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 46,101–46,200 of 56,200
Joint attribute chain prediction for zero‐shot learning
Qiao, L., Tuo, H., Wang, J., Wang, C., & Jing, Z. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2017.0438
Human extremity detection and its applications in action detection and recognition.
Yu, Q. E. (2011) · Texas ScholarWorks (Texas Digital Library)
AFD: A feature detection method for outdoor real-time video stitching system
Hu, C., Wu, H., Wang, X., et al. (2017) · International Symposium on Next-Generation Electronics · doi:10.1109/isne.2017.7968749
Design and implementation of a visual tracking system for small-scale unmanned helicopters
Wang, B., Wang, H., Lu, G., et al. (2014) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2014.6896418
Acquisition and Rectification of Shape Data Obtained by a Moving Range Sensor
Banno, A., & Ikeuchi, K. (2007) · IPSJ Digital Courier · doi:10.2197/ipsjdc.3.424
Autocalibración de parámetros extrínsecos de sistemas estéreo para aplicaciones de tráfico
Musleh, B., Beltrán, J., Jaraquemada, C., Gómez-Silva, M. J., Hernández, N., & Armingol, J. M. (2022) · Jornadas de Automática · doi:10.17979/spudc.9788497498081.0675
Wu, B.-C., Huang, C., Teng, P.-S., et al. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08222-0
Deng, W. (2014) · Nottingham ePrints (University of Nottingham)
REFA: A Robust E-HOG for Feature Analysis for Local Description of Interest Points
Grand-Brochier, M., Tilmant, C., & Dhome, M. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-32350-8_14
Low-level vision for resource-limited devices
Canosa, X. I. S. (2021) · doi:10.20868/upm.thesis.69043
Increasing the precision of junction shaped features
Cordes, K., & Östermann, J. (2015) · doi:10.1109/mva.2015.7153189
3D model retrieval based on deep learning approach with weighted three-view deep features
Xuemei, J., Li, Y., Hu, J., et al. (2020) · International Workshop on Advanced Imaging Technology (IWAIT) · doi:10.1117/12.2566221
Content-based 3D shape retrieval using deep learning approach
Benjelloun, M., Dadi, E. W., Daoudi, E. M., et al. (2018) · doi:10.1145/3230905.3230957
Descripción y análisis de forma basado en la invarianza a isometrías de los esqueletos topológicos
Cortés, P., & Alberto, D. (2019)
High-Dimensional Non-Gaussian Data Clustering using Variational Learning of Mixture Models
Fan, W. (2013) · Spectrum Research Repository (Concordia University)
Efficient Feature Extraction Methods for Enhancing Analysis of Lecture Videos
Kaur, P. C., & Ragha, L. R. (2024) · doi:10.1109/innova63080.2024.10847005
Quantitative Evaluation of Multi-Sensor Image Registraction Feature Descriptor
Zhang, Y., Zhao, D., Huang, Z., et al. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9883363
Autonomous Exploration of Indoor Environments in 3D using a UAV
Rothe, J., Strohmeier, M., & Montenegro, S. (2025) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme64568.2025.11277818
“Two primary approaches exist: indirect methods extracting distinct features (using algorithms like SIFT [8], SURF [9], and ORB [10]), and direct methods comparing raw pixel intensities.”
Roselin Mary Clare, K., & Hemalatha, M. (2018) · Biomedical Research - India · doi:10.4066/biomedicalresearch.29-17-3426
WTDL-Net: Medical Image Registration Based on Wavelet Transform and Multi-Scale Deep LearningPreprint
Chu, B., Zhang, B., Zhang, B., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-6254576/v1
Huang, H., Han, D., Zhou, X., et al. (2021) · International Conference on Smart Transportation and City Engineering (STCE) · doi:10.1117/12.2614322
Struwe, M., Hasler, S., & Bauer-Wersing, U. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29451-3_9
A View Based Approach for Matching the 3D Appearance of Local Features
Strachan, E., & Siebert, J. P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39094-4_41
Lee, S., Gwak, J., Prakash, O., et al. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-981-13-2685-1_30
Learning to see across Domains and ModalitiesPreprint
Carlucci, F. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04992
Sahu, Y., Wael, M., Gupta, B., et al. (2023) · International Conference on Computer Theory and Applications (ICCTA) · doi:10.1109/iccta60978.2023.10969271
Hybrid SIFT-SNN for Efficient Anomaly Detection of Traffic Flow-Control Infrastructure
Rathee, M., Bačić, B., & Doborjeh, M. (2025) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz67716.2025.11281836
“…on this foundation, this study proposes a hybrid neuromorphic vision framework integrating Scale-Invariant Feature Transform (SIFT) descriptors [5] for spatial feature extraction with a latency-driven spike encoding layer and a Leaky Integrate-and-Fire (LIF) Spiking Neural Network (SNN) for…”
Multi-scale foreground extraction on graph cut
Liu, J., & He, K. (2019) · MATEC Web of Conferences · doi:10.1051/matecconf/201927702031
Analysis of target recognition technology for autonomous underwater robot
Zhuang, S., & Wang, G. (2023) · International Conference on Control and Intelligent Robotics (ICCIR) · doi:10.1117/12.3010683
Vision based behavior recognition of laboratory animals for drug analysis and testing
Sandıkcı, S. (2009) · Bilkent University Institutional Repository (Bilkent University)
Nested Graph Words for Object RecognitionPreprint
Karaman, S., Benois‐Pineau, J., & Mégret, R. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1106.2729
A Fast, Square-Spiral, Finite Element Interest Point Detector
Fegan, J., Coleman, S., Kerr, D., et al. (2025) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit63637.2025.10965327
“Representing these landmarks as pixel locations with distinctive regional characteristics, known as interest points, simplifies subsequent analysis [1].”
Hemza, S. (2026) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.5263
“We can cite as indirect detector, the HARRIS detector and the SIFT Detector (Scale-Invariant-Features-Transform) [25], [26], [27].”
Adaptive Hashing with Sparse Modification for Scalable Image Retrieval
Zhang, L., Shen, Q., Li, D., Feng, G., Tang, X., & Wang, P. S. P. (2017) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001417540118
Structure-from-motion in micro-image domain for uncalibrated plenoptic 2.0 cameras
Dury, S., Bonatto, D., Sancho, J., et al. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02612-2
Real Object Recognition and Augmentation for n-Screen Convergence Service
Park, J.-I., Kim, J.-H., & Kim, S. H. (2012) · Computer Science and Engineering · doi:10.5923/j.computer.20120203.05
Estimación de un mapa disperso de profundidad usando visión estéreo
Reyes, E., & Darío, I. (2015)
Scene Recognition with Feature Maps
Zhong, J., Li, M., & Tang, S. (2026) · Springer eBooks · doi:10.1007/978-981-99-1650-4_37-1
Kidney segmentation from computed tomography images using U-Net and batch-based synthesis
Leś, T., Markiewcz, T., Dziekiewicz, M., et al. (2021) · doi:10.1109/ijcnn52387.2021.9534007
Time-series atlases of the rat brain after middle cerebral artery occlusion using FDG-PET images
Li, C., He, W., Zhang, X., et al. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109977
Unveiling Portinari's palette: Multimodal analysis of São Jorge e o dragão mural and paint tubes
de Albuquerque, I. M. B., Rizzutto, M. A., Curado, J. F., et al. (2026) · Journal of Cultural Heritage · doi:10.1016/j.culher.2026.06.012
Apprentissage non-supervisé de représentations pour l'analyse de séquences vidéos
Lorre, G. (2021) · theses.fr (ABES) · doi:10.70675/76eb723aze165z4df2za257z53a3770f3949
Location Assignment of Recognized Objects via a Multi-Camera System
Kouskouridas, R., & Gasteratos, A. C. (2015)
Salient Feature Detection for optical flow computation
Yang, J., Wang, R., & Xu, R. (2016) · doi:10.1109/cgncc.2016.7829181
Improvement of Target Extraction and Dense Matching
Jiang, Z., Wang, Q., & Cui, Y. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-34038-3_28
Adaptive appearance learning for visual object tracking
Khan, Z. H., & Gu, I. Y. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946678
Automatic Aerial Image Alignment for GeoMemories
Amato, G. M., Falchi, F., Rabitti, F., et al. (2013) · Advances in Multimedia
A Trademark Retrieval Method Based on Self-supervised Learning
Hu, K., Lu, Y., Li, H., & Song, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-5815-2_19
Automatic 3D coordinate estimation of feature points for building modeling using stereo images
Kurokawa, Y., & Susaki, J. (2012) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2012.6351022
túìõêóìåGZkGñâëå äõGõôàäíêïéGúöêï éGyvpGàïãG kñúâìåGmêìõåôêïé GçñôGhúéîåïõåãGy åàìêõ
Park, J.-H., Seo, D., Ku, M.-S., et al. (2011)
Anchor-Based 6D Object Pose Estimation
Liu, Z., Wang, H., & Liu, F. (2021) · International Conference on Virtual Reality · doi:10.1109/icvr51878.2021.9483838 · Cited by 1
Combining Multi-Modal Features for News Story Correlation Analysis
Chen, D. W., Deng, L. Q., Yuan, Z. M., et al. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.268-270.1040
Local Features in Image; Using HOG and SIFT
Shouno, H. (2013) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.67.256
Content-Based Image Retrieval Using Quaternion Discrete Cosine Transform
Keisham, N., & Neelima, A. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-82706-8_34
Unsupervised object of interest discovery in multi-view video sequence
Thummanuntawat, T., Kumwilaisak, W., & Chinrungrueng, J. (2009) · International Conference on Advanced Communication Technology
Sirisha, B., Sandhya, B., & Kumar, J. P. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-9669-5_6
Comparative Analysis of Classical and Deep Learning Techniques for Copy–Move Image Forgery Detection
Kaur, R., Kaur, S., & Singh, H. J. (2026) · International Conference on Secure Cyber Computing and Communications (ICSCCC) · doi:10.1109/icsccc69031.2026.11600153
“Techniques such as the Scale-Invariant Feature Transform (SIFT) [12] and Speeded-Up Robust Features (SURF) [13] detect distinctive features invariant to scale and rotation.”
A Review of 3D Aircraft Image Recognition Methods
Zhu, X., & Ma, C. W. (2012) · Advanced materials research · doi:10.4028/www.scientific.net/amr.433-440.2794
Generation of Multiple Background Model by Estimated Camera Motion Using Edge Segments
Kim, T., & Jo, K.-H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87442-3_67
Automatic Detection of 3D Joint Erosion in Hand HR-pQCT: A Level Set-Based ApproachPreprint
Zhang, X., Cheng, I., Liu, S., et al. (2021) · doi:10.36227/techrxiv.15088365.v2
Adaptive infrared patterns for microscopic surface reconstructions
Milosavljevic, S., Bardosi, Z. R., Oezbek, Y., et al. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03242-8
WebVR180: VR180 Video Dataset with Stereoscopic Consistency and Visual Quality
Choi, C., Jeong, S., Koo, H., & Lee, J. (2026) · Journal of the Korea Computer Graphics Society · doi:10.15701/kcgs.2026.32.3.173
Biermeier, C. M., Albert, M., Pal, A. A., Gallo, E., Wittmann, J., Hartmann, J., Bill, R. T., Julmi, J., Gilmour, D. T., & Peri, F. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117150 · Cited by 1
Cp_Hdr: A Feature Point Detection and Description Library for Ldr and Hdr ImagesPreprint
Nascimento, A. S., Andrade, B. T., Dantas, D. O., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5226204
EMPOWERING ACCESSIBILITY: AN OBJECT DETECTION SYSTEM FOR VISUALLY IMPAIRED INDIVIDUALS
(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets50661
Bayes Risk for Large Scale Hierarchical Top-K Image Classification
Paulin, M. (2013) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-009767867
Unsupervised Construction of Task-Specific Datasets for Object Re-identification
Pulc, P., & Holeňa, M. (2021) · International Conference on Computer Technology Applications (ICCTA) · doi:10.1145/3477911.3477922
GPU accelerated feature algorithms for mobile devices
Hall, S. (2014) · Tuwhera (Auckland University of Technology)
Finding Event Videos via Image Search Engine
Wang, H., & Wu, X. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.78
Chen, M., Shi, Y., Wu, L., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4782052
Research on Property of Image Matching Method Based on the Deep Fusion of Harris and SIFT
Zeng, Q., Ma, S., Feng, Y., et al. (2016) · IIAE International Conference on Intelligent Systems and Image Processing (ICISIP) · doi:10.12792/icisip2016.077
Research on unsupervised learning-based image stitching
Zhou, Y., Dong, L., Liu, Y., et al. (2023) · doi:10.1117/12.2686553
Logitboost Extension for Early Classification of Sequences
Fujino, T., Ishiguro, K., & Sawada, H. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23672-3_70
Robustly Modeling The World From Photos
Wilson, K. L. (2016) · eCommons (Cornell University) · doi:10.7298/x40p0wx1
An Adaptive Pooling Model for Aesthetic Quality Assessment Based on Convolutional Neural Network
Han, M., Shi, P. A., & Bao, X. (2019) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess47205.2019.9040714
Bragdon, S. P., Truong, V. H., Trautz, A. C., et al. (2024) · doi:10.1117/12.3013923
“The new approach consists of four steps: Haar discrete wavelet transforms (DWT), 13,14 the features from accelerated segment test (FAST) algorithm, 15 the scale-invariant feature transform (SIFT) algorithm, 16 and finally non-maximum suppression.”
Xiao, Z., He, L., Li, T., et al. (2026) · Buildings · doi:10.3390/buildings16193835
Caranx: Scalable Social Image Index Using Phylogenetic Tree of Hashtags
Xie, Y., Chen, Z., Agrawal, A., et al. (2013)
Batanlar, Y., Temizel, A., Yardımcı, Y., et al. (2009)
Development of an Algorithm for Absolute Visual Navigation of an Unmanned Aerial Vehicle
Lazareva, P. A., Malikov, A. I., Degtyarev, G. L., et al. (2025) · Russian Aeronautics · doi:10.3103/s1068799825030067
“To this end, feature extraction and matching algorithms are used, such as SIFT [4], ORB [5], or neural network-based methods (e.g., SuperPoint [6]).”
Liu, H. F., Xia, H., Li, Z., et al. (2026) · Journal of Sensors · doi:10.1155/js/2555351
Accurate and consistent depth estimation for light field camera arrays
Shim, S.-H., Kim, J. W., Hyun, S., et al. (2019) · SPIE Three-Dimensional Imaging, Visualization, and Display · doi:10.1117/12.2518581
Research on Improved SIFT Algorithm Based on Optimal Transmission Theory
Zhao, Y., & Jia, Q. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10864825
CUSIR: Constraint based Unsupervised and Scalable Image retrieval
Haghshenas, S., Maihami, V., & Shahriari, O. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21159-z
Multi-Camera Vision-Based Mobile Robot Control and Path Planning
Dönmez, E., & Özcan, A. (2021) · DergiPark (Istanbul University)
Appearance Based Stage Recognition of Drosophila Embryos
Nutakki, G. C. (2010) · TopSCHOLAR (Western Kentucky University)
Depth-Scale Method in 3D Registration of RGB-D Sensor Outputs
Bozkurt, İ., & Özden, E. (2014) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0004673204700475
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
