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 3,760
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 2,360
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1998) · Journal of the ACM · doi:10.1145/293347.293348 · Cited by 2,290
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211479 · Cited by 2,030
Research works printed but not linked: 3
- Harris, C. and Stephens, M. 1988. Acombined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.
- Mikolajczyk, K. 2002. Detection of local features invariant to affine transformations, Ph.D. thesis, Institut National Polytechnique de Grenoble, France.
- Torr, P. 1995. Motion segmentation and outlier detection, Ph.D. Thesis, Dept. of Engineering Science, University of Oxford, UK.
Cited by
Sorted by citations
Items 54,601–54,700 of 56,204
Robust registration of serial cell microscopic images using 3D Hilbert scan search
Lai, Y., Kamata, S., & Fu, Z. (2017) · doi:10.23919/mva.2017.7986917
Scale and Rotation Invariant Image Feature Descriptor for Real-Time Video AnalysisPreprint
Mohammad, K., & Assafrah, I. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4600028
Case Studies of LAM Process Monitoring and Control
Zhu, K., & Fuh, Y.-H. J. (2026) · Additive manufacturing technology · doi:10.1007/978-981-95-5226-9_11
Improving the Performance of CBIR with Genetic Approach and Feedback
Bourass, Y., Bahri, A., & Zouaki, H. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-287-990-5_29
Social Image Tag Ranking by Two-View Learning
Zhuang, J., & Hoi, S. C. H. (2011) · Springer eBooks · doi:10.1007/978-0-85729-436-4_3
Fine-tuning SIMPLE based Content Based Image Retrieval system
Vu, V.-H., Le, H.-S., Kanishcheva, O., et al. (2015) · International Symposium on Information and Communication Technology · doi:10.1145/2833258.2833273
SfM ToAEval: Trade-off Aware Evaluation of Feature Extraction Algorithms in Structure from Motion
Taha, D., El-Mashad, S. Y., & Zayed, H. H. (2024) · Engineering Research Journal · doi:10.21608/erjsh.2024.245661.1242
“…(like AGAST [17], FAST [18], and STAR [19]), feature description only (like BEBLID [20], BRIEF [21], DAISY [22], FREAK [23], LATCH [24], LUCID [25], TEBLID [26], and VGG [27]), or both feature detection and description (like AKAZE [28], BRISK [29], KAZE [30], ORB [31], SIFT [32], and SURF [33]).”
BackgroundMingqiang, 周. Z., Xiaobo, 杨. Y., Yitong, 王. W., et al. (2026) · Laser & Optoelectronics Progress · doi:10.3788/lop260507
Fashion Outfit Composition Combining Sequential Learning and Deep Aesthetic Network
Wang, Z., & Quan, H. (2019) · doi:10.1109/ijcnn.2019.8852389
Fast tunnel monitoring video stabilization method based on improved ORB feature
Mm, Z., Sun, J., Dihua, S., et al. (2018) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2018.8408155
Deep Combined Image Denoising with Cloud Images
Xia, S., Liu, J., Yang, W., Li, M., & Guo, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77380-3_73
DETECTING FEATURES VIA MODIFIED HARRIS CORNERS AND MATCHING THEM THROUGH SIFTPreprint
Pal, J. B. (2020) · doi:10.31219/osf.io/j3g9w
Khatwani, B., Jain, N., Gharal, K., et al. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-26376-6_32
Planar-SfM: Camera Pose Estimation via Homography Graph Embeddings
Pragier, G., Karklinsky, M., Ungarish, D., & Ben-Cohen, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31654-7_10
Ensemble Image Classification Using Genetic Image Network as Weak Classifiers
Nakayama, S., Shirakawa, S., Yata, N., et al. (2011) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.131.958
Detection of advertising spaces from audiovisual scenes to product placement
Zúñiga, J. C. Z., Hidalgo, J. A. M., & Herrera, J. L. A. (2014) · Sistemas y Telemática · doi:10.18046/syt.v12i28.1751
Improved method of reducing mismatching in manifold template matching
Okugawa, K., & Hashimoto, M. (2018) · International Workshop on Advanced Image Technology (IWAIT) · doi:10.1109/iwait.2018.8369776
Visualization and Interactive Exploration of Factorial Correspondence Analysis Results on Images
Pham, N.-K., Morin, A., & Gros, P. (2008)
Combining Self-Aggregation Vector and Indicator Matrix for Improved Object Retrieval
Zhang, Y., Wang, X., Pang, S., et al. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8997093
Song, M., Zhang, H., Fan, Y., et al. (2026) · Remote Sensing of Environment · doi:10.1016/j.rse.2026.115612
Remote sensing aircraft small object detection algorithm based on YOLOv5
Qiu, Y., Xue, J., Zhang, J., et al. (2024) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1117/12.3034165
Bailer, W., Sorschag, R., Lee, F., et al. (2011) · TRECVID
The 6-DOF Pose Estimation Algorithm Based on YOLO11-BiFormer-Robust ICP Model
Wang, G., Hou, C., Li, L., et al. (2025) · International Conference on Communication Software and Networks · doi:10.1109/iccsn66511.2025.11172584
“Traditional pose estimation methods mainly rely on feature point matching and model registration, such as algorithms based on SIFT[1], SURF[2], and other local features.”
Hu, X., Zhang, H., Deng, X., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38470-6_36
Reconstruction of the Moving World: a Review of Modern Methods for Dynamic and Deformable 3D Scenes
Nikolov, V., & Christoff, N. (2026) · International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) · doi:10.1109/icest71230.2026.11623603
A Novel Methodology for High Resolution Sea Ice Motion Estimation
Kaplan, K., & Kambhamettu, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37731-0_24
Fluid non-rigid registration combining SIFT and shape information for prostate treatment
Zhu, S., Zhang, S., Lu, X., et al. (2010) · doi:10.1109/miaca.2010.5528416
U-CATCH: Using Color ATtribute of image patCHes in binary descriptors.Preprint
Yılmaz, Ö., & Abdulkhaev, A. (2016) · arXiv (Cornell University)
Engineering Scene Environment Modeling Method Based on Improved FAST-LIVO
Bian, Y., Xie, L., Yu, S., et al. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-981-95-6911-3_28
A Review of Learning Based Visual Relocalization Methods
Lin, H., Wang, Y., Yin, B., & Yang, X. S. (2025) · Computational Visual Media · doi:10.26599/cvm.2025.9450492
Multi-Factors Impacting Object Detection Models' Performance in Medical Image Analysis
Alzahrani, R., & Bui, H. (2026) · doi:10.1109/icmlas67792.2026.11483935
Cheng, H. (2015) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-6714-3_7
Dahua, 李. L., Wenpeng, 郑. Z., Xuan, 李. L., et al. (2025) · Laser & Optoelectronics Progress · doi:10.3788/lop241592
Yin, L.-H., Wang, X., & Xie, J.-R. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2245977
Robot Homing with Uniformly Distributed Features
Zhu, Q., Liu, X., & Cai, C. (2013) · ROBOT · doi:10.3724/sp.j.1218.2013.00544
Deep learning based stereo cost aggregation on a small dataset
Wu, R., Sun, C., Liu, Z., et al. (2021) · Digital Image Computing: Techniques and Applications · doi:10.1109/dicta52665.2021.9647104
Image-Registration-Based Local Noise Reduction for Noisy Video Sequences
Jiang, N., Si, J., & Abousleman, G. P. (2007) · International Conference on Integration of Knowledge Intensive Multi-Agent Systems · doi:10.1109/kimas.2007.369828
Camera self-calibration network based on face shape estimation
Liu, Y., & Cui, Y. (2023) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms60103.2023.10347625
Energy Interpolated Template Coding for Video Compression in Traffic Surveillance Application
Patil, S. P., Sanyal, R., 2Belgacom International Carrier Services, B., et al. (2018) · Journal of Mobile Multimedia · doi:10.13052/jmm1550-4646.1431
Transfer sequential Monte Carlo: A framework for Bayesian model transfer
Bretherton, A. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.240788
Tsukanova, A. (2019) · theses.fr (ABES) · doi:10.70675/1cc931a0z724ez4285z85d2z7dd4bd0e2456
Point Clouds Registration Algorithm Based on Spatial Structure Similarity of Visual Keypoints
Gao, Y., Ma, J., Wu, B., et al. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-1253-5_1
Saliency map estimation by constructing graphs of possible eye-tracking paths
Szalai, S., Szirányi, T., & Vidnyánszky, Z. (2012) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom.2012.6421969
Learning human body and human action representations from visual data
Varol, G. (2019) · theses.fr (ABES) · doi:10.70675/eb8e5affza34fz4cf2z80d8z768d30b00422
Multi-view Point Cloud Registration Using Affine Shape Distributions
Du, J., Xiong, W., Chen, W., Cheng, J., Wang, Y., Gu, Y., & Chia, S.-C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16808-1_11
Creating Photo Maps with an Aerial Vehicle in USARsim
Bülow, H., Birk, A., & Feyzabadi, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-11876-0_4
Learning feature description network for image and depth map matching
Zhang, B., Cao, C., & Fei, L. (2025) · International Conference on Geoscience and Remote Sensing Mapping (GRSM) · doi:10.1117/12.3057500
“However, due to the difficulty of spatial registration of multimodal remote sensing data, traditional methods such as SIFT [1] cannot meet the requirements of geometric processing of multimodal data in surveying, and there are still some technical bottlenecks in multimodal image matching.”
Video mosaicing of planer scenes using extended kalman filter
Bati, E., Gürbüz, Y. Z., & Alatan, A. A. (2013) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2013.6531508
Learning to Reconstruct and Segment 3D ObjectsPreprint
Yang, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09582
Local Feature Extraction in the Near Infra-Red Domain for Wildlife Mammals Tracking Purpose
Trnovszký, T., & Hudec, R. (2016) · Civil and Environmental Engineering · doi:10.1515/cee-2016-0005
Hung, P. T., & Thanh, T. M. (2026) · Scientific Reports · doi:10.1038/s41598-026-38991-w
“Scale-invariant feature transform (SIFT) The scale-invariant feature transform (SIFT) 28 detects distinctive local keypoints that are robust to scale and rotation changes.”
Contextual Self-Learning Approach for Continuous Object Detections in Expansive Visual Domains
Bharatia, D. V., & Bharatia, H. V. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227623
Pseudo-Choroidal Melanocytosis in Birdshot Chorioretinitis: A Case Series from a Prospective Cohort
Vienne-Jumeau, A., Memmi, C., Foulonneau, T., et al. (2026) · Ocular Immunology and Inflammation · doi:10.1080/09273948.2026.2689360
“11 The optic nerve head boundary was manually delineated as an ellipse and used as the normalization reference.”
Curvature-Adaptive Smoothing for Multi-View Industrial Metrology
Lee, J. K., Kwon, S. W., Park, H. G., et al. (2026) · Electronics · doi:10.3390/electronics15112360
“Within this pipeline, the Scale-Invariant Feature Transform (SIFT) has been widely adopted as a robust local feature descriptor invariant to scale and rotation [1–3], enabling stable correspondence establishment across viewpoints [4,5].”
Local Covariant Region Detection Based on Image Contour Corner
Xu, L., Yang, M., Wang, H., et al. (2012) · International Symposium on Information Science and Engineering · doi:10.1109/isise.2012.58
MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation
LIU, X., Shi, J., Dang, Z., & Dai, Y. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00844
“Early works use a detector-based approach first employing some well-established handcrafted features such as SIFT [52] and ORB [68] followed by a feature matching algorithm like nearest-neighbor searches.”
Robust Object Extraction Algorithm in the Sea Environment
Park, J., & Jeong, J.-M. (2014) · Journal of Korean institute of intelligent systems · doi:10.5391/jkiis.2014.24.3.298
3D computer graphic of Fuji's mountain by using triple stereo images
Suanpaga, W., Witayakul, W., & Chotichanathawewong, S. (2012) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2012.6223388
Nunes, G. M., da Cunha, C. N., & da Silva, N. (2025) · Cuadernos de Investigación Geográfica · doi:10.18172/cig.6801
Gao, S., Mondal, S., Zhu, N., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2079446
Semantic image retrieval system based on object relationships
Shivakumar, S., Goel, N., Ananthanarayana, V. S., et al. (2013) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip.2013.6707598
Improved Material Recognition Using Neural Networks
Awan, M., Shahzad, A., & Shin, D. R. (2018) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3292448.3292460
Multi-point measurement using DIC based on UAV platform
Han, S., & Zhu, J. (2023) · International Conference on Optical and Photonic Engineering (icOPEN) · doi:10.1117/12.2666682
Learning Image-to-Surface Correspondence
Güler, R. A. (2019) · theses.fr (ABES) · doi:10.70675/5c92c11bzafbaz442fz9accz878a6d7dbaf1
Wang, Z., Li, H., & Brenner, C. (2013) · Sensor Letters · doi:10.1166/sl.2013.2664
Weighted moments of SIFT-features for decoupled visual servoing in 6DOF
Hoffmann, F., Kahn, U., Krettek, J., et al. (2007) · Technische Universität Dortmund Eldorado (Technische Universität Dortmund) · doi:10.17877/de290r-748
Self-Supervised Marine Video Analysis via Siamese Network
Ju, L., Song, J., Li, Q., Shi, Z., Gu, Z., Zheng, H., & Zheng, B. (2021) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans44145.2021.9706129
Scientific zero to one: Some common properties of highly-influential papers
Yekta, M. M. J. (2021) · Malaysian Journal of Library & Information Science · doi:10.22452/mjlis.vol26no3.1
Remote Sensing and Sensors for EDS
(2021) · American Society of Civil Engineers eBooks · doi:10.1061/9780784415726.ch01
A Probabilistic Approach to the Primary Visual Cortex
Köster, U. (2009) · Työväentutkimus Vuosikirja
A patch-based local features and HOG integration model in context of visual categorization
Albadawi, M. A. E. M., & Nawari, M. (2015) · doi:10.1109/iccneee.2015.7381413
Positioning Method of Intelligent Vehicle Based on Visual Multi-Feature Primitive Fusion
Zhao, Y., Li, B., Han, Z., et al. (2022) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait56212.2022.10137936
A Method for Face Affine Registration via Rough Search and Fine Registration
Wu, L., Xiong, L., Zhang, S., et al. (2017) · International Conference on Communication and Information Systems (ICCIS) · doi:10.1145/3158233.3159356
CIM-SIFT: An Efficient Compute-in-Memory Accelerator for Real-time SIFT Algorithm
Yan, Z., Wang, C., Lin, X., et al. (2026) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas66217.2026.11562615
“…limitations: Gaus-979-8-3315-7769-8/26/$31.00 ©2026 IEEE 967 sian kernel sizes that range from 9 × 9 to 25 × 25 reported in the original SIFT paper [3] are constrained to only 5 × 5 , degrading the quality of extracted keypoints and descriptors, and analog circuits are more susceptible to process,…”
InfluentialCooperative vSLAM based on UAV application
Li, X., & Aouf, N. (2012) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2012.6491085
PLace-Aware Active Promotion (PLAP) Technique
Nam, Y.-H., & Lee, J. (2008) · doi:10.1109/iccit.2008.183
Toward Copyright Leakage Mitigation for Spherical Panoramic Images
Kang, J., & Hou, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3639974
Ran, Y., Yang, G., Zhang, D., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5420826
Co-occurrence context of the data-driven quantized local ternary patterns for visual recognition
Han, X., Chen, Y., & Xu, G. (2015) · IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2015.7486617
AdaBoost with "keypoint presence features" for real-time vehivle visual detection
Bdiri, T., Moutarde, F., Bourdis, N., et al. (2009) · HAL (Le Centre pour la Communication Scientifique Directe)
Large-Scale Approximate k-NN Graph Construction on GPUPreprint
Wang, H., Zhao, W., & Zeng, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.15386
Fan, B., Wang, Z., & Wu, F. (2015) · SpringerBriefs in computer science · doi:10.1007/978-3-662-49173-7_5
Offline Handwritten Signature Identification and Verification Using LBP Features
Gupta, C., Singh, B., Bharti, V., et al. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-69115-7_30
Content-Based Image Retrieval with Combined Features: Color and GradientPreprint
Kılıçaslan, M., & Demırcı, R. (2021) · Research Square · doi:10.21203/rs.3.rs-222235/v1
Low-Cost Stereo Vision: A Single-Camera Approach For Precise Robotic Perception
Marx, S., Gusenburger, J., Bashir, A., et al. (2024) · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711598
“These descriptors are then matched using a k-nearest neighbour search [21, 22], where false matches are filtered out with Lowe’s ratio test [23]”
MethodGmd: Gaussian mixture descriptor for pair matching of 3D fragments
Xiong, M., Shi, Z., Zhou, X., et al. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01519-1
Fast and Incremental 3D Model Renewal for Urban Scenes With Appearance Changes
Xiong, Y., & Zhou, Z. (2024) · Computer Animation and Virtual Worlds · doi:10.1002/cav.70004
GKDT: General Keypoint Detection Transformer
Lu, C., Chen, Y., Gui, H., Wang, R., Yang, J., Yang, H., van den Hengel, A., & Jia, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36846-1_11
Leon, L. M. C. (2012) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.45.2012.tde-31102013-094950
Evaluating the Lidar/HSI direct method for physics-based scene modeling
Givens, R. N., Walli, K. C., & Eismann, M. T. (2014) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2014.7041906
A Comprehensive Review of Vision-Based Fatigue Detection AlgorithmsPreprint
Tang, C., Liu, J., Xiong, J., Han, J., Cao, M. L., Chi, H., Yu, Z., Zhou, B., & Yang, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7152575
Vangah, W. J., Ouattara, S., Okaingni, J.-C., et al. (2018) · Advances and Applications in Discrete Mathematics · doi:10.17654/dm019030243
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
