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 39,501–39,600 of 56,201
Machine Learning Applications in Computer Vision
Harandi, M. T., Taheri, J., & Lovell, B. C. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-1833-6.ch007 · Cited by 2
Lionnie, R., Andika, J., & Alaydrus, M. (2024) · Algorithms · doi:10.3390/a17110529 · Cited by 3
Learnable patchmatch and self-teaching for multi-frame depth estimation in monocular endoscopy
Shao, S., Pei, Z., Chen, W., Wu, X., & Liu, Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112463 · Cited by 5
A depth-incorporated 2D descriptor for robust and efficient 3D environment reconstruction
Zhang, K., Zheng, S., Yu, W., et al. (2015) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2015.7250334 · Cited by 2
Robust Duplicate Detection of 2D and 3D Objects
Vajda, P., Ivanov, I., Goldmann, L., et al. (2010) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/jmdem.2010070102 · Cited by 2
Learning Affective Features Based on VIP for Video Affective Content Analysis
Zhu, Y., Min, T., Huang, T., Wen, Z., & Tian, Q. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00764-5_64 · Cited by 2
Low complexity image matching using color based SIFT
Nagar, A., Saxena, A., Bucak, S. S., et al. (2013) · Visual Communications and Image Processing · doi:10.1109/vcip.2013.6706456 · Cited by 2
Building Recognition System Based on Improved SIFT Algorithm and Positioning Information
刘, 涛. (2021) · Journal of Image and Signal Processing · doi:10.12677/jisp.2021.103012 · Cited by 2
Contextualized Privacy Filters in Video Surveillance Using Crowd Density Maps
Fradi, H., Melle, A., & Dugelay, J. (2013) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2013.23 · Cited by 2
Guo, D., Teng, X., Guo, Y., et al. (2021) · ACM Transactions on Internet of Things · doi:10.1145/3450567 · Cited by 2
Dense Point Cloud Mapping Based on RGB-D Camera in Dynamic Indoor Environment
Zhang, F., Li, Q., Wang, T., et al. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9327342 · Cited by 2
A Novel DenseSIFT U-NET Based Approach to Perform Kidney Tumor Semantic Segmentation
Neha, F., & Bansal, A. K. (2023) · doi:10.1109/idicaiei58380.2023.10406429 · Cited by 2
Image feature extraction and matching of color-based scale-invariant feature transform
Yin-chu, W. U., & Rong, M. A. (2011) · Journal of Computer Applications · doi:10.3724/sp.j.1087.2011.01024 · Cited by 2
The method of InSAR/INS integrated navigation
Jiang, S., Maosheng, X., Wang, B., et al. (2016) · CIE International Conference on Radar (RADAR) · doi:10.1109/radar.2016.8059349 · Cited by 2
Phase-based fine-grained change detection
Wang, X., Wan, L., Lin, D., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120181 · Cited by 2
Classification of Oral Potentially Malignant Disorders Using Multimodal Feature Integration
Goswami, B., Neogi, S., Nagar, S. R., Punjabi, N., & Gudi, R. D. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980715 · Cited by 2
“Techniques like Hu Moments, Haralick texture features, and SIFT descriptors with Bag of Visual Words (BoVW) are effective in extracting spatial, texture, and keypoint-based features, respectively [2] [3] [4].”
Median based approaches for noise suppression and interest point detection.
Miao, Z. (2013) · doi:10.32657/10356/55053 · Cited by 2
Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model
Yin, H., Mao, K., Zhao, J., Chang, H., Dazhi, E., & Tan, Z. (2019) · Applied Sciences · doi:10.3390/app9091955 · Cited by 2
GFT: GPU Fast Triangulation of 3D Points
Sánchez, J. R., Álvarez, H., & Borro, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15907-7_29 · Cited by 2
Multi-Scale Chroma n-Gram Indexing for Cover Song Identification
Jin Soo, S. E. O. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019mul0001 · Cited by 2
Yang, M. Y. (2013) · bonndoc (University of Bonn) · Cited by 2
Compressive image retrieval with modified images
Zhang, C., & Akashi, T. (2015) · Asian Control Conference (ASCC) · doi:10.1109/ascc.2015.7244465 · Cited by 2
Starek, M. J., & Wilkinson, B. (2022) · American Society of Civil Engineers eBooks · doi:10.1061/9780784416037.ch10 · Cited by 2
Myasnikov, V. (2015) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661815010113 · Cited by 2
A new approach for measuring 3D digitalized rape leaf parameters based on images
Fang, Y., Lin, C., Ruifang, Z., et al. (2014) · doi:10.1109/agro-geoinformatics.2014.6910633 · Cited by 2
Contour extraction algorithm for the automated photo-identification of dolphins
Maglietta, R., Seller, E., Carlucci, R., et al. (2021) · doi:10.1109/metrosea52177.2021.9611574 · Cited by 2
Song, P., Ma, Y., Xu, X., et al. (2022) · Robotica · doi:10.1017/s0263574722000637 · Cited by 2
Ivliev, E., & Obukhov, P. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1029/1/012085 · Cited by 2
Multi-features combinatorial optimization for keyframe extraction
Ma, L., Wang, W., Zhang, Y., et al. (2023) · Electronic Research Archive · doi:10.3934/era.2023304 · Cited by 2
Monocular visual servo control of a quadcopter for crossing windows, doors, and corridors
Toro‐Arcila, C. A., Becerra, H. M., & Arechavaleta, G. (2025) · Asian Journal of Control · doi:10.1002/asjc.3629 · Cited by 2
A Method to Build Multi-Scene Datasets for CNN for Camera Pose Regression
Ma, Y., Guo, H., Chen, H., Tian, M., Huo, X., Long, C., Tang, S., Song, X., & Wang, Q. (2018) · IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) · doi:10.1109/aivr.2018.00022 · Cited by 2
Qiu, C., Yang, Q., Ren, K., et al. (2025) · Infrared Physics & Technology · doi:10.1016/j.infrared.2025.106199 · Cited by 2
Park, S., Kim, S., & Park, S.-K. (2008) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2008.4694444 · Cited by 2
Camera-based document digitization using multiple images
Kim, J., Koo, H. I., & Cho, N. I. (2008) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2008.4711932 · Cited by 2
Development of an AI System for Data Extraction from Vietnamese Printed Documents
Vinh, P. H., Xuan, P. N., Minh, O. C. N., & Ha, D. B. (2024) · International Conference on Green Technology and Sustainable Development (GTSD) · doi:10.1109/gtsd62346.2024.10674912 · Cited by 2
“We leveraged the Scale Invariant Feature Transform (SIFT) [14] for the detector and Fast approximate nearest neighbors [15] for the matcher to address the study's document alignment problem.”
MethodMulti-scale PIIFD for Registration of Multi-source Remote Sensing ImagesPreprint
Gao, C., & Li, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.12572 · Cited by 2
Patch-based HDR video processing for fast moving object reconstruction
Mai, Y.-F., & Chiu, C. (2017) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/iccnc.2017.7876190 · Cited by 2
Chwa, D. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3057700 · Cited by 2
GENERATING TRAIN SIDE VIEWS FROM VIDEO SEQUENCES FOR MICROPHONE ARRAY PASS-BY MEASUREMENTS
Stier, J., & Beitelschmidt, M. (2012) · Cited by 2
Learning scene-aware image priors with high-order Markov random fields
Gong, D., Zhang, Y., Yan, Q., & Li, H. (2017) · Applied Informatics · doi:10.1186/s40535-017-0039-0 · Cited by 2
Local geometry adaptive manifold re-ranking for shape-based 3D object retrieval
Ohbuchi, R., & Kurita, Y. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2396342 · Cited by 2
Face sketch recognition system: A content based image retrieval approach
Lahlali, S. E., Sadiq, A., & Mbarki, S. (2016) · IEEE International Colloquium on Information Science and Technology · doi:10.1109/cist.2016.7805085 · Cited by 2
Dual-Fisheye Image Stitching via Unsupervised Deep Learning
Jin, Z., Dong, A., Yu, J., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53311-2_21 · Cited by 2
Knowledge Leverage from Contours to Bounding Boxes: A Concise Approach to Annotation
Cheng, J., Chang, F.-J., Hsu, K.-J., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37331-2_55 · Cited by 2
Segmentation-guided network for automatic thoracic pathology classification
Tran, Q.-D., Phan, Q.-H., Pham, T.-T.-H., & Le, H.-M. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-021-00152-5 · Cited by 1
Video Image Mosaics in Real-Time Based on SIFT
Yang, P., Zheng, M., Gao, A., et al. (2010) · doi:10.1109/pcspa.2010.217 · Cited by 2
Beyond Built Year Prediction: The Bag of Time Model and a Case Study of Buddha Images
Weng, L. (2023) · doi:10.1145/3607542.3617352 · Cited by 2
Video object extraction using feature matching based on nonlocal matting
Koeshardianto, M., Yuniarno, E. M., & Hariadi, M. (2016) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia.2016.7828658 · Cited by 2
Perceptible Lightweight Zero-Mean Normalized Cross-Correlation for Infrared Template Matching
Lee, S., Kim, D., Park, I., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3492206 · Cited by 2
A facial expression recognition strategy based on template matching
Yu, C., Xie, C., & Zhang, Y. (2015) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2015.7407993 · Cited by 2
Learning visual similarity for image retrieval with global descriptors and capsule networks
Durmuş, D., Güdükbay, U., & Ulusoy, Ö. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16164-5 · Cited by 2
Object Tracking in Video Using Covariance Matrices
Sharif, M. H., Martinet, J., & Djeraba, C. (2008) · Encyclopedia of Multimedia · doi:10.1007/978-0-387-78414-4_58 · Cited by 2
Li, L., Wang, Y., Wang, B., et al. (2024) · Remote Sensing · doi:10.3390/rs16091536 · Cited by 2
“1 A and 2 A represent the positions of the master and slave radar antennas, respectively, P represents the matched tie point, and R is the slant range vector from the master antenna to point P , referred to as the look vector.”
BackgroundRegion-Based Epipolar and Planar Geometry Estimation in Low─Textured Environments
Gaudillière, V., Simon, G., & Berger, M. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451244 · Cited by 2
Biometric Identification with Limited Data Set
Lionnie, R., Attamimi, S., Sediono, W., & Alaydrus, M. (2018) · Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) · doi:10.1109/eeccis.2018.8692859 · Cited by 2
Separating real-world photos from computer graphics: comparative study of classification algorithms
Boytsov, A., & Gladilin, P. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.11.046 · Cited by 2
Deep learning performance for digital terrain model generation
Knyaz, V. A. (2018) · doi:10.1117/12.2325768 · Cited by 2
A Semi-dense Direct Visual Inertial Odometry for State Estimator
Han, T., Zong, Q., Lu, H., & Tian, B. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8865413 · Cited by 2
Chung, J., Kim, T., Chae, Y. N., & Yang, H. S. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.08.020 · Cited by 2
Motion planning for Human-Robot Interaction based on stereo vision and SIFT
Liu, H., & Zhou, J. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5346922 · Cited by 2
Dziadosz, M., Majerek, D., & Łagód, G. (2024) · Journal of Ecological Engineering · doi:10.12911/22998993/185317 · Cited by 6
Generalised Diffusion Probabilistic Scale-Spaces
Peter, P. (2024) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-024-01202-0 · Cited by 3
Visual shape similarity and retrieval of figurative images
Scholz, S. (2011) · Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · doi:10.17169/refubium-13006 · Cited by 2
A High-Precision Virtual Central Projection Image Generation Method for an Aerial Dual-Camera
Luo, X., Zhao, H., Liu, Y., et al. (2025) · Remote Sensing · doi:10.3390/rs17040683 · Cited by 2
Fast segment iterative algorithm for 3D reconstruction
Meško, M., & Kršák, E. (2014) · doi:10.1109/dt.2014.6868721 · Cited by 2
The Use of a 3D Image Comparison Program for Dental Identification
Kubo, D., Itamiya, T., Kawanishi, N., et al. (2023) · Applied Sciences · doi:10.3390/app13137517 · Cited by 2
Vision-based Localization for AUVs using Weighted Template Matching in a Structured Environment
Kim, D., Lee, D., Myung, H., et al. (2013) · Journal of Institute of Control Robotics and Systems · doi:10.5302/j.icros.2013.13.9012 · Cited by 2
Research on Scalable Real-Time Image Mosaic Technology Based on Improved SURF
Wang, G., Liu, L., & Zhang, Y. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1069/1/012162 · Cited by 2
Visual-Textual Late Semantic Fusion Using Deep Neural Network for Document Categorization
Wang, C., Yang, H., & Meinel, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26532-2_73 · Cited by 2
Jung, C.-H., & Seo, S. (2022) · Journal of Next-generation Convergence Information Services Technology · doi:10.29056/jncist.2022.12.01 · Cited by 2
Visiads: A vision-based advertising platform for camera phones
Scott, M. R., Jiang, W., Xie, X., et al. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202882 · Cited by 2
A novel method for 3D reconstruction on uncalibrated images
Zheng, H., Yuan, J., & Gu, R. (2011) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2043674.2043714 · Cited by 2
Multi-target tracking with occlusions via skeleton points assignment
Ding, H. W., & Zhang, W. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.12.003 · Cited by 2
Wang, C., & Zhang, Y. (2014) · Optik · doi:10.1016/j.ijleo.2014.01.027 · Cited by 2
Vongbunyong, S., & Chen, W. (2015) · Sustainable production, life cycle engineering and management · doi:10.1007/978-3-319-15183-0_4 · Cited by 2
SAR Image Registration Using Cluster Analysis and Anisotropic Diffusion-Based SIFT
Wang, Y., Ge, Z., Su, J., et al. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7389-2_1 · Cited by 2
RoTIR: Rotation-Equivariant Network and Transformers for Zebrafish Scale Image Registration
Wang, R., Achim, A., Raele-Rolfe, R., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66955-2_20 · Cited by 2
Estimating Distortion Parameters in Simulated Prosthetic Vision
Chundi, P., Subramaniam, M., Muthuraj, A., & Margalit, E. (2013) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2013.58 · Cited by 2
MAELab: a framework to automatize landmark estimation
Le, V. L., Beurton‐Aimar, M., Krähenbühl, A., et al. (2017) · Digital Library (University of West Bohemia) · Cited by 2
Patch-based reconstruction and rendering of human heads
Schneider, D. C., Hilsmann, A., & Eisert, P. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5652905 · Cited by 2
Kim, H.-W., Noh, S.-C., Kim, S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-69153-5 · Cited by 2
“Traditionally, methods such as the SIFT algorithm have been widely used for detecting feature points and extracting feature descriptors 13 .”
MethodTowards Foundation Models for 3D Vision: How Close are We?
Zuo, Y., Kayan, K., Wang, M., Jeon, K., Deng, J., & Griffiths, T. L. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00122 · Cited by 10
Image Classification based on CBIR
Ali, S. K., & Sadoon, S. A. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1591/1/012022 · Cited by 2
Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly
Shete, S., Mronga, D., Jadhav, A. S., et al. (2024) · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711376 · Cited by 2
“A tuning parameter, namely the Lowe’s distance ratio α ∈ [ 0 ... 1 ] [23], gauges the degree of correspondence between two images.”
Scale invariant feature transform evaluation in small dataset
Hassan, A. M. (2015) · International Conference on Sciences and Techniques of Automatic Control and Computer Engineering · doi:10.1109/sta.2015.7505105 · Cited by 2
Feature Map Activation Analysis for Object Key-Point Detection
Rush, A., & Wood, S. L. (2023) · IEEE Access · doi:10.1109/access.2023.3315589 · Cited by 2
Crowded scene understanding algorithm based on Two-Stream Residual Network
Li, P., Jiang, X., Sun, T., et al. (2017) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2017.8301919 · Cited by 2
Learning end-effector orientations for novel object grasping tasks
Balaguer, B., & Carpin, S. (2010) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2010.5686826 · Cited by 2
Gaze correction using feature-based view morphing and performance evaluation
Baek, E., & Ho, Y. (2016) · Signal Image and Video Processing · doi:10.1007/s11760-016-0918-1 · Cited by 2
Retrieving 3D Model Using Compound-Eye Visual Representation
Li, L., Zhang, S., Bai, X., et al. (2013) · International Conference on Computer-Aided Design and Computer Graphics (CAD/Graphics) · doi:10.1109/cadgraphics.2013.30 · Cited by 2
A double circle structure descriptor and Hough voting matching for real-time object detection
Ye, S., Liu, C., & Li, Z. (2016) · Pattern Analysis and Applications · doi:10.1007/s10044-016-0539-x · Cited by 2
Long Range Imaging Using Multispectral Fusion of RGB and NIR Images
Zhang, H., Mei, L., & Jung, C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095148 · Cited by 2
Learning region-wise deep feature representation for image analysis
Zhu, X., Wang, Q., Li, P., Zhang, X., & Wang, L. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0894-0 · Cited by 2
Evaluation criteria for appearance based maps
Erinc, G., & Carpin, S. (2010) · doi:10.1145/2377576.2377637 · Cited by 2
Video annotation based on adaptive annular spatial partition scheme
Ding, G., Zhang, L., & Li, X. (2010) · IEICE Electronics Express · doi:10.1587/elex.7.7 · Cited by 2
Automatic point matching and robust fundamental matrix estimation for hybrid camera scenarios
Baştanlar, Y., Temizel, A., & Yardımcı, Y. (2009) · IEEE Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2009.5136361 · Cited by 2
Reconstructing and correcting 3D building models using roof topology graphs
Xiong, B. (2014) · doi:10.3990/1.9789036538107 · Cited by 2
Automatic stitching of micrographs using local features
Wortmann, T. (2010) · International Symposium on Optomechatronic Technologies · doi:10.1109/isot.2010.5687386 · Cited by 2
Finite Libby-Novick Beta Mixture Model: An MML-Based Approach
Samiee, N., Manouchehri, N., & Bouguila, N. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-5834-4_30 · Cited by 2
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
