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 54,501–54,600 of 56,204
Real-time 3D camera pose estimation using SLAM and GPU parallel computing on Jetson Orin
Nguyen, T. N., & Tran, X. T. (2024) · Ministry of Science and Technology Vietnam · doi:10.31276/vjst.66(10db).02-09
Scene matching localization and orientation algorithm based on STN_Siamese network
Xia, H., Cheng, S., Yang, X., et al. (2023) · International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) · doi:10.1117/12.2661047
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.002.0007
Leveraging deep neural networks to capture psychological representations.Preprint
Peterson, J. C., Abbott, J. T., & Griffiths, T. L. (2017) · arXiv (Cornell University)
Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System
Cha, S., Nam, D.-Y., & Han, J.-K. (2021) · Applied Sciences · doi:10.3390/app11052033
Yang, Y. B., & Suh, E.-K. (2026) · IEEE Access · doi:10.1109/access.2026.3661618
Scale-free flocking and giant fluctuations in epithelial active solidsPreprint
Shen, Y., O’Byrne, J., Schoenit, A., Maitra, A., Mège, R., Voituriez, R., & Ladoux, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.18.619133
Wang, X., Li, J., & Zhang, D. (2026) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2026.103987
Transaction clues based video summarization using by speeded up robust features
Brindha, N. V., & Amitha, T. (2014) · International Conference on Information Communication and Embedded Systems · doi:10.1109/icices.2014.7034060
Gao, H., Xia, M., Feng, X., Li, T., & Liu, X. (2026) · Forests · doi:10.3390/f17060727
“Among them, SIFT features are widely used for keypoint matching and image recognition because of their scale and rotation stability [38].”
Iterative Search with Local Visual Features for Computer Assisted Plant Identification
Ouertani, W., Bonnet, P., Crucianu, M., et al. (2010) · HAL (Le Centre pour la Communication Scientifique Directe)
FMS:Features Motion Statistics for Incorrect Matched-pair Removal
Shu, H., Liu, Z., Su, H., et al. (2022) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv57592.2022.10004363
Flexible Region Detection-based Genetic Programming for Fish Classification With Low-Quality Images
Fan, J., Bi, Y., Xue, B., & Zhang, M. (2025) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec65147.2025.11043012
Estimating 2D Camera Motion with Hybrid Motion Basis
Li, H., Zhou, T., Yang, Z., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00715
“Traditional methods typically rely on artificially extracted and matched keypoints [33], excelling under standard conditions but often struggling in adverse scenarios ( e.g ., rain, snow, and low light) or when confronted with non-planar motion caused by depth parallax or dynamic objects.”
InfluentialTrack Almost Everything Underwater: Evaluating Visual Registration in Marine Robotics
Mughal, M. W., Hussain, M. H., Chavez, A. G., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-17933-3_23
Method of Interest Points Characterization Based C-HOG Local Descriptor
Grand-Brochier, M., Tilmant, C., & Dhome, M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17277-9_23
Blending of three-dimensional geometric model shapes
Ng, S.-B., Ng, K.-W., Rahmat, R. W. O. K., et al. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v27.i1.pp102-109
Decoding Facial Recognition: Analyzing Standalone Euclidean and KNN Distance Metrics
Chhetri, P., & Kshetri, S. R. (2024) · Journal of Science and Technology · doi:10.3126/jost.v4i2.78952
Registration of egocentric views for collaborative localization in security applications
Chen, H. (2021) · theses.fr (ABES) · doi:10.70675/07287733z64d8z4367za00fz8a15d64beb7f
Lunar Remote Sensing Image Matching under a Limited Computational Budget
Luo, M. (2026) · MATEC Web of Conferences · doi:10.1051/matecconf/202642404007
Comment retrouver une constellation dans la galaxie ?
Zubiolo, A., Harb, K., Studer, M., et al. (2015) · HAL (Le Centre pour la Communication Scientifique Directe)
Local and global keypoint description for the high resolution remote sensing image registration
Liu, Z., & Wang, L. (2018) · International Conference on Digital Image Processing · doi:10.1117/12.2503075
Three-dimensional reconstruction of gigapixel whole-mount histopathology specimens with RAPID
Schouten, D., van der Laak, J. A. W. M., Somford, D. M., Küsters‐Vandevelde, H. V. N., Khalili, N., & Litjens, G. (2026) · Scientific Reports · doi:10.1038/s41598-026-46776-4
“Although we do not obtain any meaningful reconstructions for the SIFT-based method, the LightGlue-based method can achieve some correct reconstructions, albeit at a much lower overall accuracy than our proposed approach.”
Multi-View Image Rectification for UAV-Captured Image Sequences
Zou, S., Jin, X., & Fan, Y. (2023) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip59821.2023.10402769
The Feature Extraction and Matching Algorithm Based on the Fire Video Image Orientation
Lü, Y., Wang, H. Q., Xu, F., et al. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.380-384.3986
Star-YOLO: a lightweight and detail-aware network for infrared ship detection
Li, S., Huang, Q., Yang, Q., Zhang, X., Shi, L., & Hu, G. Y. (2025) · Applied Optics · doi:10.1364/ao.580648
A Comparative Study of Recent Biometric CryptoSystems
Kaur, P., Kumar, N., & Singh, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4170-3_20
Implementation of a Fire Detection System Based on YOLOv5
Lin, Y.-C., Luo, S. N., Kuan, T.-W., et al. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-7277-6_27
The current state of face tracking for video surveillance
Whiten, C., Bouachir, W., Bilodeau, G.-A., et al. (2012) · R-libre (Université Téluq)
One-shot Visual Reasoning on RPMs with an Application to Video Frame PredictionPreprint
He, W., Ren, J., & Bai, R. (2021) · arXiv (Cornell University)
Medical Image Similarity Matching Using Deep Learning
Sunday, P. N., Amagasaki, M., Kiyama, M., et al. (2025) · International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) · doi:10.1109/itc-cscc66376.2025.11137720
“While humans can effortlessly interpret disease markers or anatomical variations, traditional algorithms often rely on color histograms [5], texture descriptors like Gabor filters [6], or local keypoints such as SIFT [7], which lack the capacity to represent complex medical semantics effectively.”
Size and rotation invariant alphabet recognition
Rim, J., & Lee, C. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2263173
Pedestrian detection: A comparative study using HOG and CoHOG
Pandit, M. A., & Gautam, P. (2021) · International journal of applied research · doi:10.22271/allresearch.2021.v7.i2c.8260
Hand-held Augmented Reality for Facility Maintenance
Liu, F. (2016) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Netterstrøm, R., Kutuzov, N. P., Darkner, S., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.16903
An Interest Point Detector Based on Polynomial Local Orientation Tensor
Lin, R., Wang, W., Du, Z., et al. (2012) · Acta Automatica Sinica · doi:10.1016/s1874-1029(11)60290-5
Efficient Similarity Indexing and Searching in High DimensionsPreprint
Zhong, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.03090
Robust Subspace Position Measurement Using Localized Sub-Windows
Smit, A., & Schuurman, D. C. (2007) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2007.57
Segmentation Based Structure Matching
Wang, H., & Oliensis, J. (2007)
Compact Binary Fingerprint for Image Copy Re-RankingPreprint
Mohammad, N., Baber, J., Bakhtyar, M., Chandio, B. A., & Sanjrani, A. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.07802
An Authoring System for Interactive Mobile TV Services Using MPEG-7 and MPEG-4 LASeR
Hofmann, A., Kriechbaum, A., & Bailer, W. (2010) · Journal of Digital Information Management
Efficient Computing for Immersive VR: Real-Time Panoramic Video Stitching on GPU Platforms
Karthik, P. M., Nair, A. R., Philip, J. W., et al. (2024) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet61516.2024.10593115
“1) Scale-Invariant Feature Transform (SIFT): SIFT[5], depicted in Fig.”
MethodRobust feature selection for large scale image retrieval
Turcot, P. J. (2010) · cIRcle (University of British Columbia) · doi:10.14288/1.0051958
Region based on object recognition in 3D scenes
Xu, L., Zhou, Y., & Li, Q. (2011) · doi:10.1109/ivsurv.2011.6157013
Qiu, J., Ding, M., Li, K., et al. (2025) · Memetic Computing · doi:10.1007/s12293-025-00472-4
“SIFT could effectively handle image scale, rotation and illumination changes by extracting scale-invariant feature points [12].”
Learning to Generate High-Quality Images for Homography Estimation
Lin, Y., Wu, F., & Zhao, J. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897392
Li, Q., Xiao, W., & Chong-Wah, N. (2009) · Wiley Encyclopedia of Computer Science and Engineering · doi:10.1002/9780470050118.ecse576
Correspondence Learning via Correspondence Embedded and Channel Recalibration Network
Liu, Y., Zhao, S., Deng, H., et al. (2024) · ITM Web of Conferences · doi:10.1051/itmconf/20246000008
A two-stage matching algorithm on heterogeneous platforms for detecting Advertisement Logo in videos
Cao, X., Xu, C., & Xiu-hua, D. (2014) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2014.6895739
Gradient-adjusted Prediction Mode of Lossless Compression Based on Block
Wang, C., Shen, Y., Lu, Y., et al. (2013) · Jisuanji gongcheng
Using Ocular Data for Unconstrained Biometric Recognition
Proença, H., Santos, G. N. C., & Neves, J. C. (2014) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-4666-5966-7.ch012
Distance-based Multiple Paths Quantization of Vocabulary Tree for Object and Scene Retrieval
Yang, H., Wang, Q., & Yi–Luen, E. (2010) · IPSJ Transactions on Computer Vision and Applications · doi:10.2197/ipsjtcva.2.253
Spiral symmetry centroid detection
Wang, J., Feng, W., & Sun, J. (2014) · Electronics Letters · doi:10.1049/el.2014.0601
Developing Automatic Markerless Sign Language Gesture Tracking and Recognition System
Demidenko, S. N., Ooi, M. P., Akmeliawati, R., et al. (2019) · IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE) · doi:10.1109/have.2019.8921358
So, S., & Cho, S. (2025) · Advances in Structural Engineering · doi:10.1177/13694332251363352
“SIFT utilizes Difference-of-Gaussian (DoG) operations to achieve stable performance across diverse scales and orientations (Lowe, 2004).”
Analyse faciale avec dérivées Gaussiennes
Hernández, R., & Alexander, J. (2011)
Mutual On-Line Learning for Detection and Tracking in High-Resolution Images
Hurych, D., Zimmermann, K., & Svoboda, T. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-32350-8_15
Mitiche, A., & Aggarwal, J. K. (2013) · Springer topics in signal processing · doi:10.1007/978-3-319-00711-3_5
LiDAR Place Recognition Evaluation with the Oxford Radar RobotCar Dataset Revised
Peltomäki, J., Alijani, F., Puura, J., Huttunen, H. J., Rahtu, E., & Kämäräinen, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-31435-3_1
ESS-PoI: Energy-Driven Semantic Segmentation with Points of Interest
Huynh, T. P., & Huynh, H. X. (2025) · Asia Conference on Information Engineering (ACIE) · doi:10.1109/acie64499.2025.00025
“In the field of feature detection, Lowe [19] introduced SIFT (Scale-Invariant Feature Transform), a pioneering method for identifying and describing invariant features with scale and rotation in images.”
VIO-Aided Structure from Motion Under Challenging EnvironmentsPreprint
Jiang, Z., Taira, H., Miyashita, N., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.09657
Learning Class-Specific Segmentation
Shotton, J. (2004)
Comparative Analysis of the Feature Extraction Performance of Augmented Reality Algorithms
Tosun, U. (2019) · Cumhuriyet Science Journal · doi:10.17776/csj.638297
An Improved HIK for Object Categorization
Wu, L., Liu, Q., & Qin, W. (2015) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2015.257
Scalable near-duplicate video stream monitoring
Chiu, C., Tsai, T., & Hsieh, C.-Y. (2012) · International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) · doi:10.1109/ispacs.2012.6473444
Chang, Y., & Medioni, G. G. (2007) · 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.2007.383290
Efficient Multimedia Similarity Measurement Using Similar ElementsPreprint
Zhang, C., Lin, Y., Zhu, L., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03867
A Multi-Level Feature Fusion Algorithm under Resource Constraints for UAV Visual Localization
Dong, Y., Li, X., Man, X., Meng, F., Liu, S., & Lu, J. (2025) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus66297.2025.11295395
“In earlier studies, many researchers employed sophisticated handcrafted features, such as SIFT [3] and SURF [4].”
Grout, J. A., Sirven, P., Leader, A. M., Maskey, S., Hector, E., Puisieux, I., Steffan, F., Cheng, E., Tung, N., Maurin, M., et al. (2023) · doi:10.1158/2159-8290.22541751
Cat-eye target recognition with image registration of dual-CCD
Li, L., & Feng, B. (2015) · Optik · doi:10.1016/j.ijleo.2015.08.049
Minomo, Y., Sato, M., Suzuki, T., et al. (2016) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.70.j75
Gupta, A., Lalwani, H. K., Paul, S., et al. (2023) · IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) · doi:10.1109/rasse60029.2023.10363500
Study on pranoramic virtual reality system based on Webgis
Wang, S., Du, J., & Gao, T. X. (2010) · IEEE International Conference on Cognitive Informatics · doi:10.1109/coginf.2010.5599749
An analytic training approach for recognition in still images and videos
Minhas, R. (2010) · Scholarship at UWindsor (University of Windsor)
Wide-Depth-Range 6D Object Pose Estimation in SpacePreprint
Hu, Y., Speierer, S., Jakob, W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00337
Robust Content based Image Retrieval System for Face Recognitionfv
KAMAL, S., & Li, Z. (2015) · International Conference on Advances in Computing, Control and Networking (ACCN) · doi:10.15224/978-1-63248-038-5-08
Fusion of near-infrared single-photon LiDAR with visible light camera
Qiu, L., Zheng, H., Han, Y., Li, B., Hao, W., Dang, Z., Yu, Y., Li, J., Xu, H. Z., Chen, H., et al. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/addddf
An Infrared Moving Small Object Detection Method Based on Trajectory Growth
Li, D., Pan, S., Wang, H., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18916-6_48
[Paper] Relative-Distance-Based Soft Voting for Human Attribute Analysis using Top-View Images
Yamasaki, T., & Chen, T. (2013) · ITE Transactions on Media Technology and Applications · doi:10.3169/mta.1.138
AI-empowered promotional video generation
Liu, C. (2022) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/161247 · Cited by 1
Augment color discrimination of local descriptors
Liu, Y., Wang, H., & Qin, Y. (2017) · Optik · doi:10.1016/j.ijleo.2017.04.083
Tracking Moving Objects Improves Recognition.
Dhulekar, N., Felch, A., & Granger, R. (2010)
Jia, P., Luo, H., Gao, S., Li, Z., Cai, J., Huang, G., Wang, L., Song, Z., Liu, Z., Li, Y., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109252
Research on the Key Technology of Survey Measurement Image Based on UAV
Ding, L., & Jiao, C. (2018) · International Journal of Advanced Network Monitoring and Controls · doi:10.21307/ijanmc-2018-053
A General Data Tool of Mining Graphic Feature Patterns from Photonic Crystal SpheresPreprint
Zhong, H., Li, G., Gan, Y., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5160201
Preset parameters calibration of focal stack computational imaging system
Hao, Z., Liu, C., & Deng, X. (2022) · Asia Pacific Conference on Optics Manufacture and International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM) · doi:10.1117/12.2617688
AlikE Content Detection in Image and Video
k, S. T., Menon, V. R., & Sreerag, S. (2015) · International Journal For Science Technology And Engineering
Remote-Sensing Image Stitching Based on Infinite Homography and Global Seam Finding
Chang, J., Li, Q., Liang, Y., et al. (2024) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126624503079
Recognition of Layout-Free Characters on Complex Background
Iwamura, M., Kobayashi, T., Matsuda, T., et al. (2013) · IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2013.191
Joint use of luminance and color invariants in partial image retrieval
Matsumoto, W., & Kameyama, K. (2013) · International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) · doi:10.1109/ispacs.2013.6704621
Hot Research Topics in Video Coding and Systems
Gao, W., & Ma, S. (2014) · Springer eBooks · doi:10.1007/978-3-319-14243-2_12
Kasem, H. M., Atef, S., & NASR, M. E. M. (2023) · Delta University Scientific Journal · doi:10.21608/dusj.2023.318654
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
