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 16,401–16,500 of 56,201
New integration approach of photogrammetric and LIDAR techninques for architectural surveys
Nex, F., & Rinaudo, F. (2009) · PORTO@Iris · Cited by 16
Image based information access for mobile phones
Chen, X., Koskela, M., & Hyväkkä, J. (2010) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2010.5529886 · Cited by 16
Real-Time Construction Site Layout and Equipment Monitoring
Liang, C., Kamat, V. R., & Menassa, C. M. (2018) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784481264.007 · Cited by 16
Accelerating parallel digital image correlation computation with feature mesh interpolation
Chi, Y., & Pan, B. (2022) · Measurement · doi:10.1016/j.measurement.2022.111554 · Cited by 16
Automatic montage of retinal images in adaptive optics confocal scanning laser ophthalmoscope
Li, H., Lü, J., Shi, G., et al. (2012) · Optical Engineering · doi:10.1117/1.oe.51.5.057008 · Cited by 16
Action recognition using 3D DAISY descriptor
Cao, X., Zhang, H., Deng, C., et al. (2013) · Machine Vision and Applications · doi:10.1007/s00138-013-0545-6 · Cited by 16
Image representations on a budget: Traffic scene classification in a restricted bandwidth scenario
Sikiric, I., Brkić, K., Krapac, J., & Šegvić, S. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856569 · Cited by 16
A robust detection algorithm for image Copy-Move forgery in smooth regions
Emam, M., Han, Q., Li, Q., et al. (2017) · International Conference on Circuits, System and Simulation (ICCSS) · doi:10.1109/cirsyssim.2017.8023194 · Cited by 16
All-sky photogrammetry techniques to georeference a cloud field
Crispel, P., & Roberts, G. (2018) · Atmospheric measurement techniques · doi:10.5194/amt-11-593-2018 · Cited by 16
Real-time moving pedestrian detection using contour features
Zhao, K., Deng, J., & Cheng, D. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6173-4 · Cited by 16
Tracking Objects with Adaptive Feature Patches for PTZ Camera Visual Surveillance
Xie, Y. F., Lin, L., & Jia, Y. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.430 · Cited by 16
Assistive system for physically disabled people using gesture recognition
Chattoraj, S., Vishwakarma, K., & Paul, T. (2017) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2017.8124506 · Cited by 16
Image resection and heat transfer measurements by IR thermography in hypersonic flows
Avallone, F., Greco, C. S., & Ekelschot, D. (2013) · Quantitative InfraRed Thermography Journal · doi:10.1080/17686733.2013.800697 · Cited by 16
Substation equipment image recognition based on SIFT feature matching
Zhou, Q., & Zhao, Z. (2012) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2012.6469854 · Cited by 16
INVARIANT PATTERN RECOGNITION USING RIDGELET PACKETS AND THE FOURIER TRANSFORM
Chen, G. Y., & Bhattacharya, P. (2009) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s0219691309002854 · Cited by 16
Wang, K., Kumar, V., Zeng, X., et al. (2019) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.190704.002 · Cited by 16
Hu, L., & Nooshabadi, S. (2019) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2019.06.003 · Cited by 16
Tella-Amo, M., Peter, L., Shakir, D. I., et al. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.2.021217 · Cited by 16
Global structure-guided learning framework for underwater image enhancement
Lin, R., Liu, J., Liu, R., et al. (2021) · The Visual Computer · doi:10.1007/s00371-021-02305-0 · Cited by 16
Utilising 3D Realistic Models in Serious Games for Cultural Heritage
Kontogianni, G., Koutsaftis, C., Skamantzari, M., et al. (2017) · International Journal of Computational Methods in Heritage Science · doi:10.4018/ijcmhs.2017070102 · Cited by 16
Content-Aware Eye Tracking for Autostereoscopic 3D Display
Kang, D., & Heo, J. (2020) · Sensors · doi:10.3390/s20174787 · Cited by 16
Hard Decorrelated Centralized Loss for fine-grained image retrieval
Zeng, X., Liu, S., Wang, X., Zhang, Y., Chen, K., & Li, D. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.030 · Cited by 16
Object Detection for Low-resolution Infrared Image in Land Battlefield Based on Deep Learning
Zheng, G., Xin-jian, W., Hu, Y., et al. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8866344 · Cited by 16
Semantic Kernels Binarized - A Feature Descriptor for Fast and Robust Matching
Zilly, F., Riechert, C., Eisert, P., & Kauff, P. P. (2011) · Conference on Visual Media Production · doi:10.1109/cvmp.2011.11 · Cited by 16
Wang, Z., Qin, C., & Mu, X. (2024) · Sensors · doi:10.3390/s24061937 · Cited by 20
“These factors pose challenges for SLAM methods based on hand-crafted features such as SIFT [5], ORB [6], and Shi-Tomasi [7] in terms of ensuring consistent feature extraction and tracking.”
MethodEZ-VSN: An Open-Source and Flexible Framework for Visual Sensor Networks
Bondi, L., Baroffio, L., Cesana, M., Redondi, A. E. C., & Tagliasacchi, M. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2504622 · Cited by 16
Improving VLAD: Hierarchical coding and a refined local coordinate system
Eggert, C., Romberg, S., & Lienhart, R. W. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025610 · Cited by 16
Challenges in Image Matching for Cultural Heritage: An Overview and Perspective
Bellavia, F., Colombo, C., Morelli, L., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13321-3_19 · Cited by 16
A palmprint based recognition system for smartphone
Tiwari, K., Hwang, C. J., & Gupta, P. (2016) · Future Technologies Conference · doi:10.1109/ftc.2016.7821664 · Cited by 16
The use of attention and spatial information for rapid facial recognition in video
Bonaiuto, J. J., & Itti, L. (2005) · Image and Vision Computing · doi:10.1016/j.imavis.2005.09.008 · Cited by 16
Zhang, H., Xu, H., Liu, H., Yu, X., Zhang, X., & Wu, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130845 · Cited by 20
Implicit Shape Kernel for Discriminative Learning of the Hough Transform Detector
Zhang, Y., & Chen, T. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.105 · Cited by 16
Similar Manga Retrieval Using Visual Vocabulary Based on Regions of Interest
Sun, W., & Kise, K. (2011) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2011.217 · Cited by 16
Sharma, K., Moon, I., & Kim, S. (2012) · IETE Technical Review · doi:10.4103/0256-4602.105002 · Cited by 16
Face recognition based on matching of local features on 3D dynamic range sequences
Echeagaray-Patrón, B. A., & Kober, V. I. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2236355 · Cited by 16
Accumulated Visual Representation for Cognitive Vision
Pugeault, N., Woergoetter, F., & Krueger, N. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.98 · Cited by 16
Condensation-based multi-person detection and tracking with HOG and LBP
Li, B., Yang, C., Zhang, Q., et al. (2014) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2014.6932665 · Cited by 16
New colour SIFT descriptors for image classification with applications to biometrics
Verma, A., Liu, C., & Jia, N. (2010) · International Journal of Biometrics · doi:10.1504/ijbm.2011.037714 · Cited by 16
A flexible free-space detection system based on stereo vision
Xie, Q., Liu, R., Sun, Z., Pei, S., & Cui, F. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.115 · Cited by 17
Bilinear random projections for locality-sensitive binary codes
Kim, S., & Choi, S. (2015) · 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.2015.7298739 · Cited by 16
Vision Analysis System for Autonomous Landing of Micro Drone
Skoczylas, M. (2014) · Acta Mechanica et Automatica · doi:10.2478/ama-2014-0036 · Cited by 16
Wang, H., Wang, L., Chen, H., et al. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad042a · Cited by 16
SIFT-based iris recognition revisited: prerequisites, advantages and improvements
Rathgeb, C., Wagner, J., & Busch, C. (2018) · Pattern Analysis and Applications · doi:10.1007/s10044-018-0719-y · Cited by 16
Virtually transparent epidermal imagery for laparo-endoscopic single-site surgery
Sun, Y., Anderson, A., Castro, C., et al. (2011) · doi:10.1109/iembs.2011.6090392 · Cited by 16
Tong, Q., Zhang, S., Hu, S., et al. (2011) · doi:10.1145/2024676.2024681 · Cited by 16
Khalid, M. J., Irfan, M., Ali, T. M., Gull, M., Draz, U., Głowacz, A., Sułowicz, M., Dziechciarz, A., Alkahtani, F., & Hussain, S. (2020) · Electronics · doi:10.3390/electronics9111886 · Cited by 16
RBRIEF: a robust descriptor based on random binary comparisons
Huang, W., Wu, L., Song, H., & Wei, Y. (2013) · IET Computer Vision · doi:10.1049/iet-cvi.2012.0087 · Cited by 16
A Bag-of-Features Algorithm for Applications Using a NoSQL Database
Gabryel, M. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-46254-7_26 · Cited by 16
Footwear for Gender Recognition
Yuan, Y., Pang, Y., & Li, X. (2009) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2009.2022795 · Cited by 16
Learning multi-view visual correspondences with self-supervision
Zhang, P., Zhou, L., Bai, X. P., Wang, C., Zhou, J. W., Zhang, L., & Zheng, J. Y. (2022) · Displays · doi:10.1016/j.displa.2022.102160 · Cited by 17
Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition
Yan, Z., Zhan, Y., Zhang, S., Metaxas, D., & Zhou, X. S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-810408-8.00006-7 · Cited by 18
Li, T., Ma, Y.-T., Shen, H., & Endoh, T. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2976958 · Cited by 11
A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry
Jasińska, A., Pyka, K., Pastucha, E., et al. (2023) · Sensors · doi:10.3390/s23020728 · Cited by 16
FPFH Revisited: Histogram Resolutions, Improved Features, and Novel Representation
Szalai-Gindl, J. M., & Varga, D. (2024) · IEEE Access · doi:10.1109/access.2024.3400591 · Cited by 20
A Survey of Recent View-based 3D Model Retrieval MethodsPreprint
Liu, Q. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1208.3670 · Cited by 16
Gesture detection system using smart watch based motion sensors
Gouthaman, S., Pandya, A., Karande, O., et al. (2014) · doi:10.1109/cscita.2014.6839278 · Cited by 16
Tolias, G., Kalantidis, Y., & Avrithis, Y. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2393379 · Cited by 16
Chen, C., & Wang, H. (2006) · The International Journal of Robotics Research · doi:10.1177/0278364906068375 · Cited by 16
Compact Local Directional Texture Pattern for Local Image Description
Zeng, H., Zhang, R., Huang, M., & Wang, X. (2015) · Advances in Multimedia · doi:10.1155/2015/360186 · Cited by 16
UAV image stitching by estimating orthograph with RGB cameras
Cai, W., Ду, С., & Yang, W. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103835 · Cited by 16
Intensity guided cost metric for fast stereo matching under radiometric variations
Khan, A., Khan, M. U. K., & Kyung, C. (2018) · Optics Express · doi:10.1364/oe.26.004096 · Cited by 16
Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method
Möller, R., Horst, M., & Fleer, D. (2014) · Robotics · doi:10.3390/robotics3010022 · Cited by 16
Robust Point Matching Using Mixture of Asymmetric Gaussians for Nonrigid Transformation
Wang, G., Wang, Z., Zhao, W., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16817-3_28 · Cited by 16
Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing Image Scene Classification
Jiang, N., Shi, H., & Geng, J. (2022) · Remote Sensing · doi:10.3390/rs14215550 · Cited by 16
Semisupervised Center Loss for Remote Sensing Image Scene Classification
Zhang, J., Zhang, M., Pan, B., et al. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2978864 · Cited by 16
Adaptive Component Discrimination Network for Airplane Detection in Remote Sensing Images
Jia, H., Guo, Q. J., Chen, J., Wang, F., Wang, H., & Xu, F. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3098296 · Cited by 16
Industrial Monitoring Using Image Processing, IoT and Analyzing the Sensor Values Using Big Data
Rukmani, P., Teja, G. K., Vinay, M. S., et al. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.07.077 · Cited by 16
A Robust Indoor Scene Recognition Method Based on Sparse Representation
Nascimento, G., Laranjeira, C., Braz, V., Lacerda, A. M., & Nascimento, E. R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75193-1_49 · Cited by 17
Hare, J. S., Samangooei, S., Dupplaw, D., et al. (2013) · doi:10.1145/2461466.2461514 · Cited by 16
Decentralized Visual-Inertial Localization and Mapping on Mobile Devices for Augmented Reality
Sartipi, K., DuToit, R. C., Cobar, C. B., & Roumeliotis, S. I. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967804 · Cited by 15
Content-Based Image Retrieval and the Semantic Gap in the Deep Learning Era
Barz, B., & Denzler, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68790-8_20 · Cited by 23
Full Reference Printed Image Quality: Measurement Framework and Statistical Evaluation
Eerola, T., Lensu, L. T., Kalviainen, H. A., Kämäräinen, J., Leisti, T., Nyman, G., Halonen, R., & Oittinen, P. T. (2010) · Journal of Imaging Science and Technology · doi:10.2352/j.imagingsci.technol.2010.54.1.010201 · Cited by 16
Neumann, C., Förster, M., Kleinschmit, B., et al. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2536199 · Cited by 16
Handwritten word image matching based on Heat Kernel Signature
Zhang, X., & Tan, C. L. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.10.028 · Cited by 16
Self-supervised on-line cumulative learning from video streams
Pernici, F., Bruni, M., & Bimbo, A. D. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.102983 · Cited by 17
A novel dynamic multi-model relevance feedback procedure for content-based image retrieval
de Ves, E., Benavent, X., Coma, I., et al. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.02.073 · Cited by 16
Özen, Y., Aksoy, S., Kösemehmetoğlu, K., Onder, S. C., & Üner, A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412903 · Cited by 21
Implementation of high performance hardware architecture of OpenSURF algorithm on FPGA
Fan, X., Wu, C., Cao, W., et al. (2013) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/fpt.2013.6718346 · Cited by 16
Harmonic Filters for Generic Feature Detection in 3D
Reisert, M., & Burkhardt, H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03798-6_14 · Cited by 16
OpenCL-Based Design of an FPGA Accelerator for Phase-Based Correspondence Matching
Tatsumi, S., Hariyama, M., Miura, M., et al. (2015) · Cited by 16
Markerless Tracking for Augmented Reality
Herling, J., & Broll, W. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0064-6_11 · Cited by 16
Automatic loop closure detection using multiple cameras for 3D indoor localization
Kua, J., Corso, N., & Zakhor, A. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.916639 · Cited by 16
A Multi-modal Clustering Method for Web Videos
Huang, H., Lu, Y., Zhang, F., et al. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-35795-4_21 · Cited by 16
Nie, H., Fu, Z., Tang, B., Li, Z., Chen, S., & Wang, L. (2022) · Remote Sensing · doi:10.3390/rs14122946 · Cited by 17
Mono visual odometry for Autonomous Underwater Vehicles navigation
Zacchini, L., Bucci, A., Franchi, M., Costanzi, R., & Ridolfi, A. (2019) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceanse.2019.8867286 · Cited by 18
GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
Hoang, L., Lee, S., Lee, E.-J., & Kwon, K. (2022) · Applied Sciences · doi:10.3390/app12010483 · Cited by 16
Real-time landslide monitoring using close-range stereo image sequences analysis
Feng, T., Liu, X., Scaioni, M., et al. (2012) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2012.6223569 · Cited by 16
A camera on-line recalibration framework using SIFT
Li, C., Lü, P., & Ma, L. (2009) · The Visual Computer · doi:10.1007/s00371-009-0400-y · Cited by 16
Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network
Yao, G., Yılmaz, A., Zhang, L., Meng, F., Ai, H., & Jin, F. (2021) · Remote Sensing · doi:10.3390/rs13020274 · Cited by 16
Ruíz-del-Solar, J., Loncomilla, P., & Devia, C. (2007) · Springer eBooks · doi:10.1007/978-3-540-77129-6_51 · Cited by 16
Vision-Based Georeferencing of GPR in Urban Areas
Barzaghi, R., Cazzaniga, N. E., Pagliari, D., et al. (2016) · Sensors · doi:10.3390/s16010132 · Cited by 16
A 12-camera body scanning system based on close-range photogrammetry for precise applications
Pesce, M., Galantucci, L. M., & Lavecchia, F. (2015) · Virtual and Physical Prototyping · doi:10.1080/17452759.2015.1101872 · Cited by 16
Adaptively Dense Feature Pyramid Network for Object Detection
Pan, H., Chen, G., & Jiang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2922511 · Cited by 17
Moving Car Recognition and Removal for 3D Urban Modelling Using Oblique Images
Yang, C., Zhang, F., Gao, Y., et al. (2021) · Remote Sensing · doi:10.3390/rs13173458 · Cited by 16
Online self-supervised segmentation of dynamic objects
Guizilini, V., & Ramos, F. T. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631249 · Cited by 16
SSD-TSEFFM: New SSD Using Trident Feature and Squeeze and Extraction Feature Fusion
Hwang, Y.-J., Lee, J.-G., Moon, U.-C., & Park, H. (2020) · Sensors · doi:10.3390/s20133630 · Cited by 16
A systematic literature review of computer vision applications in robotized wire harness assembly
Wang, H., Salunkhe, O., Quadrini, W., Lämkull, D., Ore, F., Despeisse, M., Fumagalli, L., Stahre, J., & Johansson, B. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102596 · Cited by 29
Object-Based Visual Camera Pose Estimation From Ellipsoidal Model and 3D-Aware Ellipse Prediction
Zins, M., Simon, G., & Berger, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01585-w · Cited by 16
Stable volumetric features in deformable shapes
Litman, R., Bronstein, A. M., & Bronstein, M. M. (2012) · Computers & Graphics · doi:10.1016/j.cag.2012.03.034 · Cited by 16
Directional Space-Time Oriented Gradients for 3D Visual Pattern Analysis
Norouznezhad, E., Harandi, M. T., Bigdeli, A., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33712-3_53 · Cited by 16
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