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 2,030
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 48,801–48,900 of 56,203
Application of Deep Learning for Registration Between SAR and Optical Images
Zhang, W., & Zhao, Y. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-1145-5_10
Past Earthquakes in Continental Settings—A Geomorphologic Perspective
Zielke, O., & Klinger, Y. (2025) · Springer eBooks · doi:10.1007/978-3-031-73580-6_5
Image Registration Via Marginal Distribution Adaptation
Yan, X., Yang, J., Liu, X., et al. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3091592
Learning to Predict Repeatability of Interest PointsPreprint
Doan, A.-D., Turmukhambetov, D., Latif, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03578
Label-Free Computer Vision Method for Measuring the Natural Frequencies of Tall Structures
Chen, D., Quan, Z., Zhang, Y., Gao, Q., Jin, B., Zhang, Z., Li, Z., & Sun, Q. (2026) · Applied Sciences · doi:10.3390/app16147049
Medrzycka, D., Copland, L., Thomson, L., et al. (2024) · doi:10.5194/gi-2024-10-rc2
Cross-reference attention for region-aware super-resolution of blood cell images
Kang, G., Hwang, S., Cho, Y., et al. (2026) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwag041
Use of Images in Internet Photo Sharing Sites for Example-Based Super-Resolution
Kondo, S., Taipalus, H., Toma, T., et al. (2008) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.62.1836
Martínez, F., Castillo, F., & Bautista, L. (2019) · Revista Politécnica · doi:10.33571/rpolitec.v15n29a7
Algorithmically Improved Framework for Image-only Robotic Mapping
Elibol, A., & Chong, N. Y. (2021) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur52253.2021.9494684
Multimedia Sensor Network Supported IoT Service
Ma, H., Liu, L., & Luo, H. (2021) · Advances in computer science and technology · doi:10.1007/978-981-16-0107-1_5
A fast vehicle tag recognition system with video by IP camera
Mo, G., & Zhang, S. (2010) · doi:10.1145/1900179.1900246
AI Tool Detection using Vision Transformers: A Deep Learning Approach
Dadi, R., Vihasith, R., & Akramuddin, M. (2025) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss64956.2025.11500721
“In this study, the Vision Transformer (ViT) model was fine-tuned on a mechanical tools dataset sourced from Kaggle [6].”
Registration and Geolocation of Aerial Thermal Images for Wildfire Monitoring
Garcia, R. D. L., Nanjappa, N., Alvarez, A., Anand, P., & Vejarano, G. (2025) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc66453.2025.11304674
“This is accomplished using Lowe’s ratio test [42].”
Generating Reference Views of Traffic Intersection for Safe Driving Assistance
Kato, J., & Wang, Y. (2011) · Springer eBooks · doi:10.1007/978-1-4419-9607-7_14
MTA-Dataset: Multiple-Tilt-Angle Dataset for UAV–Satellite Image Matching
Liu, Q., Jiang, L., Wu, G., et al. (2026) · Applied Sciences · doi:10.3390/app16052488
Bi, Y., Xue, B., & Zhang, M. (2020) · doi:10.26686/wgtn.13158323
Application of Learning-based Resource Allocation Scheme for Different UE Antenna Orientations
Li, J. (2017) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Multimedia annotation through search and mining
Manjunath, B. S., & Moxley, E. (2009)
A fast corner detector for fuzzy mineshaft images based on dual-threshold
Xing, Y., Zhang, D., Xu, A., et al. (2012) · doi:10.1109/icwamtip.2012.6413457
Multimodal style aggregation network for art image classification
Wang, Q., & Feng, G. (2025) · Signal Processing Image Communication · doi:10.1016/j.image.2025.117309 · Cited by 2
Elantcev, M., Архипов, И. О., & Gafarov, R. (2020) · International Conference on Information Technology and Nanotechnology (ITNT) · doi:10.1109/itnt49337.2020.9253299
3D convolutional neural networks by modal fusion
Yoshiyasu, Y., Yoshida, E., Pirk, S., & Guibas, L. J. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296587
Weapons recognition in X-ray images using Bag of Visual Words
Visuales, S. D. P., Castro, D., Macías, F. S., et al. (2016) · Revista Cubana de Ciencias Informáticas
Block-regressed face alignment algorithm based on supervised descent method
Shi, Y. (2019) · International Conference on Advanced Information Science and System (AISS) · doi:10.1145/3373477.3373697
Nie, J., Zhao, Q., Xue, D., et al. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104440
In defense of local descriptor-based few-shot object detection
Zhou, S., Li, H., Wang, Z., & Zhang, Z. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1349204
“For instance, classical “Bag of Words (BoWs)” employed normalized patches or SIFT descriptors over Difference of Gaussian, Harris-scale or Harris-affine keypoints (Mikolajczyk and Schmid, 2002), vector quantized using k-means variants.”
Optimal Feature Design for Vision-Guided Manipulation
Shademan, A. (2013)
Construction and exploitation of a 3D model from 2D image features
Bliss, N. T., Sun, Z. Z., Ni, K. S., et al. (2010) · DSpace@MIT (Massachusetts Institute of Technology)
Toyama, F., Shoji, K., & Miyamichi, J. (2009) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.129.481
FUJIWARA, K., Chino, R., Teppei, O. T. A., et al. (2013) · The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · doi:10.1299/jsmermd.2013._1p1-j07_1
Using Regularity Unit As Guidance For Summarization-Based Image Resizing
Hsiao, F. T., Lin, Y.-H., & Lu, Y.-C. (2021) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip53242.2021.9675328
Remote Sensing Image Matching using TPS Transformation and Local Geometrical Constraint
Chen, J., Liu, H., Luo, L., et al. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8897824
Tolerance analysis of model-based camera tracking by modeling errors
Rhee, E. J., Park, J., Seo, B.-K., et al. (2013) · doi:10.1109/fcv.2013.6485510
Bharadwaj, A., & Lichti, D. D. (2026) · Remote Sensing · doi:10.3390/rs18132220
Lane Detection and Pixel-Level Tracking for Autonomous Vehicles
Wang, Y., Wu, J., Wei, Z., et al. (2022) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2022-01-0077
DOAFT: Infrared And Visual Image Registration Based on Dominant Orientation Align Feature Transform
Wang, S., Xi, P., & Cai, Y. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8865405
Ensembles of Gradient Based Descriptors with Derivative Filters for Visual Object Categorization
Eqlouss, E. A. A., Abdullah, A., & Abdullah, S. N. H. S. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-37374-9_4
Uso de fluxo óptico na odometria visual aplicada a robótica
Araújo, D. C. D. S. (2015) · doi:10.47749/t/unicamp.2015.945968
Visual Object Detection System for Autonomous Vehicles in Smart Factories
Zorkun, N. G., Çevıkalp, H., Yavuz, H. S., & Yazıcı, A. T. (2019) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu48272.2019.8946370
PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and TumorsPreprint
Li, Z., Pan, H., Zhu, Y., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.01001
Evaluating Cognitive Architectures
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0011
Video modeling via implicit motion representations
Zheng, Y. (2008) · doi:10.33915/etd.2728
Image accuracy prediction based on no-reference metrics using GA-XGBoost
Zhou, H., Zhu, H., Bian, Y., Teng, X., Liu, Y., Liu, M., Liu, K., & Ni, J. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05655-1
“Local structural information is characterized using statistical representations derived from SIFT descriptor matrices [14].”
CStorage: An efficient classification-based image storage system in cloud datacenters
Shen, H., & Zhou, H. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8257961
Lecture video indexing using boosted margin maximizing neural networksPreprint
Ma, D., Zhang, X., Ouyang, X., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00575
Algorithm of unmanned aerial vehicles navigation based on the image of the underlying surface
Litvinenko, A. I., Budko, M. Y., Гирик, А. В., et al. (2026) · Известия высших учебных заведений. Приборостроение · doi:10.17586/0021-3454-2026-69-4-313-321
GPU-accelerated feature point matching using extended ColourFAST descriptors
Fonseca, E. D., Ensor, A., & Hall, S. (2015) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2015.7761518
Image-Based Ripeness Detection of Cocoa Pods using Machine Learning and Deep Learning
Warrier, A., Mawalkar, Y., Sharath, A., & Mary, L. (2025) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics66191.2025.11330937
An Automatic Registration of UV Images
Wang, C., & Sun, J. (2012) · International Conference on Computer Application and System Modeling · doi:10.2991/iccasm.2012.227
Mishra, P., Kishore, J. K., & Bharadwaj, H. S. (2015) · International Journal of Computational Vision and Robotics · doi:10.1504/ijcvr.2015.072191
Efficient dense 3D reconstruction using image pairs
Ravi, P., Li, M., & Li, X. S. (2015) · International Conference on Computer Science & Education (ICCSE) · doi:10.1109/iccse.2015.7250338
A High-Performance Image Matching and Recognition System for Multimedia Applications
You, S. O., & Neumann, U. (2007) · doi:10.1109/icme.2007.4285008
Simultaneous 3D Object Segmentation and 6-DOF Pose EstimationPreprint
Liu, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12095
A Complementarity-Based Switch-Fuse System for Improved Visual Place Recognition
Waheed, M., Waheed, S., Milford, M. J., McDonald-Maier, K. D., & Ehsan, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341876
A Feature Extraction Network Based on Self-Attention Mechanism for Underwater ImagePreprint
Wu, D., Su, B., Hao, L., et al. (2023) · Preprints.org · doi:10.20944/preprints202310.0974.v1
Elhanashi, A., & Saponara, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30078-3.00001-9
Research on Visual Effect of Enhancing Image
(2019) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2019.06.031
Region description using scalable circumferential filters
Zhang, W., & You, X. (2014) · doi:10.1109/spac.2014.6982696
ROBUST 3-D OBJECT DETECTION AND FEATURE EXTRACTION FOR MULTI-ROBOT COOPERATION
Liyanage, A. K., & de Silva, C. W. (2011) · International Journal of Information Acquisition · doi:10.1142/s0219878911002306
- Applications: Image Alignment
El-Baz, A. S., Gimel’farb, G., & Suri, J. S. (2015) · CRC Press eBooks · doi:10.1201/b19253-9
A Generalized BoVW Model for Content-Based Image Retrieval
Xu, W. M., Yang, H., Fang, K. L., et al. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.496-500.1817
Irregular-structure tree models for image interpretation
Todorović, S., & Wu, D. O. (2005)
SwinEdge: A Unified Framework for Accurate Architectural Edge Extraction via Swin-Transformer
Zeng, T., Shang, J., Zhao, Y., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3501-8_48
Multistage PCA Whitening: A Robust Method to Dimensionality Reduction in Image Retrieval
Zhang, B.-J., Liu, G., Li, Z., & Song, S. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3669538
“To overcome these limitations, researchers introduced the scale-invariant feature transform (SIFT) [6] and the Bag-of-Words (BoW) [7] models for instance-level image retrieval [8], where local features and their structural information are extracted and then quantized into fixed-length vectors, which…”
Multi-Affine Misalignment Removal from Bracketed Images for HDR Photography
Barazzetti, L. (2016) · Computer Graphics International · doi:10.1145/2949035.2949051
Li, X., Yao, H., Sun, X., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738060
Xiao, Y. (2017) · Deep Blue (University of Michigan)
Fashion product entity matching
Jundt, O. (2017)
Harnessing Topological Semantics for Network Topology Diagram Retrieval
Sun, L., Zhang, Y., Wei, Y., Zou, Z., Zhong, S., & Gong, C. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3675542
“Some studies proposed image retrieval methods that directly relied on local features [13], while others leveraged these features to construct global representations based on the Bag-of-Words model and similar techniques [14], [15], [16], [17].”
Multi‐group–multi‐class domain adaptation for event recognition
Feng, Y., Wu, X., & Jia, Y. (2015) · IET Computer Vision · doi:10.1049/iet-cvi.2014.0405
BackNet: An Enhanced Backbone Network for Accurate Detection of Objects with Large Scale Variations
Hossain, M. T., Teng, S. W., & Lu, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34879-3_5 · Cited by 1
A novel stereo matching approach for pedestrian re-identification
Biswas, S. (2015) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2015.7490003
Contracting curve density algorithm for applications in personal robotics
Shu-Lei, Z. H. U., Pangercic, D., & Beetz, M. (2011) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2011.6100884
Phase image-guided adaptive rotation-invariant feature point detector
Mashaly, A. S., & Mahmoud, T. A. (2023) · The Imaging Science Journal · doi:10.1080/13682199.2023.2202088
The processing of vectors field for image registration
Beneš, R., & Říha, K. (2010) · International Conference on Circuits, systems, electronics, control & signal processing
Vision-based manipulative gesture recognition in a human-robot interaction scenario
Li, Z. (2008) · PUB – Publications at Bielefeld University (Bielefeld University)
Ghost Towns: Semantically Labelled Object Removal From Video
Clifford, W. (2019) · Arrow - TU Dublin (Technological University Dublin) · doi:10.21427/1tag-k222
Nikolaev, D., Limonova, E. E., Tropin, D. V., et al. (2018) · International Conference on Machine Vision · doi:10.1117/12.2310093
SIFT image matching algorithm using fusion color information
Zheng, Y., & Wang, L. (2022) · International Conference on Computer, Artificial Intelligence and Control Engineering (CAICE) · doi:10.1117/12.2640911
Comparison of Four Local Invariant Characteristics Based on Palm Vein
Lü, W., & Yuan, W. (2017) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/cse-euc.2017.170
Human Activity and Crowd Violence Analysis Using Deep Learning and Optimization Models
Naik, A. J., & Gopalakrishna, M. T. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-19215-8_6
Coarse Registration of Point Cloud Base on Deep Local Extremum Detection and Attentive DescriptionPreprint
Lu, H., & Nie, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4106324
Multi-Object Detection and Tracking Algorithm Based on Improved YOLO11n and ByteTrack
Jiao, Y., & Liu, G. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11559953
Performance of Biometrics Recognition System Using Multiple Scales Analysis
Lionnie, R., & Alaydrus, M. (2020) · doi:10.1109/bcwsp50066.2020.9249453
Robust Model for Melanoma Classification Using Deep Learning and Machine Learning Techniques
y Cárdenas, J. R. M., Calderon, M. V. G., Fabián, J., et al. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-96798-6_42
Near‐Duplicate Image and Video Detection
Zhao, W., & Ngo, C. (2015) · Wiley Encyclopedia of Electrical and Electronics Engineering · doi:10.1002/047134608x.w8255
A SELF-SUPERVISED KEYPOINT DETECTION NETWORK FOR MULTIMODAL REMOTE SENSING IMAGES
Li, L., Han, L., Cao, H., et al. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2022-609-2022
Image Compression Using Stitching with Harris Corner Detector and SPIHT Coding
Chia, W. C., Ang, L.-M., & Seng, K. P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-05036-7_62
AutoFill: An Automatic Approach to Filling Panoramic Images
Wang, S., & Cao, M. (2024) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai62207.2024.10826716
Human Computation and Crowdsearching
Ceri, S., Bozzon, A., Brambilla, M., et al. (2013) · Springer eBooks · doi:10.1007/978-3-642-39314-3_15
High-precision multimodal remote sensing image matching based on Log-Gabor filtering
Li, Y., Gao, Z., & Liu, Y. (2025) · doi:10.1117/12.3065952
“The Scale-Invariant Feature Transform (SIFT) [9] is a representative method in this category and has been widely used for optical remote sensing image matching due to its robustness to scale, illumination changes, and rotation.”
Tarng, W., Tang, C.-K., Ou, K.-L., Wu, Y.-J., Hsu, J.-C., & Ding, T.-J. (2026) · Journal of Baltic Science Education · doi:10.33225/jbse/26.25.710
Landmark detection with surprise saliency using convolutional neural networks
Tang, F., Lyons, D. M., & Leeds, D. (2016) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi.2016.7849490
Summarization of LifeLog Data Based on Implicit Feedback from Personal Browsing History
Hatada, K., Yamasaki, T., & Aizawa, K. (2010) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.64.237
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
