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 16,001–16,100 of 56,202
Feature Matching Performance of Compact Descriptors for Visual Search
Chandrasekhar, V., Takacs, G., Chen, D. M., et al. (2014) · DCC · doi:10.1109/dcc.2014.50 · Cited by 16
Digital Image Stabilization for humanoid eyes inspired by human VOR system
Ryu, Y. G., Roh, H. C., Kim, S., et al. (2009) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2009.5420460 · Cited by 16
A Registration Algorithm for Astronomical Images Based on Geometric Constraints and Homography
Lin, B., Xu, X., Shen, Z., et al. (2023) · Remote Sensing · doi:10.3390/rs15071921 · Cited by 16
Large scale evaluation of local image feature detectors on homography datasetsPreprint
Lenc, K., & Vedaldi, A. (2018) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1807.07939 · Cited by 31
Object Categorization Using Kernels Combining Graphs and Histograms of Gradients
Suard, F., Rakotomamonjy, A., & Bensrhair, A. (2006) · Lecture notes in computer science · doi:10.1007/11867661_3 · Cited by 16
Unmanned aerial vehicle-aided stereo camera calibration for outdoor applications
Feng, W., Zhang, S., Liu, H., et al. (2020) · Optical Engineering · doi:10.1117/1.oe.59.1.014110 · Cited by 16
Automatic Segmentation for Plant Leaves via Multiview Stereo Reconstruction
Guo, J., & Xu, L. (2017) · Mathematical Problems in Engineering · doi:10.1155/2017/9845815 · Cited by 16
Omnidirectional depth segmentation using orthogonal fringe patterns and multi-scale enhancement
Ji, D., Li, J., Feng, H., et al. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.105890 · Cited by 16
Human Attribute Recognition— A Comprehensive Survey
Yaghoubi, E., Khezeli, F., Borza, D. L., Kumar, S. A., Neves, J. C., & Proença, H. P. M. C. (2020) · Applied Sciences · doi:10.3390/app10165608 · Cited by 19
Using Language to Learn Structured Appearance Models for Image Annotation
Jamieson, M., Fazly, A., Stevenson, S., Dickinson, S., & Wachsmuth, S. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.283 · Cited by 16
Copy-Move Tampering Detection based on Local Binary Pattern Histogram Fourier Feature
Soni, B., Das, P. K., & Thounaojam, D. M. (2017) · International Conference on Computer and Communication Technology · doi:10.1145/3154979.3155001 · Cited by 16
Robust Camera Pose Estimation for Unordered Road Scene Images in Varying Viewing Conditions
Venator, M., Bruns, E., & Maier, A. (2019) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2019.2955375 · Cited by 16
Blood parasite identification using feature based recognition
Thung, F., & Suwardi, I. S. (2011) · doi:10.1109/iceei.2011.6021590 · Cited by 16
Deep Micro-Dictionary Learning and Coding Network
Tang, H., Wei, H., Xiao, W., Wang, W., Xu, D., Yan, Y., & Sebe, N. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00047 · Cited by 14
Underwater 3D object reconstruction with multiple views in video stream via structure from motion
Xu, X. W., Che, R., Nian, R., et al. (2016) · OCEANS 2016 - Shanghai · doi:10.1109/oceansap.2016.7485694 · Cited by 16
Song, F. D., Dan, T., Yu, R., et al. (2019) · Remote Sensing Letters · doi:10.1080/2150704x.2019.1576949 · Cited by 16
Estimating point-of-regard using corneal surface image
Takemura, K., Kimura, S., & Suda, S. (2014) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/2578153.2578197 · Cited by 16
Automatic Extrinsic Calibration of Monocular Camera and LIDAR in Natural Scenes
Wang, L., Xiao, Z., Zhao, D., et al. (2018) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2018.8812555 · Cited by 16
Recognition and reconstruction of transparent objects for augmented reality
Torres-Gomez, A., & Mayol-Cuevas, W. W. (2014) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2014.6948418 · Cited by 16
Training Visual-Semantic Embedding Network for Boosting Automatic Image Annotation
Zhang, W., Hu, H., & Hu, H. (2018) · Neural Processing Letters · doi:10.1007/s11063-017-9753-9 · Cited by 17
Lee, B. C. G. (2018) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqy063 · Cited by 16
Learning Shared, Discriminative, and Compact Representations for Visual Recognition
Löbel, H., Vidal, R., & Soto, Á. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408349 · Cited by 16
Xu, G. L., Chen, J., & Li, X. (2017) · IEEE Access · doi:10.1109/access.2017.2777818 · Cited by 16
A low-cost stereo system for 3D object recognition
Oleari, F., Rizzini, D. L., & Caselli, S. (2013) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2013.6646095 · Cited by 16
Zhang, H., Jin, Y., Liu, Q., et al. (2022) · Measurement · doi:10.1016/j.measurement.2022.110835 · Cited by 16
Tang, S., Grier, C., Acıiçmez, O., et al. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772786 · Cited by 16
Toward Sparse Coding on Cosine Distance
Choi, J., Cho, H., Kwac, J., et al. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.757 · Cited by 18
An improved parking space recognition algorithm based on panoramic vision
Zhang, J., Liu, T., Yin, X., et al. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10370-1 · Cited by 16
An Accurate Localization Scheme for Mobile Robots Using Optical Flow in Dynamic Environments
Cheng, J., Sun, Y., Chi, W., et al. (2018) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2018.8664893 · Cited by 16
Real-Time Video Stitching Using Camera Path Estimation and Homography Refinement
Yoon, J., & Lee, D. (2017) · Symmetry · doi:10.3390/sym10010004 · Cited by 16
Medical image registration and its application in retinal images: a review
Nie, Q., Zhang, X., Hu, Y., Gong, M., & Liu, J. (2024) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-024-00173-8 · Cited by 22
“The warped images from both networks are used as image pairs and sent to the networks themselves to ensure that they could be returned to their original state, maximizing the similarity between the original and reversed images.”
MethodWatch fashion shows to tell clothing attributes
Zhang, S., Liu, C., Cao, X., Song, Z., & Zhou, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.12.027 · Cited by 16
Stereo rectification of pushbroom satellite images by robustly estimating the fundamental matrix
Tatar, N., & Arefi, H. (2019) · International Journal of Remote Sensing · doi:10.1080/01431161.2019.1624862 · Cited by 16
Detection of moving targets with a moving camera
Zhou, D., Wang, L., Cai, X., et al. (2009) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2009.5420591 · Cited by 16
Outliers Elimination Based Ransac for Fundamental Matrix Estimation
Yang, S., & Li, B. (2013) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv.2013.63 · Cited by 16
Jiang, Z.-T., & Liu, S.-C. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-19853-3_66 · Cited by 16
Zhou, J., Gan, J., Gao, W., & Liang, A. (2021) · Information Sciences · doi:10.1016/j.ins.2021.06.002 · Cited by 16
Liu, F., Zhang, Z., & Li, S. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3332075 · Cited by 17
Mobile Robot Localization: Where We Are and What Are the Challenges?
Skrzypczyński, P. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-54042-9_23 · Cited by 16
Xu, Y., Quan, Y., Zhang, Z., et al. (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.2012.6248080 · Cited by 16
Entropy based fuzzy classification of images on quality assessment
De, I., & Sil, J. (2012) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2012.05.001 · Cited by 16
Large-Scale High-Dimensional Clustering with Fast Sketching
Chatalic, A., Gribonval, R., & Keriven, N. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461328 · Cited by 16
Saovana, N., Yabuki, N., & Fukuda, T. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101169 · Cited by 16
Picasso - to sing, you must close your eyes and draw
Stupar, A., & Michel, S. (2011) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2009916.2010012 · Cited by 15
Handwritten Digit Recognition using DAISY Descriptor: A Study
Chatterjee, A., Malakar, S., Sarkar, R., et al. (2018) · International Conference on Emerging Applications of Information Technology · doi:10.1109/eait.2018.8470426 · Cited by 16
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth DataPreprint
Sundaresan, P., Grannen, J., Thananjeyan, B., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01835 · Cited by 16
Image registration based on multi-scale SIFT for remote sensing images
Rube, I. E., Sharks, M. A., & Salem, A. R. (2009) · International Conference on Signal Processing and Communication Systems · doi:10.1109/icspcs.2009.5306401 · Cited by 16
Target Driven Instance DetectionPreprint
Ammirato, P., Fu, C.-Y., Shvets, M., Košecká, J., & Berg, A. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.04610 · Cited by 16
A survey of computer vision applied in Aerial robotic Vehicles
Liu, Y.-C., & Dai, Q. (2010) · International Conference on Optics, Photonics and Energy Engineering · doi:10.1109/opee.2010.5508131 · Cited by 15
Generation of three dimensional photo-realistic models from Lidar and image data
Li-Chee-Ming, J., Gumerov, D., Ciobanu, T., et al. (2009) · IEEE Toronto International Conference Science and Technology for Humanity · doi:10.1109/tic-sth.2009.5444457 · Cited by 16
A Study on Tracking and Augmentation in Mobile AR for e-Leisure
Jang, S.-W., Ko, J., Lee, H. J., et al. (2018) · Mobile Information Systems · doi:10.1155/2018/4265352 · Cited by 16
Semi-autonomous Learning of Objects
Kim, H., Murphy-Chutorian, E., & Triesch, J. (2006) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2006.193 · Cited by 16
Registration of Mars remote sensing images under the crater constraint
Cheng, L., Ma, L., Yang, K., Liu, Y., & Li, M. (2013) · Planetary and Space Science · doi:10.1016/j.pss.2013.05.002 · Cited by 16
Verdier, F., Miramond, B., Maillard, M., et al. (2008) · EURASIP Journal on Embedded Systems · doi:10.1155/2008/349465 · Cited by 16
Zhang, B., Song, Y., Guan, S.-U., & Zhang, Y. (2010) · International Journal of Soft Computing · doi:10.3923/ijscomp.2010.19.28 · Cited by 16
Ancient Roman Coin Retrieval: A Systematic Examination of the Effects of Coin Grade
Fare, C., & Arandjelović, O. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56608-5_32 · Cited by 16
Complex Emotion Recognition via Facial Expressions with Label Noises Self‐Cure Relation Networks
Wang, X., Wang, Y., & Zhang, D. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/7850140 · Cited by 16
Viewpoint invariant semantic object and scene categorization with RGB-D sensors
Zaki, H. F. M., Faisal Shafait, B., & Mian, A. S. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9776-8 · Cited by 15
Local Gradient Order Pattern for Face Representation and Recognition
Lei, Z., Yi, D., & Li, S. Z. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.75 · Cited by 16
A Novel Approach for Interest Point Detection via Laplacian-of-Bilateral Filter
Huang, M., Mu, Z., Zeng, H., & Huang, H. (2015) · Journal of Sensors · doi:10.1155/2015/685154 · Cited by 16
Efficient mobile AR technology using scalable recognition and tracking based on server-client model
Jung, J., Ha, J., Lee, S., et al. (2012) · Computers & Graphics · doi:10.1016/j.cag.2012.01.004 · Cited by 16
An accurate retrieval through R-MAC+ descriptors for landmark recognition
Magliani, F., & Prati, A. (2018) · International Conference on Distributed Smart Cameras · doi:10.1145/3243394.3243686 · Cited by 17
Compiling Scilab to high performance embedded multicore systems
Stripf, T., Oey, O., Bruckschloegl, T., et al. (2013) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2013.07.004 · Cited by 16
Research on Adaptive ORB-SURF Image Matching Algorithm Based on Fusion of Edge Features
Han, L., Wang, J., Zhang, Y., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3212151 · Cited by 16
Color Point Cloud Registration Based on Supervoxel Correspondence
Yang, Y., Chen, W., Wang, M.-Y., Zhong, D.-X., & Du, S. (2020) · IEEE Access · doi:10.1109/access.2020.2963987 · Cited by 13
Improved Video Stabilization using SIFT-Log Polar Technique for Unmanned Aerial Vehicles
Sharif, M. I., Khan, S., Saba, T., Raza, M., & Rehman, A. U. (2019) · doi:10.1109/iccisci.2019.8716427 · Cited by 16
A Study of Real-Time Hand Gesture Recognition Using SIFT on Binary Images
Lin, W.-S., Wu, Y.-L., Hung, W.-C., et al. (2012) · Smart innovation, systems and technologies · doi:10.1007/978-3-642-35473-1_24 · Cited by 16
Image Watermarking Scheme Based on Scale-Invariant Feature Transform
Lyu, W., Chang, C., Nguyen, T., et al. (2014) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2014.10.018 · Cited by 16
AProNet: Detecting objects with precise orientation from aerial images
Zheng, X., Zhang, W., Huan, L., Gong, J., & Zhang, H. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.08.023 · Cited by 16
An efficient approach for recognizing and tracking spontaneous facial expressions
Zraqou, J. S., Alkhadour, W., & Al-Nuaimi, A. (2013) · International Conference on E-Learning and E-Technologies in Education · doi:10.1109/icelete.2013.6644393 · Cited by 16
Multiple spatial pooling for visual object recognition
Huang, Y., Wu, Z., Wang, L., et al. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.09.037 · Cited by 16
Kernels on Attributed Pointsets with Applications
Parsana, M., Bhattacharya, S., Bhattacharya, C., et al. (2007) · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · Cited by 16
Kim, S. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2011/101428 · Cited by 16
Road mark recognition using HOG-SVM and correlation
Alfalou, A., Ouerhani, Y., & Brosseau, C. (2017) · Optics and Photonics for Information Processing · doi:10.1117/12.2273304 · Cited by 16
Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study
Avola, D., Cinque, L., Foresti, G. L., et al. (2018) · Intelligent systems reference library · doi:10.1007/978-3-319-73891-8_2 · Cited by 16
Representation and coding of signal geometry
Boufounos, P. T., Rane, S. D., & Mansour, H. (2017) · Information and Inference A Journal of the IMA · doi:10.1093/imaiai/iax002 · Cited by 16
Structured Epipolar Matcher for Local Feature Matching
Chang, J., Yu, J., & Zhang, T. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00657 · Cited by 16
Deneault, D., Schinstock, D. E., & Lewis, C. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543188 · Cited by 16
A System for Colorectal Tumor Classification in Magnifying Endoscopic NBI Images
Tamaki, T., Yoshimuta, J., Takeda, T., Raytchev, B., Kaneda, K., Yoshida, S., Takemura, Y., & Tanaka, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19309-5_35 · Cited by 16
Use of geometric features of principal components for indexing a biometric database
Jayaraman, U., Prakash, S., & Gupta, P. (2012) · Mathematical and Computer Modelling · doi:10.1016/j.mcm.2012.06.005 · Cited by 16
Learning models for visual 3D localization with implicit mappingPreprint
Rosenbaum, D., Besse, F., Viola, F., Rezende, D. J., & Eslami, S. M. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.03149 · Cited by 16
A Filtering Strategy for Interest Point Detecting to Improve Repeatability and Information Content
Zhu, Q., Wan, N., & Wu, B. (2007) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.73.5.547 · Cited by 16
Improving Object Retrieval Quality by Integration of Similarity Propagation and Query Expansion
Pang, S., Ma, J., Zhu, J., Xue, J., & Tian, Q. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2866230 · Cited by 16
Gorokhovatskyi, V., Gadetska, S., & Ponomarenko, R. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-26474-1_35 · Cited by 16
An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
Odeh, N., Toma, A., Mohammed, F., et al. (2021) · Applied Sciences · doi:10.3390/app11115225 · Cited by 16
Robot End-Effector Mounted Camera Pose Optimization in Object Detection-Based Tasks
Roveda, L., Maroni, M., Mazzuchelli, L., Praolini, L., Shahid, A. A., Bucca, G., & Piga, D. (2021) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-021-01558-0 · Cited by 19
Local visual features extraction from texture+depth content based on depth image analysis
Karpushin, M., Valenzise, G., & Frederic Dufaux, A. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025568 · Cited by 16
Cross-spectral image patch similarity using convolutional neural network
Suárez, P. L., Sappa, A. D., & Vintimilla, B. X. (2017) · IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM) · doi:10.1109/ecmsm.2017.7945888 · Cited by 16
Salient Object Detection Combining a Self-Attention Module and a Feature Pyramid Network
Ren, G., Dai, T., Barmpoutis, P., et al. (2020) · Electronics · doi:10.3390/electronics9101702 · Cited by 16
An adaptive symmetry detection algorithm based on local features
Cai, D., Li, P., Su, F., et al. (2014) · doi:10.1109/vcip.2014.7051610 · Cited by 16
Multi-Scale Fused SAR Image Registration Based on Deep Forest
Mao, S., Yang, J., Gou, S., Jiao, L., Xiong, T., & Xiong, L. (2021) · Remote Sensing · doi:10.3390/rs13112227 · Cited by 16
Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors
Zheng, X., Zhang, Y., Hong, S., Li, H., Tang, L., Xiong, Y., Zhou, J., Wang, Y., Sun, X., Zhu, P., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3069250 · Cited by 16
Learning visual object definitions by observing human activities
Veloso, M., Hundelshausen, F. V., & Rybski, P. E. (2006) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2005.1573560 · Cited by 16
Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis
Mwebaze, E., & Biehl, M. L. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-28518-4_29 · Cited by 16
Tingting, H. U., & Ikenaga, T. (2018) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2017mvp0012 · Cited by 16
Gurevich, I. B., & Yashina, V. V. (2020) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661820030086 · Cited by 16
Video Text Tracking With a Spatio-Temporal Complementary Model
Gao, Y., Li, X., Zhang, J., et al. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3124313 · Cited by 16
Efficient Texture Representation Using Multi-scale Regions
Wildenauer, H., Mičušík, B., & Vincze, M. (2007) · Springer eBooks · doi:10.1007/978-3-540-76386-4_5 · Cited by 16
Colon10k: A Benchmark For Place Recognition In Colonoscopy
Ma, R., McGill, S., Wang, R., Rosenman, J. G., Frahm, J., Zhang, Y., & Pizer, S. M. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433780 · Cited by 15
Hand fine-motion recognition based on 3D Mesh MoSIFT feature descriptor
Ming, Y. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.06.088 · Cited by 16
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
