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 42,601–42,700 of 56,200
Asset identification using image descriptors
Friedel, R., Figuerola, O., Kalva, H., et al. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1688-1 · Cited by 1
Rawlinson, D., & Jarvis, R. A. (2008) · Cited by 1
A renaissance of explicit motion information mining from transformers for action recognition
Zhuang, P., Bai, L., Wu, Y., et al. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112645 · Cited by 1
“Given the conceptual similarity between the a ffi nity matrix and traditional local feature descriptors in video understanding [57, 58], it is promising to reutilize the informative displacement field through careful designs to better align with the characteristics of these classical descriptors.”
A Hierarchical Non-Parametric Method for Capturing Non-Rigid Deformations
Ecker, A., & Ullman, S. (2005) · doi:10.1109/crv.2005.6 · Cited by 1
A Review of Point Cloud Alignment Methods and Their Applications
Wu, J., & Zhang, M. (2024) · Academic Journal of Science and Technology · doi:10.54097/jg0q5p11 · Cited by 1
“The feature extraction stage aims to identify key feature points and their descriptors from the point cloud [15].”
BackgroundGutierrez, S. F. C., Watanabe, M., Ooyama, M., et al. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3575646 · Cited by 1
“Common feature descriptors include the Scale-Invariant Feature Transform (SIFT) [14] and ORB (Oriented FAST and Rotated BRIEF) [16].”
Optimized Laplacian Sparse Coding for Image Classification
Chen, L., Gao, S., Yuan, B., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21963-9_58 · Cited by 1
Fast k-means based on KNN GraphPreprint
Deng, C.-H., & Zhao, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.01813 · Cited by 1
DETR-SAM: Automated Few-Shot Segmentation With Detection Transformer and Keypoint Matching
Khanmohamadi, M., & Farahani, B. (2024) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins61597.2024.10622658 · Cited by 1
“The Scale-Invariant Feature Trans-form (SIFT) [24] is a robust algorithm for detecting and describing local features in images [25]–[27].”
BackgroundMethodVision-aided localization and navigation based on trifocal tensor geometry
Fang, Q., Zhang, D., & Hu, T. (2017) · International Journal of Micro Air Vehicles · doi:10.1177/1756829317708317 · Cited by 1
Object-based Image Retrieval using Local Feature Extraction and Relevance Feedback
Freitas, M. H. G., Pádua, F. L. C., & de Assis, G. T. (2013) · International Journal of Computer Applications · doi:10.5120/13499-1239 · Cited by 1
Spatio-temporal Features for Efficient Video Copy Detection
Hu, R., Li, B., Hu, W., & Yang, J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42057-3_16 · Cited by 1
A robust SIFT-based matching algorithm for the occluded area and complex texture
Shin, D. K., & Moon, Y. S. (2009) · doi:10.1109/cspa.2009.5069202 · Cited by 1
Robust Complex Salient Regions
Escalera, S., Pujol, O., & Radeva, P. I. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72849-8_15 · Cited by 1
Moving Path Estimation from Video Sequences
amasyali, F., & Münük, A. (2011) · DergiPark (Istanbul University) · doi:10.12739/nwsaes.v6i1.5000067033 · Cited by 1
Video based Activity Recognition in a Trauma Center
Elgammal, A., & Chakraborty, I. (2011) · doi:10.7282/t3vh5sbx · Cited by 1
Towards Fixation Prediction: A Nonparametric Estimation-Based Approach through Key-Points
Oliveira, S. A. F., Neto, A. R. R., & Gomes, J. P. P. (2016) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2016.077 · Cited by 1
Measurement of Ocular Torsion Using Feature Matching on the Iris and Sclera
Bell, A., Green, R., & Everitt, A. (2024) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz64857.2024.10794482 · Cited by 1
Robust Graph Neural Diffusion for Image Matching
She, R., Kang, Q., Wang, S., et al. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10223130 · Cited by 1
Infrared and Visible Image Matching Algorithm Based on SIFT and LDB
Zhang, L., Dai, M., & Tian, J. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9917-6_11 · Cited by 1
Mitchell, D. A., Rafaely, B., Kumar, A., et al. (2025) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-025-01247-2 · Cited by 1
“(15)).”
Nonparametric Divergence Estimation and its Applications to Machine Learning
Póczos, B., Xiong, L., & Schneider, J. (2018) · doi:10.1184/r1/6475940 · Cited by 1
Ridge and tree feature detection on images
Yurin, D. V., & Levashov, A. E. (2013) · Cited by 1
Depth Calculation using Computer Vision and Sift
Neto, E. C., Maya, H. C., Varela, A. T., et al. (2010) · Springer eBooks · doi:10.1007/978-90-481-9151-2_44 · Cited by 1
Ehsan, S., Clark, A. F., & McDonald-Maier, K. D. (2009) · International Conference on Computer and Electrical Engineering · doi:10.1109/iccee.2009.137 · Cited by 1
A hierarchical block matching algorithm in depth extraction for binocular vision
Liangbao, J., Jiao, C., Chen, R., et al. (2011) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1145/1968613.1968760 · Cited by 1
Motion-Feat: Motion Blur-Aware Local Feature Description for Image Matching
Gao, Y., Zhang, D., Yu, X., et al. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245810 · Cited by 1
Research on Evaluation Method of Aerial Image Segmentation Algorithm
Wang, H., Zhuang, C., Zhao, J., et al. (2022) · 2022 7th International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/icsip55141.2022.9886900 · Cited by 1
EDGE-BASED LOCALLY AGGREGATED DESCRIPTORS FOR IMAGE CLUSTERING
Dong, Y., Fan, D., Ma, Q., et al. (2018) · 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-3-303-2018 · Cited by 1
Textureless Surface Feature Point Detection via Micro-Geometry Reconstruction
Liang, Y., Wang, Y., Yan, T., Yang, J., Li, W., Huang, L., Ning, X., & Ataxonovich, K. T. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3681931 · Cited by 1
“(cid:2) Classical gradient-based methods: Shi-Tomasi [15], SIFT [5], SURF [16], FAST [6], ORB [7], BRISK [17], KAZE [8], AKAZE [18].”
Guzhva, N. S., Ali, B., Bakulev, K. S., et al. (2023) · Saint Petersburg International Conference on Integrated Navigation Systems · doi:10.23919/icins51816.2023.10168407 · Cited by 1
Multi-View Consistency for Correspondence Pruning
Yang, P. L., Peng, F., Liu, Y., et al. (2023) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia60676.2023.10429445 · Cited by 1
Segmentation-free object localization in image collections
Wang, S.-C., & Wang, Y.-C. F. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5582948 · Cited by 1
An Improved Extended Information Filter SLAM Algorithm Based on Omnidirectional Vision
Wang, J., & Chen, W. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/948505 · Cited by 1
3D Dense & Scaled Reconstruction Pipeline with Smartphone Acquisition
Thisse, Q., Houzet, D., & Adoux, J. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-08277-1_1 · Cited by 1
An FPGA processor for real-time, fixed-point refinement of CDVS keypoints
Lopez, G., Napoli, E., Meglio, D., et al. (2015) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2015.7169276 · Cited by 1
Innovative approaches in image processing: enhancing feature extraction and recognition capabilities
Yang, Z., Yu, Y., Huang, Y., et al. (2025) · The Visual Computer · doi:10.1007/s00371-025-03830-y · Cited by 1
Integrating kAS and SIFT-like Descriptor for Image Description
Shang, M., Pan, J. S., Pang, Y., et al. (2011) · International Conference on Image and Graphics · doi:10.1109/icig.2011.164 · Cited by 1
A Gradient Extension of Center Symmetric Local Binary Patterns for Robust RGB-NIR Image Matching
Saleem, S., Bais, A., & Sablatnig, R. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.150 · Cited by 1
KTRICT A KAZE Feature Extraction
Soni, B., Borah, A., Sowgandhi, P. N. L., et al. (2020) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2020040104 · Cited by 1
An Enhanced Local Texture Descriptor for Image Segmentation
Tania, S., Murshed, M., Teng, S. W., et al. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190895 · Cited by 1
Visual Odometry using the Global-appearance of Omnidirectional Images
Amorós, F., Payá, L., Valiente, D., Gil, A., & Reinoso, Ó. (2014) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0005062604830490 · Cited by 1
Detection Method of Malicious Mirroring Site in Mass Network Traffic
Che, H. (2020) · International Conference on Information Technologies and Electrical Engineering (ICITEE) · doi:10.1145/3452940.3453068 · Cited by 1
Kisku, D. R., Rattani, A., Gupta, P., et al. (2010) · BiblioBoard Library Catalog (Open Research Library) · doi:10.5772/intechopen.83935 · Cited by 1
A topology-based approach to computing neighborhood-of-interest points using the Morse complex
da Silva, R. D., Schwartz, W. R., Pedrini, H., Pulido, J., & Bernd Hamann, A. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.05.001 · Cited by 1
Fisher, R. B., Breckon, T. P., Dawson-Howe, K., Fitzgibbon, A., Robertson, C., Trucco, E., & Williams, C. K. I. (2016) · doi:10.1002/9781119286462.refs · Cited by 1
A Bag of Constrained Visual Words Model for Image Representation
Mukherjee, A., Sil, J., & Chowdhury, A. S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-32-9291-8_32 · Cited by 1
Discriminative Learning of Similarity and Group Equivariant RepresentationsPreprint
Trivedi, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.10078 · Cited by 1
MRIM-LIG at ImageCLEF 2009: Robotvision, Image Annotation and Retrieval Tasks
Pham, T.-T., Maisonnasse, L., Mulhem, P., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15751-6_42 · Cited by 1
Generic models for adaptive robust feature extraction in video
Cauwelaert, D. V. (2008) · Ghent University Academic Bibliography (Ghent University) · Cited by 1
Anomaly Detection of Filter Mesh Based on DifferNet and SSIM Algorithm
Xie, L., Qin, N., Du, Y., Liu, J., & Zhou, Q. (2022) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea54703.2022.10006256 · Cited by 1
Dense reconstruction of underwater scenes from monocular sequences of images
Rzhanov, Y., Hu, H., & Boyer, T. (2014) · doi:10.1109/oceans-taipei.2014.6964337 · Cited by 1
A New Stable and Accurate Algorithm of Large Image Mosaic
Cai, B., Liu, Z., Wang, J., et al. (2015) · International Journal of Signal Processing Image Processing and Pattern Recognition · doi:10.14257/ijsip.2015.8.6.23 · Cited by 1
Adaptive Compressive Tracking Algorithm Based on SIFT Features
Yang, H., Jiang, X., & Gao, S. (2015) · International Conference on Robot, Vision and Signal Processing · doi:10.1109/rvsp.2015.28 · Cited by 1
An improved classification approach for echocardiograms embedding temporal information
Li, W. (2016) · Middlesex University Research Repository (Middlesex University Of London) · Cited by 1
Guaranteed Localization and Mapping for Autonomous Vehicles
Wang, Z. (2018) · doi:10.70675/3b358d9ezaf18z4b47z8b54zcb342741b9a5 · Cited by 1
Recognition using SIFT and its Variants on Improved Segmented Iris
Pathak, A. (2013) · Cited by 1
Binary Multiresolution Wavelet based Algorithm for Face Identification
Joshi, S., & Kumar, A. (2014) · Cited by 1
Towards open ended learning: budgets, model selection, and representation
Perona, P., & Gomes, R. G. (2011) · doi:10.7907/t92x-dq05 · Cited by 1
Efficient Multi-stage Image Classification for Mobile Sensing in Urban Environments
Mujumdar, S., Rajamani, N., Subramaniam, L. V., & Porat, D. (2013) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2013.45 · Cited by 1
Magnetic Ultrathin Tissue Sections for Ease of Light and Electron MicroscopyPreprint
Köstinger, G., Düring, D. N., Holler-Rickauer, S., Leite, V., Yamahachi, H., Csúcs, G., & Hahnloser, R. H. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/532549 · Cited by 1
Maı̂tre, H. (2017) · doi:10.1002/9781119402442.biblio · Cited by 1
Jaenal, A., Cubero, P. C., Araújo, J., et al. (2025) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51701.2025.02486 · Cited by 1
“For training and validating Cross-Feature, we employed four well-known handcrafted and learned features: SIFT [25] (using the OpenCV extractor and the ko-rnia descriptor [38]), ORB [39] (using the implementation from ORB-SLAM2 [29]), the open-source implementation of SuperPoint [10] and the release…”
Mohapatra, A., Sarangi, S., Patnaik, S., & Sabut, S. K. (2014) · Journal of information and communication convergence engineering · doi:10.6109/jicce.2014.12.4.263 · Cited by 1
A Research on Fast Face Feature Points Detection on Smart Mobile Devices
Li, X., Zhang, X., & Wang, H. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/9729014 · Cited by 1
Scene categorization based on heterogeneous features
Lu, F., Yang, X., Zhang, R., & Yu, S. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.863481 · Cited by 1
B-spline based Motion Tracking of Cardiac Surface in Minimally Invasive Surgery
Pang, C., Wu, X., Lin, W. T., et al. (2017) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber.2017.8446305 · Cited by 1
Mobile robot localization using appearance based place recognition
Mitsuhashi, M., Sakai, A., & KURODA, Y. (2010) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2010.5723383 · Cited by 1
Moehrmann, J., Heidemann, G., Siemoneit, O., et al. (2010) · doi:10.5281/zenodo.1060378 · Cited by 1
RGB-D Visual Odometry Algorithm Based on Improved ORB for Indoor Environments
Ma, Q., & Li, W. (2019) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms46890.2019.8991543 · Cited by 1
Robust and light-weight simultaneous localization and mapping for autonomous vehicles
Wang, H. (2021) · doi:10.32657/10356/151717 · Cited by 1
Detection and Recognition for Multiple Flames Using Deep Learning
Chen, X. (2018) · Tuwhera (Auckland University of Technology) · Cited by 1
Progressive Image Registration Based on Probability Boosting Tree
Guo, L., Yi, J., & Yang, X. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33478-8_52 · Cited by 1
Abing: Adjusted Binarized Normed Gradients for objectness measure
Tian, W.-J., Zhao, Y. J., & Yuan, Y. (2014) · doi:10.1109/icosp.2014.7015209 · Cited by 1
Fast-match: Fast and robust feature matching on large images
Arnfred, J. T., & WINKLER, S. S. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351353 · Cited by 1
Beugher, S. D., Brône, G., & Goedemé, T. (2018) · Advances in interaction studies · doi:10.1075/ais.10.08beu · Cited by 1
Real time Object Distance Measurement System with tracking based on Stereo Vision
Govindiya, B. N., & Shaikh, N. (2015) · Cited by 1
Sequence image matching using adaptive SIFT under complex environmental conditions
Yan, C., Hao, Y., Zhang, D., et al. (2019) · International Conference on Digital Image Processing · doi:10.1117/12.2539675 · Cited by 1
DVD: Constructing a Discriminative Video Descriptor by Convolving Frame Features
Yang, B., Chen, Y., He, W., & Xiang, J. (2018) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2018.8499251 · Cited by 1
Pose Estimation for Rover-to-Lander Tube Transfer for Mars Sample Return
Mavrakis, N., Pham, T.-H., & Bailey, P. (2023) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero55745.2023.10115908 · Cited by 3
Reconstruction of indoor scene from a single image
Wu, D., Li, H., & Zhang, L. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2179370 · Cited by 1
Real Time Non-rigid Surface Detection Based on Binary Robust Independent Elementary Features
Kuo, C.-T., Tsai, L. Y., Feng, M. Y., et al. (2014) · International Symposium on Computer, Consumer and Control · doi:10.1109/is3c.2014.98 · Cited by 1
Monocular SLAM for a Small-Size Humanoid Robot
Wang, Y.-T., Hung, D.-Y., & Cheng, S.-H. (2011) · Tamkang University Institutional Repository (TKUIR) · doi:10.6180/jase.2011.14.2.05 · Cited by 1
Unsupervised Deep Infrared and Visible Homography Estimation Algorithm Based on Content-Aware
Liao, Y. Y., Luo, Y., & Wang, X. (2023) · International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR) · doi:10.1145/3656766.3656834 · Cited by 1
Cupec, R., Nyarko, E. K., Kitanov, A., et al. (2009) · European Conference on Mobile Robots · Cited by 1
Tohl, D., Li, J., & Bull, C. M. (2015) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0005317306230629 · Cited by 1
Browsing Information Overloading Unstructured Multimedia Social Network Contents on Mobile Devices
Chen, C. W. (2016) · Electronic Imaging · doi:10.2352/issn.2470-1173.2016.11.imawm-458 · Cited by 1
Robust image watermarking using local invariant features and independent component analysis
Hanling, Z., & Liu, J. (2006) · Wuhan University Journal of Natural Sciences · doi:10.1007/bf02831910 · Cited by 1
Enhancing Visual Place Recognition with Semantic Segmentation Filters
Zhou, Z., Yang, H., Liu, H., et al. (2024) · IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM) · doi:10.1109/cis-ram61939.2024.10672796 · Cited by 1
“In the past decades, hand-engineered encoding techniques, like Scale-Invariant Feature Transform (SIFT) [15], Speeded Up Robust Features (SURF) [16], and Oriented FAST and rotated BRIEF (ORB) [17], were used as feature extractors.”
MethodGeneric Merging of Structure from Motion Maps with a Low Memory Footprint
Flood, G., Gillsjö, D., Persson, P., et al. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412879 · Cited by 1
On the Coöperative Creation of Multimedia Meaning
Cusano, C., Santini, S., & Schettini, R. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10543-2_5 · Cited by 1
Liu, Y., Shi, Z., Wang, G., & Guan, H. (2012) · doi:10.1145/2390790.2390801 · Cited by 1
An Extended HOOFR SLAM Algorithm Using IR-D Sensor Data for Outdoor Autonomous Vehicle Localization
Bouazzaoui, I. E., Chghaf, M., Rodríguez, S., et al. (2023) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-023-01975-3 · Cited by 1
Feature-based multi-epoch rock slope monitoring using images and terrestrial laser scans
Lucks, L., & Holst, C. (2026) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2026.100120 · Cited by 1
Information Technology of Realtime Optical Navigation based on Photorealistic Orthophoto Plan
Prystavka, P., & Sorokopud, V. (2021) · IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development (APUAVD) · doi:10.1109/apuavd53804.2021.9615409 · Cited by 1
End-to-end learning of keypoint detection and matching for relative pose estimationPreprint
Fond, A., Pero, L. D., Sivacki, N., & Paladini, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01085 · Cited by 1
Real-time near-duplicate web video identification by tracking and matching of spatial features
Park, K.-W., Heu, J.-U., Kim, B.-K., et al. (2013) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1145/2448556.2448633 · Cited by 1
Descriptor-Driven Keypoint Detection
Sluzek, A. S. (2018) · Digital Image Computing: Techniques and Applications · doi:10.1109/dicta.2018.8615841 · Cited by 1
Utility-based map reduction for ground and flight vehicle navigation
Steiner, T. J. (2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1
Co-occurrence Models for Image Annotation and Retrieval
Garg, N. (2009) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 1
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