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 37,101–37,200 of 56,201
Nadi, M. A. B., & Fauzan, S. A. (2019) · Journal of Science and Applicative Technology · doi:10.35472/281449 · Cited by 2
Tomašev, N., & Mladenić, D. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2254-1 · Cited by 2
DFTD-YOLO: Lightweight Multi-Target Detection From Unmanned Aerial Vehicle Viewpoints
Chen, Y.-T., & Liu, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3535624 · Cited by 2
Framework for texture classification and retrieval using scale invariant feature transform
Do, T., Aikala, A., & Saarela, O. (2012) · doi:10.1109/jcsse.2012.6261967 · Cited by 2
Visual Object Class Recognition
Stark, M. M., Schiele, B., & Leonardis, A. (2016) · Springer handbooks · doi:10.1007/978-3-319-32552-1_33 · Cited by 2
Canonical Plane Segmentation Without Annotating Pixel-Level Object Regions for Image Registration
Yoneda, S., Irie, G., & Nishiyama, M. (2024) · IEEE Access · doi:10.1109/access.2024.3373463 · Cited by 2
Vehicle tracking from disparate views
Yang, L., Johnstone, J. K., & Zhang, C. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202551 · Cited by 2
Efficient keypoint detection and description using filter kernel decomposition in scale space
Okutani, R., Sugimoto, K., & Kamata, S. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532313 · Cited by 2
Unsupervised Cross-Domain Deep-Fused Feature Descriptor for Efficient Image Retrieval
He, Q. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3519175 · Cited by 3
Scale-Invariant Feature Transform Improvement for Problems of Recognition of Similar Images
Kachalov, K. A., & Kharchenko, S. S. (2022) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661822030178 · Cited by 2
UAV Visual Localization Technology Based on Heterogenous Remote Sensing Image Matching
Tang, H., Shi, J., Miao, X., et al. (2022) · doi:10.1145/3573942.3574094 · Cited by 2
Dynamic Environments Localization via Dimensions Reduction of Deep Learning Features
Zhang, H., Wang, X., Du, X., Liu, M., & Chen, Q. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68345-4_22 · Cited by 3
A general elimination strategy for camera motion estimation
Ding, Y., Su, Y., Xu, C., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561300 · Cited by 2
A Robust Method Applied to Human Detection
Hosseini, S. M., & Farsi, H. (2010) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2010.v2.226 · Cited by 2
AIRBORNE SYNTHETIC SCENE GENERATION (AEROSYNTH)
Walli, K. C., Schott, J. R., & Salvaggio, C. (2009) · Cited by 2
Kyutoku, H., Akita, T., & Mita, S. (2020) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294170 · Cited by 2
Robust Change Detection in High Resolution Satellite Images with Geometric Distortions
Jin, D., Lim, K., & Kim, C. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023244 · Cited by 2
Robust image region descriptor using local derivative ordinal binary pattern
Shang, J., Chen, C., Pei, X., et al. (2015) · Journal of Electronic Imaging · doi:10.1117/1.jei.24.3.033009 · Cited by 2
Semantic Description of Objects in Images Based on Prototype Theory
Pino, O. V., Nascimento, E. R., & Campos, M. F. M. (2020) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.5753/sibgrapi.est.2020.12994 · Cited by 2
el Shehaby, M., Youssef, S. M., & Fayed, S. F. (2019) · International Conference on Communications, Signal Processing, and their Applications (ICCSPA) · doi:10.1109/iccspa.2019.8713651 · Cited by 2
Radial Line Fourier Descriptor for Handwritten Word Representation.Preprint
Hast, A., & Vats, E. (2017) · arXiv (Cornell University) · Cited by 2
How smart does your profile image look? Estimating intelligence from social network profile images
Wei, X., & Stillwell, D. J. (2016) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.48550/arxiv.1606.09264 · Cited by 2
Object Movement Computation from Two Images
Park, J., & Park, S. H. (2015) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.752-753.1085 · Cited by 2
LMFE-Net: A lightweight multi-scale pedestrian feature enhancement network for complex scenarios
Yuan, T., Lai, H., Tang, J., et al. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.113860 · Cited by 2
Li, X., Lei Zhang, A., Jian, Z., & Zuo, L. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10502-0 · Cited by 2
Learning Deep ISP for High-Speed Cameras: Achieving DSLR-Quality Imaging Under High Frame Rates
Chen, H., Liu, T., Tu, T., Wei, W., Jin, Y., & Chen, E. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3637771 · Cited by 2
“These misalignments are difficult to resolve using existing alignment methods [60], [63], [64], [67] due to severe color and brightness discrepancies.”
RGBD Object Recognition and Flat Area Analysis Method for Manipulator Grasping
Wang, K., & Liu, S. (2020) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls49620.2020.9275208 · Cited by 2
(2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6974594 · Cited by 2
Yang, B.-H., & Lian, F. (2016) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2016.7474977 · Cited by 2
Temu Kembali Berbasis Citra untuk Menemukan Kemiripan Merek Menggunakan Algoritma SIFT dan SURF
Zuliarso, E., Sulastri, S., & Anis, Y. (2022) · Jurnal Buana Informatika · doi:10.24002/jbi.v13i02.6328 · Cited by 2
Estimation of Crowd Density Based on Adaptive LBP
Li, Y., Zou, T., & Chen, P. (2014) · Advanced materials research · doi:10.4028/www.scientific.net/amr.998-999.864 · Cited by 2
Postsynaptic mitochondria are positioned to support functional diversity of dendritic spinesPreprint
Thomas, C. I., Ryan, M. A., Kamasawa, N., & Scholl, B. (2023) · eLife · doi:10.7554/elife.89682.1 · Cited by 3
Machine learning nuclear detonation features
Schmitt, D. T., & Peterson, G. L. (2014) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2014.7041936 · Cited by 2
Stereo Vision based Localization of a Robot using Partial Depth Estimation and Particle Filter
Prabu, S., & Hu, G. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140824-6-za-1003.01599 · Cited by 2
Semantic segmentation of oblique UAV video based on ConvLSTM in complex urban area
Majidizadeh, A., Hasani, H., & Jafari, M. (2024) · Earth Science Informatics · doi:10.1007/s12145-024-01355-x · Cited by 3
Privacy Preserving Visual SLAMPreprint
Shibuya, M., Sumikura, S., & Sakurada, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10361 · Cited by 2
Application of Analytics in Machine Vision using Big Data
Elfiky, N. M. (2019) · Asian Journal of Applied Sciences · doi:10.24203/ajas.v7i4.5910 · Cited by 2
Methodology for image retrieval based on binary space partitioning and perceptual image hashing
Hodovychenko, M. A., Antoshchuk, S. G., & Kuvaieva, V. (2022) · Applied Aspects of Information Technology · doi:10.15276/aait.05.2022.10 · Cited by 2
A Discriminatively Learned CNN Embedding For Remote Sensing Image Scene Classification
Wang, W., Du, L., Gao, Y., et al. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8900069 · Cited by 2
A study into annotation ranking metrics in geo-tagged image corpora
Hughes, M. M., Jones, G. J. F., & O'Connor, N. E. (2012) · Arrow@dit (Dublin Institute of Technology) · Cited by 2
Data Partitioning Technique for Online and Incremental Visual SLAM
Tongprasit, N., Kawewong, A., & Hasegawa, O. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10677-4_88 · Cited by 2
SURF Feature Description of Color Image Based on Gaussian Model
Sun, W. B., Shen, Q., & Liu, C. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-0356-1_28 · Cited by 2
A computationally efficient pipeline for 3D point cloud reconstruction from video sequences
Kehtarnavaz, N., & Chang, C. (2018) · doi:10.1117/12.2302674 · Cited by 2
Compact descriptor for local feature using dominating centre‐symmetric local binary pattern
Li, Y., Tan, J., Zhong, J., et al. (2015) · IET Computer Vision · doi:10.1049/iet-cvi.2014.0394 · Cited by 2
Enhanced image coverage using evolutionary keypoint selection
Bostancı, E. (2017) · The Imaging Science Journal · doi:10.48550/arxiv.1512.03155 · Cited by 3
Lukianova, E., Jeong, J.-Y., & Jeong, J.-W. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712087 · Cited by 3
“Specifcally, to discern meaningful changes between successive images, we utilize the SIFT [37] feature extraction algorithm, which can extract distinctive invariant features from images, allowing for reliable matching between different views of an object or scene.”
MDS-SIFT: An improved SIFT matching algorithm based on MDS dimensionality reduction
Zhou, Z., Shimeng, C., & Li, Z. (2016) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2016.7811078 · Cited by 2
Qu, W., Zhou, X., Wang, D., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48740-3_16 · Cited by 2
Thai Font Type Recognition Using SIFT
Jamjuntr, P., & Dejdumrong, N. (2012) · International Conference on Computer Graphics, Imaging and Visualization (CGIV) · doi:10.1109/cgiv.2012.23 · Cited by 2
Cluster-Wise Ratio Tests for Fast Camera LocalizationPreprint
Díaz, R. A., & Fowlkes, C. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.01689 · Cited by 2
RE-TRIP: Reflectivity Instance Augmented Triangle Descriptor for 3D Place Recognition
Park, Y., Pak, G., & Kim, E. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128417 · Cited by 2
Weakly-Supervised Deep Shape-From-Template
Luengo-Sánchez, S., Fuentes-Jiménez, D., Losada-Gutiérrez, C., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3534271 · Cited by 2
Enhancing Content-Based Histopathology Image Retrieval Using QR Code Representation
Rouzegar, H., Rahnamayan, S., Bidgoli, A. A., et al. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10371928 · Cited by 2
Context-Based Adaptive Filtering of Interest Points in Image Retrieval
Giang, N. T. P., & Andersen, H. J. (2009) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2009.25 · Cited by 2
S4Net: Single Stage Salient-Instance SegmentationPreprint
Fan, R., Cheng, M., Hou, Q., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07618 · Cited by 2
Fast Manifold Ranking With Local Bipartite Graph
Chen, X., Ye, Y., Wu, Q., & Nie, F. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3096082 · Cited by 2
Trainable Siamese keypoint descriptors for real-time applications
Fedorenko, F., Ivanova, A. A., Limonova, E. E., & Konovalenko, I. A. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2266351 · Cited by 2
Performance evaluation of feature detection in using subsampled images for image stitching
Chia, W. C., Ang, L.-M., & Seng, K. P. (2010) · doi:10.1109/iccsit.2010.5564525 · Cited by 2
ActionPool : a novel dynamic task scheduling method for service robots
Taipalus, T. (2010) · Aaltodoc (Aalto University) · Cited by 2
A simple and effective postprocessing method for image classificationPreprint
Liu, Y., Li, Y., Yuan, Y., & Qiang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07934 · Cited by 2
Service Action Recognition in Power Supply Business Hall with 3D-Fused ConvNet
Lin, T., Ouyang, L., Wen, H., Xiong, D., & Smulko, J. (2021) · Advances in Electrical and Electronic Engineering · doi:10.15598/aeee.v19i1.3950 · Cited by 2
3D Gaussian Splatting for Lunar Surface with Limited View Input
Zhang, X., Cui, L., Zhang, X., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3619948 · Cited by 2
Liu, S., Wan, Y., Li, K., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104518 · Cited by 2
Video Stream Relocalization With Deep Learning
Hu, T., & Sun, H. (2018) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2018.8599392 · Cited by 2
Aggregating Low-Level Features for Human Action Recognition
Parrigan, K., & Souvenir, R. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17289-2_14 · Cited by 2
Relatively-Paired Space Analysis
Kuang, Z., & Wong, K. K. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.131 · Cited by 2
Fundamentals of Machine Vision
Stiller, C., Bachmann, A., & Geiger, A. (2015) · Springer eBooks · doi:10.1007/978-3-319-09840-1_21-1 · Cited by 2
MicroVi: A Cost-Effective Microscopy Solution for Yeast Cell Detection and Count in Wine Value Chain
Benito-Altamirano, I., Moreno, S. R., Vaz-Romero, D. M., et al. (2025) · Biosensors · doi:10.3390/bios15010040 · Cited by 2
Universal blind image quality assessment for stereoscopic images
Fezza, S. A., Chetouani, A., & Larabi, M. (2016) · 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video · doi:10.1109/3dtv.2016.7548888 · Cited by 2
Kosch, S., & Ashgriz, N. (2016) · Measurement · doi:10.1016/j.measurement.2016.07.063 · Cited by 2
Automatic Model-Based Stationing: Robust Total Station Localization Without Known Control Points
Reyes‐Aviles, F., Gloor, T., & Arth, C. (2025) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-025-02224-5 · Cited by 2
Pan, S., Cheng, X., & Fan, W. (2025) · IEEE Access · doi:10.1109/access.2025.3540477 · Cited by 3
Barella, R., Marín, C., Callegari, M., et al. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9553989 · Cited by 2
Loose fusion based on SLAM and IMU for indoor environment
Wang, Z., Zhu, H., Zhou, J., et al. (2018) · International Conference on Graphic and Image Processing (ICGIP) · doi:10.1117/12.2302929 · Cited by 2
Radyo Haberleşmesinde Evrişimli Sinir Ağı Kullanılarak Yapılan Modülasyon Sınıflandırması
Çalışır, B. (2022) · Fırat Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.35234/fumbd.1141515 · Cited by 2
Han, A.-H., Park, C.-S., Kim, S.-S., et al. (2010) · Journal of the Korea Society for Simulation · Cited by 2
Using consistency of depth gradient to improve visual tracking in RGB-D sequences
Shi, H., Gao, C., & Sang, N. (2015) · Chinese Automation Congress · doi:10.1109/cac.2015.7382555 · Cited by 1
Gui, S., & Qin, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03876 · Cited by 2
All-day Image Alignment for PTZ Surveillance Based on Correlated Siamese Neural Network
Hu, Z., Zheng, X., Wang, S., et al. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02720-x · Cited by 2
An Attention-Based Image Retrieval System
Özyer, G. T., & Vural, F. Y. (2011) · doi:10.1109/icmla.2011.27 · Cited by 2
The Shape of Data and Probability MeasuresPreprint
Martínez, D. H. D., Mémoli, F., & Mio, W. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.04632 · Cited by 2
Enhancing Normal-Abnormal Classification Accuracy in Colonoscopy Videos via Temporal Consistency
Puerto-Souza, G. A., Manivannan, S., Trujillo, M., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29965-5_13 · Cited by 2
Shape Representations for Maya Codical Glyphs
Can, G., Odobez, J., & Gática-Pérez, D. (2017) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3095713.3095746 · Cited by 2
Whitten, D., Brink, K., & Hurtado, J. E. (2017) · Proceedings of the ION ... Pacific PNT Meeting/Proceedings of the Institute of Navigation Pacific PNT · doi:10.33012/2017.15024 · Cited by 2
Wang, J., Liang, Y., Yang, X., Zhao, M., & Xia, J. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07769-8 · Cited by 3
“Figure 9 illustrates the feature point detection results obtained using the SIFT [69] algorithm.”
Recovering planar projections of printed clustered-dot halftones
Pollard, S. B., Ulichney, R. A., & Gaubatz, M. D. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025858 · Cited by 2
A Review of Machine Vision Pose Measurement
(2023) · doi:10.20944/preprints202305.2164.v1 · Cited by 2
Content Based Video Retrieval—Methods, Techniques and Applications
Mounika, B., Palanisamy, P., & Khare, A. (2021) · Studies in big data · doi:10.1007/978-3-030-75657-4_4 · Cited by 2
Viewpoint calibration method based on point features for point cloud fusion
Zhang, A. L., Zhang, B. X., Song, C., et al. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296677 · Cited by 2
Bifocal Matching Using Multiple Geometrical Solutions
Carrasco, M., & Mery, D. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25346-1_18 · Cited by 2
Zero-Shot-Learning for Plant Species Classification
Maachi, S. E., Chehri, A., & Saadane, R. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.492 · Cited by 3
Independent Components Analysis for Representation Interest Point Descriptors
Han, D., Li, W., Wang, T., et al. (2006) · Lecture notes in computer science · doi:10.1007/11816157_152 · Cited by 2
Getting Rid of Night: Thermal Image Classification Based on Feature Fusion
Lu, G., Yu, H., & Yuan, C. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545321 · Cited by 2
Bi, Y., Xue, B., & Zhang, M. (2020) · doi:10.26686/wgtn.13158320.v1 · Cited by 2
Template Matching for Wide-Baseline Panoramic Images from a Vehicle-Borne Multi-Camera Rig
Ji, S., Yu, D., Hong, Y., et al. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7070236 · Cited by 2
Dense Correspondence using Local Regions with Affine Transformations
Reyes, A., & López-Juárez, I. (2016) · IEEE Latin America Transactions · doi:10.1109/tla.2016.7437237 · Cited by 2
Iris Occluded Image Classification Using Color and SIFT Features
Păvăloi, I., & Ignat, A. (2019) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb47216.2019.8970046 · Cited by 2
Heterogeneous Pairwise-Semantic Enhancement Hashing for Large-Scale Cross-Modal Retrieval
Wong, W. K., Fei, L., Qin, J., et al. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3535401 · Cited by 2
Object Recognition Based on Maximally Stable Extremal Region and Scale-Invariant Feature Transform
Guo, H., & Chen, L. (2016) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v14i2.2754 · Cited by 2
Uniqueness Filtering for Local Feature Descriptors in Urban Building Recognition
Giang, N. T. P., & Andersen, H. J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69905-7_10 · 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
