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 32,801–32,900 of 56,201
A case study of BoVW for keyword spotting on historical Mongolian document images
Guo, X., Wei, H., & Su, X. (2016) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2016.7852739 · Cited by 4
Video Human Motion Recognition Using a Knowledge-Based Hybrid Method Based on a Hidden Markov Model
Suk, M., Ramadass, A., Jin, Y., et al. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168756 · Cited by 4
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
Spencer, J., Bowden, R., & Hadfield, S. (2019) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 4
Improved DSIFT Descriptor Based Copy-Rotate-Move Forgery Detection
Khayeat, A. R. H., Sun, X., & Rosin, P. L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29451-3_51 · Cited by 4
Performance Comparison of Feature Generation Algorithms for Mosaic Photoacoustic Microscopy
Le, T. D., Kwon, S. Y., & Lee, C. (2021) · Photonics · doi:10.3390/photonics8090352 · Cited by 4
Feature Detection Based on Significancy of Local Features for Image Matching
Kim, T. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edl8048 · Cited by 4
A 3D Scene Information Enhancement Method Applied in Augmented Reality
Li, B., Wang, X., Gao, Q., et al. (2022) · Electronics · doi:10.3390/electronics11244123 · Cited by 4
Torimitsu, S., Uemura, Y., Tsuneya, S., et al. (2025) · International Journal of Legal Medicine · doi:10.1007/s00414-025-03564-5 · Cited by 4
“…which can detect comprehensive features with high recognition accuracy, has better process - ing speed than classical keypoint extraction algorithms such as Scale-Invariant Feature Transformer (SIFT) [48 ] and Speeded-Up Robust Features (SURF) [49], and is robust to rotation and dilation [ 44 ].”
Feature-based visual odometry and featureless place recognition for SLAM in 2.5D environments
Milford, M. J., McKinnon, D. H., Warren, M., Wyeth, G. F., & Upcroft, B. (2011) · QUT ePrints (Queensland University of Technology) · Cited by 5
A Novel Image Enhancement Method for Palm Vein Images
Perera, K., Khelifi, F., & Belatreche, A. (2022) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit55151.2022.9804034 · Cited by 4
El, A., El, Y., Ruichek, Y., et al. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.061139 · Cited by 4
Spherical Panorama Image Watermarking Using Viewpoint Detection
Kang, J., Ji, S.-K., & Lee, H.-K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-11389-6_8 · Cited by 17
ATDT: Autonomous Template-Based Detection and Tracking of Objects from Airborne Camera
Sadeghi‐Tehran, P., & Angelov, P. P. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-11310-4_48 · Cited by 4
A unified framework for exploring time-varying volumetric data based on block correspondence
Lu, K., Wang, C., Wu, K., et al. (2019) · Visual Informatics · doi:10.1016/j.visinf.2019.10.001 · Cited by 4
Lower Facial Curves Extraction for Unconstrained Face Detection in Video
Sawat, D. D., & Hegadi, R. S. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-7901-6_75 · Cited by 4
ENHANCEMENT OF STEREO IMAGERY BY ARTIFICIAL TEXTURE PROJECTION GENERATED USING A LIDAR
Veitch-Michaelis, J., Müller, J., Walton, D., et al. (2016) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xli-b5-599-2016 · Cited by 4
Zhang, Y., Zhang, M., Xu, C., et al. (2024) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace61206.2024.10549209 · Cited by 4
“At present, the visual inertial fusion positioning system mostly adopts the visual front-end based on feature points, such as SIFT [10] , SURF [11], ORB [12] , etc.”
MethodAn improvement of the Canny edge based image expansion algorithm
Zhang, S., & Shi, H. (2017) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2017.8302046 · Cited by 4
Algorithm of Vehicle Trajectory Extraction using Fusion of Hu Moments and Grayscale Feature
Zhou, J., Zhao, X., Xu, Z., et al. (2014) · Information Technology Journal · doi:10.3923/itj.2014.1770.1778 · Cited by 4
A Fast Dense Feature-Matching Model for Cross-Track Pushbroom Satellite Imagery
Du, W.-L., Xiao-yi, L., Ye, B., et al. (2018) · Sensors · doi:10.3390/s18124182 · Cited by 4
Spatial weighted fisher vector for image retrieval
Qi, C., Shi, C., Xu, J., Wang, C., & Xiao, B. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019457 · Cited by 4
Multimodal Colored Point Cloud to Image Alignment
Rotstein, N., Bracha, A., & Kimmel, R. (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/cvpr52688.2022.00654 · Cited by 4
Efficient descriptors selection in automatic image retrieval using DENOL
Ignat, A., Luca, M., Păvăloi, I., et al. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-219275 · Cited by 4
An Automatic Image Stitching Method for Infrared Image Series
Zhi-jian, H., Hui, B., & Shujin, S. (2021) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais52680.2021.9624642 · Cited by 4
VD-Matcher: A Very Deep Local Feature Matcher With Weight Recycling and Keypoint Detection
Dai, K., Zhou, Z., Jiang, Z., et al. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3574735 · Cited by 4
“The detector-based pipeline, extensively explored in prior researches [5], [6], [7], [8], [9], [10], [11], follows a standard paradigm that includes feature detection, description, and matching.”
Comparative Analysis of Automated Bone Age Assessment Techniques
Saputri, M. S., Wibisono, A., Mursanto, P., Rachmad, J., Alberto, Yudasubrata, A. T. W., Rizki, F., & Anderson, E. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914274 · Cited by 4
A Training-Free Approach for Generic Object Detection
Baheti, B., Talbar, S. N., & Gajre, S. S. (2019) · IETE Journal of Research · doi:10.1080/03772063.2019.1611491 · Cited by 4
SRI-SURF: A better SURF powered by scaled-RAM interpolator on FPGA
Jia, X., Guo, K., Wang, W., et al. (2016) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.1109/fpl.2016.7577363 · Cited by 4
F-SURF Feature Descriptor for Video Copy Detection
Asha, S., & Sreeraj, M. (2014) · International Conference on Advances in Computing and Communications (ICACC) · doi:10.1109/icacc.2014.28 · Cited by 4
Liang, Z., Zeng, D., Guo, S., et al. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.01.022 · Cited by 4
Schulte, L., Faul, C., Oswald, P., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298285 · Cited by 4
“…could be that the adult images, unlike the larval images, were not taken under standardized conditions, and although the implemented SIFT operator should account for rotations and different views [44], in most cases the animal’s positions may have been too variable for successful matching.”
Individual identification of armadillos (Mammalia, Cingulata) using a photo-identification software
Gallo, J. A., Abba, A. M., & Superina, M. (2022) · Mammalian Biology · doi:10.1007/s42991-022-00260-4 · Cited by 4
Mozas‐Calvache, A. T., Gómez‐López, J. M., Pérez, J. L., et al. (2024) · Heritage · doi:10.3390/heritage7060137 · Cited by 4
Eswindnet: Image Demoiréing Using Multiscale Swin Transformer Layers
Alaa, K., & Torki, M. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084615 · Cited by 4
Temur, M. A., Kocaman, S., & Nefeslioğlu, H. A. (2024) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-024-03947-9 · Cited by 4
Automatic region of interest detection in tagged images
Sorschag, R., Mörzinger, R., & Thallinger, G. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202827 · Cited by 4
Infrared Long-distance Target Detection Based on Deep Learning
Yang, X., & Qiao, Y. (2022) · IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) · doi:10.1109/seai55746.2022.9832245 · Cited by 4
Feature Selection for Image Categorization
Xu, F., & Zhang, Y. (2006) · Lecture notes in computer science · doi:10.1007/11612704_65 · Cited by 4
Mobile Robot Automatic Aiming Method Based on Binocular Vision
Xiang, H., Cheng, L., Wu, H., et al. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9550047 · Cited by 4
Registration of Remote Sensing Images with Steerable Pyramid Transform and Robust SIFT Features
Liu, X., Tian, Z., Xu, H., et al. (2015) · Cited by 4
A New Accumulator-Based Approach to Shape Recognition
Krish, K., & Snyder, W. E. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89646-3_16 · Cited by 4
Formal verification of robustness and resilience of learning-enabled state estimation systems
Huang, W., Zhou, Y., Jin, G., Sun, Y., Meng, J., Zhang, F., & Huang, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127643 · Cited by 4
A homography-based inpainting algorithm for effective depth-image-based rendering
Shan, H., Chien, W., Wang, H., & Yang, J. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026095 · Cited by 4
Category-Separating Strategy for branded handbag recognition
Wang, Y., Li, S., & Kot, A. C. (2014) · International Symposium on Communications, Control and Signal Processing · doi:10.1109/isccsp.2014.6877816 · Cited by 4
Lavigne, D. A., Sahli, S., Ouyang, Y., & Sheng, Y. (2010) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2010.5712007 · Cited by 5
Multilayer-Pseudopolar Fractional Fourier Transform Approach for Image Registration
Li, Z., Yang, J., Lan, R., et al. (2012) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2012.79 · Cited by 4
Taira, R. K., Iglesias, J. E., & Jahanshad, N. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0385-3_5 · Cited by 4
EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE
Brocks, S., & Bareth, G. (2016) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xli-b5-785-2016 · Cited by 4
A survey of feature extraction techniques in content-based illicit image detection
Yaghoubyan, S. H., Maarof, M. A., Zainal, A. B., & Oghaz, M. M. (2016) · Anglia Ruskin Research Online (Anglia Ruskin University) · Cited by 4
Multiple Instance Choquet Integral for multiresolution sensor fusion
Du, X. (2017) · doi:10.32469/10355/63615 · Cited by 4
Content-Based Image Retrieval using Generated Textual Meta-Data
Huang, K. (2018) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3292448.3292457 · Cited by 4
Image recognition of individual cow based on SIFT in Lαβ color space
Feng, L., Zhang, C., & Lv, C. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201817601023 · Cited by 4
Feature-Assisted Sparse to Dense Motion Estimation Using Geodesic Distances
Ring, D., & Pitié, F. (2009) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.1109/imvip.2009.9 · Cited by 4
Point-context descriptor based region search for logo recognition
Fu, J., Wang, J., Zhang, Y., et al. (2012) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2382336.2382390 · Cited by 4
Robust Feature Matching via Graph Neighborhood Motion Consensus
Huang, J., Li, H., Gong, Y., et al. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3398266 · Cited by 4
Bell, J. M., Yagnik, G. B., Dettori, L. G., Carvalho, P., Wan, Z., Rothschild, K. J., & Lim, M. J. (2025) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.5c00057 · Cited by 5
Human and Sheep Facial Landmarks Localisation by Triplet Interpolated FeaturesPreprint
Yang, H., Zhang, R., & Robinson, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.04954 · Cited by 4
Toward semantic attributes in dictionary learning and non-negative matrix factorization
Babaee, M., Wolf, T., & Rigoll, G. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.06.020 · Cited by 4
Object detection based on HOG features: Faces and dual-eyes augmented reality
Hbali, Y., Sadgal, M., & Fazziki, A. E. (2013) · doi:10.1109/wccit.2013.6618716 · Cited by 4
Quasi-Dense Matching between Perspective and Omnidirectional Images
Lu, L., & Wu, Y. (2008) · Cited by 4
Hierarchical Hashing for Image Retrieval
Yan, C., Bai, X. P., Zhou, J. W., & Liu, Y. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7302-1_10 · Cited by 4
A vision-enhanced multisensor LBS suitable for augmented reality applications
Ruiz-Ruiz, A. J., Cánovas, Ó., & López-de-Teruel, P. E. (2013) · Journal of Location Based Services · doi:10.1080/17489725.2013.807026 · Cited by 4
Automatic remote sensing image registration based on SIFT descriptor and image classification
Zhu, Z., Luo, J., & Shen, Z. (2010) · International Conference on Geoinformatics · doi:10.1109/geoinformatics.2010.5567705 · Cited by 4
Cross, M. D. (2016) · ScholarWorks@BGSU (Bowling Green State University) · Cited by 4
IFD: An Intelligent Fast Detection for Real-Time Image Information in Industrial IoT
Zhang, H., Wang, Y., Liu, Y., et al. (2022) · Applied Sciences · doi:10.3390/app12157847 · Cited by 4
Automatic Image-to-Image Registration of Near-Real-Time SAR Images
Wessel, B., Huber, M., & Roth, A. (2007) · elib (German Aerospace Center) · Cited by 4
Distribution Fields with Adaptive Kernels for Large Displacement Image Alignment
Mears, B., Sevilla-Lara, L., & Learned-Miller, E. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.17 · Cited by 4
Object class and instance recognition on rgb-d data
Seib, V., Christ-Friedmann, S., Thierfelder, S., et al. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2049915 · Cited by 4
Salient region detection with opponent color boosting
Cao, G., & Cheikh, F. A. (2010) · European Workshop on Visual Information Processing · doi:10.1109/euvip.2010.5699129 · Cited by 4
Tracking landmarks for control of an electric wheelchair using a stereoscopic camera system
Viet, N. B., Hải, N. T., & Hùng, N. V. (2013) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc.2013.6698133 · Cited by 4
3D Scene Reconstruction Based on Uncalibrated Image Sequences
Wan, Y., & Miao, Z. (2009) · International Conference on Digital Image Processing · doi:10.1109/icdip.2009.60 · Cited by 4
Locally aggregated histogram-based descriptors
Lu, X., Yao, H., Sun, X., et al. (2017) · Signal Image and Video Processing · doi:10.1007/s11760-017-1161-0 · Cited by 4
Supervised Feature Learning Network Based on the Improved LLE for face recognition
Meng, D., Cao, G., Cao, W., et al. (2016) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2016.7846591 · Cited by 4
Detection of Multiple Instances of Video Objects
Bursuc, A., Zaharia, T. B., & Prêteux, F. (2011) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2011.74 · Cited by 4
Synthesis and Prototyping of a 6-dof Parallel Robot for the Automatic Performance of Assembly Tasks
Landuré, J., Gosselin, C., Laliberté, T., et al. (2022) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2022.p0125 · Cited by 4
GF-2 Panchromatic and Multispectral Remote Sensing Image Registration Algorithm
Wang, S., Wang, X., & Li, J. (2020) · IEEE Access · doi:10.1109/access.2020.3012555 · Cited by 4
Side-scan sonar based landmark detection for underwater vehicles
Hoff, S., Haraldstad, V., Hogstad, B. R., et al. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801447 · Cited by 4
Adaptive Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition
Li, A., Chen, J., Kang, B., Zhuang, W., & Zhang, X. (2019) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps45667.2019.9024585 · Cited by 4
Allam, A., Tadjine, M., Nemra, A., et al. (2017) · International Conference on Systems and Control (ICSC) · doi:10.1109/icosc.2017.7958700 · Cited by 4
Newsam, S. D., Leung, D., Caballero, O., et al. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869880 · Cited by 4
Li, G., Cai, J., Huang, C., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3547002 · Cited by 4
Nakamoto, S., & Toriu, T. (2011) · Cited by 4
SRPose: Two-View Relative Pose Estimation with Sparse Keypoints
Rui, Y., Zhang, Y., Pan, Z., Zhu, J., Wang, C., & Jia, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73010-8_6 · Cited by 5
Spatio-chromatic Opponent Features
Alexiou, I., & Bharath, A. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_6 · Cited by 4
SCK: A Sparse Coding Based Key-Point Detector
Hong-Phuoc, T., He, Y., & Guan, L. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451829 · Cited by 4
LATCH: Learned Arrangements of Three Patch CodesPreprint
Levi, G., & Hassner, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.03719 · Cited by 4
Evaluation of different SLAM algorithms using Google tangle data
Liu, L., Wang, Y., Zhao, L., et al. (2017) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2017.8283158 · Cited by 4
Relative pose estimation with efficient feature distribution in landing guidance
Liu, H., Liu, R., Qu, X., et al. (2025) · Journal of Control and Decision · doi:10.1080/23307706.2025.2574677 · Cited by 4
Mahajan, K., Sharma, M., & Vig, L. (2019) · doi:10.1109/icdarw.2019.30060 · Cited by 4
Experiments on Stereo Visual Odometry in Feature-Less Volcanic Fields
Otsu, K., Otsuki, M., & Kubota, T. (2014) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-07488-7_25 · Cited by 4
Natural feature tracking on a mobile handheld Tablet
Maïdi, M., Preda, M., Dailey, M. N., et al. (2013) · IEEE International Conference on Signal and Image Processing Applications (ICSIPA) · doi:10.1109/icsipa.2013.6708012 · Cited by 4
Cultural Heritage Resources Profiling
Ellefi, M. B., Drap, P., Papini, O., et al. (2018) · doi:10.1145/3184558.3191598 · Cited by 4
Unsupervised Discovery of Object Landmarks via Contrastive LearningPreprint
Cheng, Z., Su, J.-C., & Maji, S. (2020) · arXiv (Cornell University) · Cited by 4
Yaghoubi, S., Khalili, S., Nezhad, R. M., et al. (2012) · Cited by 4
Video Representation via Fusion of Static and Motion Features Applied to Human Activity Recognition
Arif, S., Wang, J., Fei, Z., & Hussain, F. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.07.015 · Cited by 4
Keypoint Detection and Matching on High Resolution Spherical Images
Gava, C. C., Hengen, J.-M., Taetz, B., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41914-0_36 · Cited by 4
Algorithm for Three-Dimensional Reconstruction of Nonrigid Objects Using a Depth Camera
Ruiz-Rodríguez, M. L., Kober, V. I., Карнаухов, В. Н., et al. (2020) · Journal of Communications Technology and Electronics · doi:10.1134/s1064226920060248 · Cited by 4
Compressed histogram attribute profiles for the classification of VHR remote sensing images
Battiti, R., Demir, B., & Bruzzone, L. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2195988 · Cited by 4
Joint utilization of local appearance and geometric invariants for 3D object recognition
Soysal, M., & Alatan, A. A. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1622-6 · 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