annotations to assist persons with Alzheimers and their caregivers
Quintana, E., & Favela, J. (2012) · Personal and Ubiquitous Computing · doi:10.1007/s00779-012-0558-6 · Cited by 39
Robustness Guarantees for Bayesian Inference with Gaussian Processes
Cardelli, L., Kwiatkowska, M., Laurenti, L., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017759 · Cited by 6
Exploiting depth from single monocular images for object detection and semantic segmentation
Cao, Y., Shen, C., & Shen, H. T. (2017) · UTS ePRESS (University of Technology Sydney) · Cited by 36
Zhao, D., Cao, J., Zhu, X., Zhang, Z., Arun, P. V., Guo, Y., Qian, K., Zhang, L., Zhou, H., & Hu, J. (2022) · Remote Sensing · doi:10.3390/rs14246219 · Cited by 42
One Shot Detection withLaplacian Objectand Fast Matrix Cosine Similarity
Biswas, S. K., & Milanfar, P. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2453950 · Cited by 38
Describing Images Using Qualitative Models and Description Logics
Falomir, Z., Jiménez-Ruiz, E., Escrig, M. T., et al. (2011) · Spatial Cognition and Computation · doi:10.1080/13875868.2010.545611 · Cited by 39
Extracting watermarks from printouts captured with wide angles using computational photography
Pramila, A., Keskinarkaus, A., Takala, V., et al. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3895-z · Cited by 39
Evaluating topic representations for exploring document collections
Αλέτρας, Ν., Baldwin, T., Lau, J. H., & Stevenson, M. (2015) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23574 · Cited by 36
Moving on to dynamic environments: Visual odometry using feature classification
Kitt, B., Moosmann, F., & Stiller, C. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5650517 · Cited by 37
Location-based image retrieval for urban environments
Zhang, J., Hallquist, A., Liang, E., et al. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116517 · Cited by 39
Cao, W., & Li, J. (2022) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-022-0855-8 · Cited by 40
Gómez‐Gutiérrez, Á., Blasco, J. J. D. S., Matías, J. D., & Sempere, F. B. (2014) · Remote Sensing · doi:10.3390/rs6065407 · Cited by 39
A hybrid deep model with HOG features for Bangla handwritten numeral classification
Sharif, S. M. A., Mohammed, N., Mansoor, N., et al. (2016) · International Conference on Electrical and Computer Engineering (ICECE) · doi:10.1109/icece.2016.7853957 · Cited by 39
Moradi, S., Zayed, T. M., Nasiri, F., & Golkhoo, F. (2020) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000553 · Cited by 38
Automatic Registration of High-Resolution Images Using Local Properties of Features
Han, Y. K., Byun, Y.-G., Choi, J. W., et al. (2012) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.78.3.211 · Cited by 39
SIFT Matching with CNN Evidences for Particular Object Retrieval
Zhang, G., Zeng, Z., Zhang, S., Zhang, Y., & Wu, W. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.081 · Cited by 38
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images
Ghosh, N., Sun, Y., Bhanu, B., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.05.002 · Cited by 39
Highly accurate boundary detection and grouping
Kokkinos, I. (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.2010.5539956 · Cited by 37
Analyzing images' privacy for the modern web
Squicciarini, A., Caragea, C., & Balakavi, R. (2014) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2631775.2631803 · Cited by 39
3D Object Recognition in Cluttered Scenes With Robust Shape Description and Correspondence Selection
Tang, K., Song, P., & Chen, X. (2017) · IEEE Access · doi:10.1109/access.2017.2658681 · Cited by 39
Bian, C., Wang, Y., Lü, Z., An, Y., Wang, H., Kong, L., Du, Y., & Tian, J. (2021) · Cancers · doi:10.3390/cancers13071659 · Cited by 43
Multi-class remote sensing object recognition based on discriminative sparse representation
Wang, X., Shen, S., Ning, C., et al. (2016) · Applied Optics · doi:10.1364/ao.55.001381 · Cited by 39
Meng, X., Yin, Y., Yang, G., et al. (2013) · Sensors · doi:10.3390/s130709248 · Cited by 39
Rolling Shutter Correction in Manhattan World
Purkait, P., Zach, C., & Leonardis, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.101 · Cited by 40
Comparative analysis of shape descriptors for 3D objects
López, G. L., Negrón, A. P. P., de Antonio, A., et al. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3330-5 · Cited by 39
Vision-based localization using an edge map extracted from 3D laser range data
Borges, P., Zlot, R., Bosse, M. C., Nuske, S. T., & Tews, A. D. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509517 · Cited by 39
Dozza, M., & González, N. P. (2013) · Accident Analysis & Prevention · doi:10.1016/j.aap.2013.02.014 · Cited by 39
Vision-based environmental perception for autonomous driving
Liu, F., Lu, Z., & Lin, X. (2023) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070231203059 · Cited by 51
Global shape modeling using the OSIRIS-REx scanning Laser Altimeter
Seabrook, J. A., Daly, M. G., Barnouin, O. S., et al. (2019) · Planetary and Space Science · doi:10.1016/j.pss.2019.07.003 · Cited by 39
Tran, T.-T., & Ha, C. (2018) · International Journal of Control Automation and Systems · doi:10.1007/s12555-017-0535-y · Cited by 39
Takeuchi, Y., & Perlin, K. (2012) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208404 · Cited by 40
Image Location Estimation by Salient Region Matching
Qian, X., Zhao, Y., & Han, J. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2462131 · Cited by 39
3DHoPD: A Fast Low-Dimensional 3-D Descriptor
Prakhya, S. M., Lin, J., Chandrasekhar, V., et al. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2667721 · Cited by 39
Geodesic Distance-weighted Shape Vector Image Diffusion
Hua, J., Lai, Z., Dong, M., et al. (2008) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2008.134 · Cited by 39
Document image classification: Progress over two decades
Liu, L., Wang, Z., Qiu, T., Chen, Q., Lu, Y., & Suen, C. Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.114 · Cited by 40
Lee, J. A., Cheng, J. T., Lee, B. H., et al. (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.2015.7298707 · Cited by 39
Optical flow-based structure-from-motion for the reconstruction of epithelial surfaces
Phan, T.-B., Trinh, D. H., Wolf, D., & Daul, C. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107391 · Cited by 39
Research on floc structure and physical properties based on pipeline flocculation
Chen, G., Li, C., Ruan, Z., et al. (2023) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2023.103627 · Cited by 39
Interframe Coding of Feature Descriptors for Mobile Augmented Reality
Makar, M. A., Chandrasekhar, V., Tsai, S. S., et al. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2331136 · Cited by 39
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
Kokkinos, I., Maragos, P. A., & Yuille, A. (2006) · 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.2006.74 · Cited by 39
DCT Regularized Extreme Visual Recovery
Wang, Y., Xu, C., You, S., Xu, C., & Tao, D. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2678798 · Cited by 39
StereoPasting: Interactive Composition in Stereoscopic Images
Tong, R., Zhang, Y., & Cheng, K. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.319 · Cited by 39
Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding
Zhang, S., Yao, H., Sun, X., & Liu, S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168757 · Cited by 38
Hussain, L., Aziz, W., Alshdadi, A. A., Nadeem, M. S. A., Khan, I. R., & Chaudhry, Q.-U.-A. (2019) · IEEE Access · doi:10.1109/access.2019.2917303 · Cited by 39
Practical Camera Calibration From Moving Objects for Traffic Scene Surveillance
Zhang, Z., Tan, T., Huang, K., et al. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2210670 · Cited by 39
Structural epitome: a way to summarize one’s visual experience
Jojić, N., Perina, A., & Murino, V. (2010) · neural information processing systems · Cited by 39
PCNet: A structure similarity enhancement method for multispectral and multimodal image registration
Cao, S.-Y., Yu, B., Luo, L., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.004 · Cited by 40
3D reconstruction of spherical images: a review of techniques, applications, and prospects
Jiang, S., You, K., Li, Y., Weng, D., & Chen, W. (2024) · Geo-spatial Information Science · doi:10.1080/10095020.2024.2313328 · Cited by 41
“Classical methods SIFT [47], [48], SURF [49], ORB [50] Advantages: (1) existed algorithms can be used directly; (2) high efficiency without other computational costs.”
InfluentialGround and aerial meta-data integration for localization and reconstruction: A review
Gao, X., Shen, S., Hu, Z., & Wang, Z. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.07.036 · Cited by 40
An improved multi-source based soft sensor for measuring cement free lime content
Li, W., Wang, D., Zhou, X., et al. (2015) · Information Sciences · doi:10.1016/j.ins.2015.06.035 · Cited by 39
Age classification using Radon transform and entropy based scaling SVM
Zhou, H., Miller, P. C., & Zhang, J. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.28 · Cited by 39
Digital close range photogrammetry for the study of rill development at flume scale
Guo, M., Shi, H., Zhao, J., et al. (2016) · CATENA · doi:10.1016/j.catena.2016.03.036 · Cited by 39
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