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 30,701–30,800 of 56,201
Automatic content based image retrieval using semantic analysis
Santos, E., & Gu, Q. (2014) · Journal of Intelligent Information Systems · doi:10.1007/s10844-014-0321-8 · Cited by 4
Mefteh, S., Kaâniche, M.-B., Ksantini, R., & Bouhoula, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14788-1 · Cited by 4
Registration-Is-Evaluation: Robust Point Set Matching With Multigranular Prior Assessment
Shu, Y., Liao, Z., Xiao, B., et al. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3071550 · Cited by 4
Yin, H., Sun, M., Zhang, L., et al. (2025) · Displays · doi:10.1016/j.displa.2025.103030 · Cited by 4
Robust Template Matching against Intensity Variations and Noise Using Eigen Decomposed Templates
Koutaki, G., & Uchimura, K. (2011) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.131.1625 · Cited by 4
Ortho-diffusion decompositions of graph-based representation of images
Gudivada, S., & Borş, A. G. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.022 · Cited by 4
Stereo Correspondence with Local Descriptors for Object Recognition
Jison, G.-S. (2011) · InTech eBooks · doi:10.5772/12886 · Cited by 4
Realistic Smile Expression Recognition Using Biologically Inspired Features
He, C., Mao, H., & Jin, L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25832-9_60 · Cited by 4
A forensic method for detecting image forgery
Mahalakshmi, S. D., Vijayalakshmi, K., & Agnes, E. (2013) · IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology · doi:10.1109/ice-ccn.2013.6528568 · Cited by 4
Anusha, T. R., Hemavathi, N., Mahantesh, K., et al. (2014) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i.2014.7019774 · Cited by 4
Lobnig, K., Tscharf, A., & Mayer, G. (2015) · BHM Berg- und Hüttenmännische Monatshefte · doi:10.1007/s00501-015-0388-8 · Cited by 4
Generalizing objects by analyzing language
Tamošiūnaitė, M., Markelić, I., Kulvičius, T., & Wörgötter, F. (2011) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2011.6100812 · Cited by 4
Edge-based LBP description of surfaces with colorimetric patternsPreprint
Thompson, E. M., & Biasotti, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.03977 · Cited by 4
A spatially aware generative model for image classification, topic discovery and segmentation
González-Díaz, I., García-García, D., & Díaz-de-María, F. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414236 · Cited by 4
A Review of Camouflaged Target Detection Research
Wu, T., Liu, H., Zhu, J., et al. (2021) · IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) · doi:10.1109/imcec51613.2021.9482239 · Cited by 4
Bhuyan, S., Sen, D., & Deb, S. (2022) · IET Image Processing · doi:10.1049/ipr2.12399 · Cited by 4
Object based Image Retrieval from Database using Combined Features
Kavitha, H., & Sudhamani, M. V. (2013) · International Journal of Computer Applications · doi:10.5120/13270-0798 · Cited by 4
An image processing method to recognize position of sawn boards within the log
Li, X., Pot, G., Ngo, P., et al. (2023) · Wood Science and Technology · doi:10.1007/s00226-023-01495-1 · Cited by 4
Classification of Digital Chess Pieces and Board Position using SIFT
Kong, B. S., Hipiny, I., & Ujir, H. B. (2021) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa52582.2021.9576797 · Cited by 4
Costa, A. F., Traina, A. J. M., & Traina, C. (2014) · ACM Symposium on Applied Computing · doi:10.1145/2554850.2554868 · Cited by 4
Object matching using feature aggregation over a frame sequence
Bassiouny, M., & El-Saban, M. (2011) · Proceedings · doi:10.1109/wacv.2011.5711489 · Cited by 4
Tscharf, A. (2016) · BHM Berg- und Hüttenmännische Monatshefte · doi:10.1007/s00501-016-0527-x · Cited by 4
Image-based nutrition composition analysis with a local orientation descriptor
Chen, J.-C., Lin, K. W., Ting, C.-W., et al. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844893 · Cited by 4
Passenger monitoring in moving bus video
Leoputra, W. S., Venkatesh, S., & Tan, T. (2008) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2008.4795606 · Cited by 4
Facial feature localization usingweighted vector concentration approach
Kozakaya, T., Shibata, T., Yuasa, M., et al. (2008) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2008.4813360 · Cited by 4
Fast dense 3D reconstruction using an adaptive multiscale discrete-continuous variational method
Kang, Z., & Medioni, G. G. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836118 · Cited by 4
Underwater Photogrammetry and Visual Odometry
Royer, J., Pasquet, J., Merad, D., & Drap, P. (2018) · MDPI eBooks · doi:10.3390/books978-3-03842-685-1-12 · Cited by 5
Color palette generation for image classification by bag-of-colors
Kojima, A., & Ozeki, T. (2015) · Korea-Japan Joint Workshop on Frontiers of Computer Vision · doi:10.1109/fcv.2015.7103734 · Cited by 4
Feature Extraction for Face Recognition Using Haar Cascade Classifier
Diyasa, I. G. S. M., Putra, A. H., Ariefwan, M. R. M., et al. (2022) · Nusantara Science and Technology Proceedings · doi:10.11594/nstp.2022.2432 · Cited by 4
NCTR: Neighborhood Consensus Transformer for Feature Matching
Lu, X., & Ду, С. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897245 · Cited by 4
Construction of hybrid visual map for indoor SLAM
Ahn, S., Chung, W. K., & Oh, S. (2007) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2007.4399274 · Cited by 4
Ma, S., Li, X., Liu, K., et al. (2024) · Entropy · doi:10.3390/e26010061 · Cited by 4
Research on Image Classification of RBF Kernel SVM
Yan, N., & Xu, Z. (2024) · Academic Conference of China Instrument and Control Society (ACCIS) · doi:10.1109/accis62068.2024.10948583 · Cited by 5
“Lowe [6] introduced the Scale Invariant Feature Transform (SIFT) method in 2004, offering a solution that remains unaffected by changes in scale and rotation.”
Image Feature Matching Based on Semantic Fusion Description and Spatial Consistency
Zhang, W., & Zhang, G. (2018) · Symmetry · doi:10.3390/sym10120725 · Cited by 4
Recognizing faces in news photographs on the web
Zitouni, H., Bulut, M. F., & Duygulu, P. (2009) · International Symposium on Computer and Information Sciences · doi:10.1109/iscis.2009.5291920 · Cited by 4
An efficient intensity‐based ready‐to‐use X‐ray image stitcher
Wang, J., Zhang, X., Sun, Z., & Yuan, F. (2018) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.1925 · Cited by 4
An Innovative SIFT‐Based Method for Rigid Video Object Recognition
Yu, J., Zhang, F., & Xiong, J. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/138927 · Cited by 4
Liu, H., Zhang, Z., Su, H., et al. (2025) · River · doi:10.1002/rvr2.114 · Cited by 4
Two‐step mutual information‐based stereo matching
Heo, Y. S. (2016) · Electronics Letters · doi:10.1049/el.2016.0838 · Cited by 4
Relevance Metrics for Coverage Extension Using Community Collected Cell-phone Camera Imagery
Kansal, A., Xiao, L., & Zhao, F. (2006) · Sensor Networks and Applications · Cited by 4
Comparing Images Based on Histograms of Local Interest Points
Nowak, T., Gabryel, M., Korytkowski, M., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-642-55224-3_40 · Cited by 4
A new approach to traffic sign recognition through primary visual characteristics
Deshpande, A. V., & Subashini, M. M. (2017) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/ipact.2017.8244955 · Cited by 4
An improved active shape model method for facial landmarking based on relative position feature
Chen, H., Gao, M., & Fang, B. (2016) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s0219691317500084 · Cited by 4
Bayesian place recognition based on bag of objects for intelligent vehicle localisation
Wang, X., Hu, Z., Tao, Q., Huang, G., & Mu, M. (2019) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2018.5431 · Cited by 4
Robust multi-source co-adaptation with adaptive loss minimization
Tao, J., Dan, Y., & Zhou, D. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116455 · Cited by 4
Estimating the Number of Correct Matches Using Only Spatial Order
Talker, L., Moses, Y., & Shimshoni, I. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2869560 · Cited by 4
A Particle Filter Framework for Object Tracking Using Visual-Saliency Information
Mahapatra, D., & Saini, M. K. (2013) · Springer eBooks · doi:10.1007/978-3-642-41512-8_7 · Cited by 4
Modified Scale Invariant Feature Transform in omnidirectional images
Wang, Y., Xia, G., Zhu, Q., et al. (2009) · International Conference on Mechatronics and Automation · doi:10.1109/icma.2009.5246708 · Cited by 4
An improved bag of words method for appearance based visual loop closure detection
Zhu, H., Xie, L., Yu, H., & Liujun, W. (2018) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2018.8408123 · Cited by 4
Neural Outlier Rejection for Self-Supervised Keypoint LearningPreprint
Tang, J., Kim, H., Guizilini, V., Pillai, S., & Ambruş, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10615 · Cited by 27
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking
Liu, X., Liu, X., Yi, Z., Zhou, X., Le, T. H., Zhang, L., Huang, Y., Yang, Q. K., & Fan, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01870 · Cited by 4
Tang, Y., Wang, C., Zhang, H., et al. (2013) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2013.6723596 · Cited by 4
Design and preliminary study of custom laser scanning cystoscope for automated bladder surveillance
Yoon, W. J., Brown, M. A., Reinhall, P. G., et al. (2012) · Minimally Invasive Therapy & Allied Technologies · doi:10.3109/13645706.2011.653374 · Cited by 4
Ensemble classifier for joint object instance and category recognition on RGB-D data
Seib, V., Memmesheimer, R., & Paulus, D. W. R. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350776 · Cited by 4
MonoGhost: Lightweight Monocular GhostNet 3D Object Properties Estimation for Autonomous Driving
El-Dawy, A., El-Zawawi, A., & El-Habrouk, M. (2023) · Robotics · doi:10.3390/robotics12060155 · Cited by 4
Lv, G., Teng, S. W., Lu, G., et al. (2013) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2013.6607629 · Cited by 4
Top down saliency detection via Kullback-Leibler divergence for object recognition
Liang, J. S., & Zhang, Y. (2015) · doi:10.1109/isbb.2015.7344958 · Cited by 4
Efficient Pairwise Neuroimage Analysis Using the Soft Jaccard Index and 3D Keypoint Sets
Chauvin, L., Kumar, K., Desrosiers, C., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3123252 · Cited by 4
Color and Local Maximum Edge Patterns Histogram for Content Based Image Retrieval
Jasmine, K. P., & Kumar, P. R. (2014) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2014.11.09 · Cited by 4
An efficient edge-feature constraint visual SLAM
Liu, W., Mo, Y., & Jiao, J. (2019) · International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC) · doi:10.1145/3371425.3371455 · Cited by 4
Dai, F., & Zhu, Z. (2014) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0119-5 · Cited by 4
Logo Spotting on Document Images using Local Features
Le, V. P., Dang, Q. B., & Tran, C. D. (2015) · International Symposium on Information and Communication Technology · doi:10.1145/2833258.2833292 · Cited by 4
ASN: Image Keypoint Detection from Adaptive Shape Neighborhood
Ouellet, J.-N., & Hébert, P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_35 · Cited by 4
Content-Based Search in Multilingual Audiovisual Documents Using the International Phonetic Alphabet
Quénot, G., Tan, T.-P., Le, V. B., et al. (2009) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2009.42 · Cited by 4
Active Multitask Learning using Supervised and Shared Latent Topics
Acharya, A., Mooney, R. J., & Ghosh, J. (2016) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789813144552_0003 · Cited by 4
A Local Shape Descriptor Designed for Registration of Terrestrial Point Clouds
Tao, W., Lu, T., Chen, X., et al. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3394037 · Cited by 4
Discriminative local binary pattern
Kobayashi, T. (2016) · Machine Vision and Applications · doi:10.1007/s00138-016-0780-8 · Cited by 4
Augmenting SIFT with 3D Joint Differential Invariants for multimodal, hybrid face recognition
Cadoni, M. I., Lagorio, A., & Grosso, E. (2013) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2013.6712746 · Cited by 4
Recognizing 3D objects in cluttered scenes using projection images
Zarpalas, D., Kordelas, G., & Daras, P. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116642 · Cited by 4
A Fast Video Stabilization System Based on Speeded-up Robust Features
Zhou, M., & Asari, V. K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24031-7_43 · Cited by 4
Object Detection To Enable Autonomous Vessels On European Inland Waterways
Billast, M., Janssens, R., Vanneste, A., et al. (2022) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon49645.2022.9969042 · Cited by 4
Haase, D., & Denzler, J. (2013) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2013-45 · Cited by 4
SIFT Features for Deep and Variational Autoencoders: A Performance Comparison
Barreto, F. T. R., Yadav, S., Patnaik, S., et al. (2020) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icacccn51052.2020.9362843 · Cited by 4
Gao, Z., Wang, L., Jojić, N., Niu, Z., Zheng, N., & Hua, G. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2866114 · Cited by 4
A binary classification and online vision
Osman, H. E. (2009) · doi:10.1109/ijcnn.2009.5178674 · Cited by 4
Metadata based need-to-know view in large-scale video surveillance systems
Sultan, S., & Jensen, C. D. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102452 · Cited by 6
Object identification and tracking for steady registration in mobile augmented reality
Lehiani, Y., Preda, M., Maïdi, M., et al. (2015) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa.2015.7412163 · Cited by 4
Multiperson Interactive Activity Recognition Based on Interaction Relation Model
Tu, H., Xu, R., Chi, R., et al. (2021) · Journal of Mathematics · doi:10.1155/2021/5576369 · Cited by 4
Augmented Reality System for Accelerometer Equipped Mobile Devices
Skoczewski, M., & Maekawa, H. (2010) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2010.140 · Cited by 4
Region Based Visual Object Categorization Using Segment Features and Polynomial Modeling
Fu, H., Pujol, A., Dellandréa, E., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89689-0_32 · Cited by 4
Bülthoff, H., Wallraven, C., & Giese, M. A. (2016) · Springer handbooks · doi:10.1007/978-3-319-32552-1_78 · Cited by 4
Learning to Compose Hypercolumns for Visual CorrespondencePreprint
Min, J., Lee, J., Ponce, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10587 · Cited by 4
3D Surfel Map-Aided Visual Relocalization with Learned Descriptors
Ye, H., Huang, H., Hutter, M., Sandy, T., & Liu, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561005 · Cited by 5
A Speeded-Up Local Descriptor for dense stereo matching
Zhao, G., Chen, L., & Chen, G. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414314 · Cited by 4
Qing-ji, G., Yue, Y., & Guoqing, Y. (2008) · doi:10.1109/csse.2008.606 · Cited by 4
Probabilistic visual verification for robotic assembly manipulation
Choi, C., & Rus, D. L. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487786 · Cited by 4
On the use of feature tracks for dynamic concept detection in video
Mezaris, V., Dimou, A., & Kompatsiaris, I. Y. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5653867 · Cited by 4
Image registration method using Harris Corner and modified Hausdorff distance with near set
Biswas, B., Chakrabarti, A., & Dey, K. N. (2015) · IEEE International Conference on Recent Trends in Information Systems · doi:10.1109/retis.2015.7232911 · Cited by 4
RESEARCH PROGRESS ON FEATURE MATCHING OF SAR AND OPTICAL IMAGES
Qian, H., Yue, J. W., & Chen, M. (2020) · 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-3-w10-77-2020 · Cited by 4
Investigation of image registration method for the multi-directional image fusion of woven fabrics
Wang, W., Deng, N., Xin, B., et al. (2019) · Journal of the Textile Institute · doi:10.1080/00405000.2019.1652038 · Cited by 4
Robust feature description and matching using local graph
Lee, M. H., & Park, I. K. (2013) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2013.6694289 · Cited by 4
Delta Complexes in Digital Images. Approximating Image Object ShapesPreprint
Ahmad, M. Z., & Peters, J. F. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04549 · Cited by 4
Improving Integrality of Detected Moving Objects Based on Image Matting
Zhang, L., Wang, H., Deng, T., et al. (2014) · Chinese Journal of Electronics · doi:10.23919/cje.2014.10851933 · Cited by 4
Vasudevan, S., & Siegwart, R. (2007) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010038418 · Cited by 4
Consensus multi-view spectral clustering network with unified similarity
Zhao, Y., Zhu, D., Yuan, A., et al. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107647 · Cited by 4
Merino, I., Azpiazu, J., Remazeilles, A., et al. (2019) · International Journal of Artificial Intelligence & Applications · doi:10.5121/ijaia.2019.10601 · Cited by 4
Nguyen, A., & Yoshitaka, A. (2019) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2019.8868828 · Cited by 4
A two‐stage substation equipment classification method based on dual‐scale attention
Yao, Y., Wang, X., Zhou, G., & Wang, Q. (2024) · IET Image Processing · doi:10.1049/ipr2.13091 · Cited by 6
On Model-Free Re-Ranking for Visual Place Recognition With Deep Learned Local Features
Pivoňka, T., & Přeučil, L. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3404564 · Cited by 4
“Most of the model-free approaches apply generalized Hough transform [18] to measure the similarity between images.”
MethodVirtualization of heterogeneous and adaptive multi-core/multi-board systems
Oey, O., Werner, S., Göhringer, D., et al. (2012) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 4
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
