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 29,801–29,900 of 56,201
On the feature detection of nonconforming objects with automated drone surveillance
Xu, B., Xu, X., & Own, C. (2017) · International Conference on Communication and Information Processing (ICCIP) · doi:10.1145/3162957.3163039 · Cited by 5
Retinal image registration feature descriptors-a survey
Christy, B. M. D., & Moses, C. (2014) · International Conference on Electronics, Circuits, and Systems · doi:10.1109/ecs.2014.6892684 · Cited by 3
Robust Line Feature Matching via Point–Line Invariants and Geometric Constraints
Zhang, C., Xiang, Y., Wang, Q., et al. (2025) · Sensors · doi:10.3390/s25102980 · Cited by 5
“Most statistical-based line descriptors were partially inspired by the descriptor present in [13], in which the authors exploited image gradients in local subregions to formulate line feature descriptors according to the model of SIFT feature descriptor [14].”
KID: Kirsch Directional Features Based Image Descriptor
Shekar, B. H., Holla, K. R., & Kumari, M. S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-45062-4_44 · Cited by 5
No-reference image quality assessment based on gradient histogram response
Sun, T., Ding, S., Chen, W., et al. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2015.11.007 · Cited by 5
Onmek, Y., Triboulet, J. P., Druon, S., et al. (2017) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar.2017.7942725 · Cited by 5
A prediction-based approach for features aggregation in Visual Sensor Networks
Redondi, A. E. C., Cesana, M., Fratta, L., et al. (2018) · Ad Hoc Networks · doi:10.1016/j.adhoc.2018.05.010 · Cited by 5
Object Pose Estimation Using Color Images and Predicted Depth Maps
Hoang, D.-C., Tan, P. X., Nguyen, A.-N., Vu, D.-Q., Vu, V.-D., Nguyen, T.-U., Duong, Q.-T., Nguyen, V.-T., Hoang, N.-A., Phan, K.-T., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3397715 · Cited by 5
Cartoon scene matching system using color and CSIFT feature
Deng, L., Chen, D., Yuan, Z., et al. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5647745 · Cited by 5
Reinoso-Gordo, J. F., Romero‐Zaliz, R., León-Robles, C. A., et al. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9080482 · Cited by 5
Registration of Color and OCT Fundus Images Using Low-dimensional Step Pattern Analysis
Lee, J. A., Cheng, J. T., Xu, G., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_26 · Cited by 5
Abnormal behavior detection for harbour operator safety under complex video surveillance scenes
Cheng, G., Wang, S., Guo, T., et al. (2017) · International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · doi:10.1109/spac.2017.8304298 · Cited by 5
A Mean Features Method for Face Photo-Sketch Synthesis and Recognition
Abel-Aziz, H. G. M., Ebied, H. M., & Mostafa, M. G. M. (2016) · International Conference on Informatics and Systems · doi:10.1145/2908446.2908492 · Cited by 5
Image match using wavelet — Colour SIFT features
Kumar, N. A. M., & Sathidevi, P. S. (2012) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciinfs.2012.6304762 · Cited by 5
Diffusion of DNA on Atomically Flat 2D Material Surfaces
(2023) · doi:10.1101/2023.11.01.565159 · Cited by 5
Nonlinear and Dotted Defect Detection with CNN for Multi-Vision-Based Mask Inspection
Woo, J., & Lee, H. (2022) · Sensors · doi:10.3390/s22228945 · Cited by 5
Granillo, A. O., Zamora, D., Schnittker, R. R., Scott, A. R., Spluga, A., Russell, J., Brewster, C. E., Ross, E. J., Acheampong, D. A., Zhang, N., et al. (2024) · iScience · doi:10.1016/j.isci.2024.110737 · Cited by 5
EF-Razor: An Effective Edge-Feature Processing Method in Visual SLAM
Liu, W., Mo, Y., Jiao, J., & Deng, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3013806 · Cited by 5
Adaptive Contour Feature and Color Feature Fusion for Monocular Textureless 3D Object Tracking
Li, C., & Gao, X. (2018) · IEEE Access · doi:10.1109/access.2018.2839761 · Cited by 5
Papapetros, I. T., Kansizoglou, I., Bampis, L., & Gasteratos, A. C. (2023) · Machines · doi:10.3390/machines11050558 · Cited by 5
YOLO-UAVShip: An Effective Method and Dateset for Multi-View Ship Detection in UAV Images
Li, Y., Tian, Y., Yuan, C., Yu, K., Yin, K., Huang, H., Yang, G., Li, F., & Zhou, Z.-G. (2025) · Remote Sensing · doi:10.3390/rs17173119 · Cited by 8
A scalable framework for markerless camera-based smartphone interaction with large public displays
Baldauf, M., Fröhlich, P., & Lasinger, K. (2012) · International Symposium on Pervasive Displays · doi:10.1145/2307798.2307802 · Cited by 5
Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric AuthenticationPreprint
Singh, A. K., Joshi, P., & Nandi, G. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.05296 · Cited by 5
Static Video Summarization through Optimum-Path Forest Clustering
Martins, G. B., Afonso, L. C. S., Osaku, D., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12568-8_108 · Cited by 5
Papliński, A. P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39065-4_63 · Cited by 5
An Optimized Method Based on RANSAC for Fundamental Matrix Estimation
Wu, W., & Liu, W. (2018) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2018.8600471 · Cited by 5
A Finite Gamma Mixture Model-Based Discriminative Learning Frameworks
Al-Osaim, F. R., & Bouguila, N. (2015) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2015.77 · Cited by 5
Multiple-kernel based vehicle tracking using 3-D deformable model and license plate self-similarity
Lee, K.-H., Lee, Y., & Hwang, J. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6637961 · Cited by 5
An approach for signature recognition using contours based technique
Aravinda, C. V., Meng, L., & R., U. K. R. K. (2019) · International Conference on Advanced Mechatronic Systems · doi:10.1109/icamechs.2019.8861516 · Cited by 5
Adaptive region assisted GAN for image steganography
Cai, J., Xiao, F., Zhang, K., et al. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01785-7 · Cited by 5
“Fang et al. [12] proposed an intensity-based Scale-Invariant Feature Transform (SIFT) [15] to identify embedding regions, enabling the concealment of secrets across multiple areas.”
Instataneous geo-location of multiple targets from monocular airborne video
Han, K. M., & DeSouza, G. N. (2009) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2009.5417549 · Cited by 5
Bag-of-visual-words model for artificial pornographic images recognition
Li, F., Luo, S., Liu, X., et al. (2016) · Journal of Central South University · doi:10.1007/s11771-016-3190-1 · Cited by 5
FINGER KNUCKLE PRINT RECOGNITION WITH SIFT AND K-MEANS ALGORITHM
Muthukumar, A., & Kannan, S. (2013) · ICTACT Journal on Image and Video Processing · doi:10.21917/ijivp.2013.0083 · Cited by 5
Line Segment-based Aerial Image Registration
Kwon, Y. P. (2014) · UC Berkeley · Cited by 5
How to Measure Memorability and Social Interestingness of Images: A Review
Amengual, X., Bosch, A., & de la Rosa, J. L. (2016) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001417540040 · Cited by 5
Bi-heterogeneous Convolutional Neural Network for UAV-based dynamic scene classification
Zheng, J., Cao, X., Zhang, B., et al. (2017) · Integrated Communications, Navigation and Surveillance Conference (ICNS) · doi:10.1109/icnsurv.2017.8011932 · Cited by 5
GLOCAL: A self-supervised learning framework for global and local motion estimation
Zheng, Y., Luo, K., Liu, S., Li, Z., Xiang, Y., Wu, L., Zeng, B., & Chen, C. W. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.12.024 · Cited by 5
Comparative analysis of color- and grayscale-based feature descriptions for image recognition
Petrushan, M. V., Vermenko, Y., Shaposhnikov, D. G., et al. (2013) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661813030115 · Cited by 5
Video stabilisation using local salient feature in particle filter framework
Chen, G., Ma, Z., Shan, Y., et al. (2013) · International Journal of Wireless and Mobile Computing · doi:10.1504/ijwmc.2013.053034 · Cited by 5
Pan, B., Xiang, J., Zhang, N., et al. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-03823-3 · Cited by 5
Reversible anonymization for privacy of facial biometrics via cyclic learning
Xu, S., Chang, C., Nguyen, H. H., et al. (2024) · EURASIP Journal on Information Security · doi:10.1186/s13635-024-00174-3 · Cited by 5
“To evaluate fidelity, we utilize two commonly used metrics: peak signal-to-noise ratio (PSNR) [37] and structural similarity index (SSIM) [38].”
MethodEffective Descriptors based Face Recognition Technique for Robotic Surveillance Systems
Vinay, A., Deshpande, A. R., Pranathi, B. S., et al. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.07.080 · Cited by 5
Building recognition with adaptive interest point selection
Hascoët, N., & Zaharia, T. B. (2017) · International Conference on Consumer Electronics · doi:10.1109/icce.2017.7889218 · Cited by 5
Angle-based homing from a reference image set using the 1D trifocal tensor
Aranda, M., Nicolás, G. L., & Sagüés, C. (2013) · Autonomous Robots · doi:10.1007/s10514-012-9313-0 · Cited by 5
Fingerspelling Recognition in the Wild with Fixed-Query based Visual Attention
S, S. K. S., Jose, G., Tandon, N., et al. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475580 · Cited by 5
Cognitive UAV Tracking: Leveraging DRL and Hybrid Curriculum Learning for Target Reacquisition
Wang, J., Zeng, B., Deng, L., et al. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3577984 · Cited by 5
Vehicle Tracking in Occlusion and Clutter
Mcbride, K. N. (2008) · UWSpace (University of Waterloo) · Cited by 5
A Robust Approach to Vision-Based Terrain-Aided Localization
Navon, D., Rivlin, E., & Rotstein, H. P. (2025) · NAVIGATION Journal of the Institute of Navigation · doi:10.33012/navi.683 · Cited by 5
Life Cycle of a SLAM System: Implementation, Evaluation and Port to the Project Tango Device
Araujo, T., Roberto, R. A., Teixeira, J. M., et al. (2016) · doi:10.1109/svr.2016.13 · Cited by 5
Neural Video Compression Based on SURF Scene Change Detection Algorithm
Grycuk, R., & Knop, M. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-23814-2_13 · Cited by 5
Implementation and application of orientation correction on SIFT and SURF matching
Goh, K. M., Abu-Bakar, A. R., Mokji, M. M., et al. (2013) · Cited by 5
Multitask Fine-Grained Feature Mining for Multilabel Remote Sensing Image Classification
Guo, J., Sun, H., Han, J., Song, B., Chi, Y., & Song, B. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3426473 · Cited by 6
Efficient Video Mosaicking by Multiple Loop Closing
Meidow, J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24393-6_1 · Cited by 5
An accurate retrieval through R-MAC+ descriptors for landmark recognitionPreprint
Magliani, F., & Prati, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08565 · Cited by 5
Prochukhan, N., Rafferty, A., Canavan, M., Daly, D., Selkirk, A., Saranya, R., & Morris, M. A. (2024) · Microscopy Research and Technique · doi:10.1002/jemt.24514 · Cited by 6
Vision-Based Control for an AUV in a Multi-robot Undersea Intervention Task
Garcia‐Fidalgo, E., Ortiz, A., & Massot‐Campos, M. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-70833-1_4 · Cited by 5
Liu, X., Gaoqiang, H., Ma, X., et al. (2018) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2018.8407835 · Cited by 5
Mösch, L., Pokee, D. Q., Barz, I., Müller, A., Follmann, A., Moormann, D., Czaplik, M., & Pereira, C. B. (2024) · Drones · doi:10.3390/drones8100589 · Cited by 6
Service robots navigation using pictographs detection for indoor environment
Hirose, K., Chugo, D., Yokota, S., et al. (2011) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2011.6119644 · Cited by 5
Machine Learning for Image Based Motion Capture
Agarwal, A., Tel, H. I., De, D., Grenoble, P. D., Roger, M., Andrew, M., Pascal, M., William, M., Directeur, T., Philip, M., et al. (2006) · Cited by 5
Selecting User Generated Content for Use in Media Productions
Bailer, W., Weiss, W., & Wechtitsch, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27674-8_38 · Cited by 5
Scale Invariant Metrics of Volumetric Datasets
Raviv, D., & Raskar, R. (2015) · SIAM Journal on Imaging Sciences · doi:10.1137/140987675 · Cited by 5
Vipparla, C., Krock, T. J., Nouduri, K., Fraser, J., Aliakbarpour, H., Sagan, V., Cheng, J.-R. C., & Palaniappan, K. (2024) · Sensors · doi:10.3390/s24248217 · Cited by 5
Lu, X., Yan, Y., Wei, T., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00343 · Cited by 5
Fusion of Deep Convolutional Neural Networks
Suchy, R., Ezekiel, S., & Cornacchia, M. (2017) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2017.8457945 · Cited by 5
Multi-agent and driving behavior based rear-end collision alarm modeling and simulating
Liang, J. S., Chen, L., Cheng, X., et al. (2010) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2010.02.006 · Cited by 5
Yang, Z., Li, J., Wu, H., et al. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2416468 · Cited by 5
“The latter method uses SfM to reconstruct overlapping 2D images from the same scene into a 3D map, and then establish correspondences between 2D pixels in the query image and 3D points in the model (Brejcha and Čadík 2017; Lowe 2004; Sarlin et al. 2019).”
An Open-Source UAV Image Processing Web Service for Crop Health Monitoring
Neupane, B., Horanont, T., Duy, H. N., Suebvong, S., & Mahattanawutakorn, T. (2019) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai.2019.00014 · Cited by 5
Multi-Image Semantic Matching by Mining Consistent FeaturesPreprint
Wang, Q., Zhou, X., & Daniilidis, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07641 · Cited by 5
Incremental learning approach for human detection and tracking
Ammar, B., Wali, A., & Alimi, A. M. (2011) · International Conference on Innovations in Information Technology · doi:10.1109/innovations.2011.5893802 · Cited by 5
MULTIDIRECTIONAL BUILDING DETECTION IN AERIAL IMAGES WITHOUT SHAPE TEMPLATES
Manno-Kovács, A., & Szirányi, T. (2013) · 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/isprsarchives-xl-1-w1-227-2013 · Cited by 5
Overview of approaches for accelerating scale invariant feature detection algorithm
Zhang, J., Sang, H., & Xu-bang, S. (2011) · International Conference on Electric Information and Control Engineering · doi:10.1109/iceice.2011.5778312 · Cited by 5
AUTOMATIC ORIENTATION AND MOSAICKING OF ARCHIVED AERIAL PHOTOGRAPHY USING STRUCTURE FROM MOTION
Gonçalves, J. A. (2016) · 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/isprsarchives-xl-3-w4-123-2016 · Cited by 5
Shi, C., Yang, J., Liu, L., et al. (2014) · Science China Information Sciences · doi:10.1007/s11432-014-5174-9 · Cited by 5
Menon, H. P. (2017) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8125938 · Cited by 5
Exploring the DolphinSLAM's parameters
Zaffari, G. B., dos Santos, M. M., Duarte, A. C., et al. (2016) · OCEANS 2016 - Shanghai · doi:10.1109/oceansap.2016.7485531 · Cited by 5
SIFT Feature-Based Relative Altitude Estimation Enhanced With Siamese Network
Nasr-Esfahani, S., & Jagannathan, S. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3523317 · Cited by 5
Earth Mover's Distance vs. Quadratic form Distance: An Analytical and Empirical Comparison
Beecks, C., Uysal, M. S., & Seidl, T. (2015) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2015.76 · Cited by 5
Vision-based autonomous navigation based on motion estimation
Kim, J., & Kweon, I. S. (2008) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2008.4694509 · Cited by 5
Dimension Reduction for Objects Composed of Vector Sets
Szemenyei, M., & Vajda, F. (2017) · International Journal of Applied Mathematics and Computer Science · doi:10.1515/amcs-2017-0012 · Cited by 5
Urban Visual Localization of Block-Wise Monocular Images with Google Street Views
Li, Z., Li, S., Anderson, J., et al. (2024) · Remote Sensing · doi:10.3390/rs16050801 · Cited by 5
k-SpecNET: Localization and classification of indoor superimposed sound for acoustic sensor networks
Wang, W., Seraj, F., & Havinga, P. (2020) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops48775.2020.9156174 · Cited by 5
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
Aldea, E., & Kiyani, K. H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16634-6_50 · Cited by 5
3D Reconstruction Data Set -The Langweil Model of Prague
Sedlacek, D., Buriánek, J. D., & Žára, J. (2013) · International Journal of Heritage in the Digital Era · doi:10.1260/2047-4970.2.2.195 · Cited by 5
Deep Convolutional Neural Network for 6-DOF Image LocalizationPreprint
Jia, D., Su, Y., & Li, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02776 · Cited by 5
6-DOF motion estimation using optical flow based on dual cameras
Liu, M.-Y., Wang, Y., & Guo, L. (2017) · Journal of Central South University · doi:10.1007/s11771-017-3448-2 · Cited by 5
A method for Content-Based Image Retrieval using visual attention model
Mohammadpour, M., & Mozaffari, S. (2015) · Conference on Information and Knowledge Technology · doi:10.1109/ikt.2015.7288764 · Cited by 5
BINK: Biological binary keypoint descriptor
Saleiro, M., Terzić, K., Rodrigues, J. M. F., & du Buf, J. M. H. (2017) · Biosystems · doi:10.1016/j.biosystems.2017.10.007 · Cited by 5
Feature tracking for sea ice drift retrieval from SAR images
Demchev, D., Волков, В. А., Казаков, Э. Э., et al. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8126963 · Cited by 5
UAV Small Target Detection Model Based on Dual Branches and Adaptive Feature Fusion
Wang, G., Gao, M., & Liu, Y. (2025) · Sensors · doi:10.3390/s25154542 · Cited by 5
“Before the development of deep learning, target detection mainly relied on traditional methods such as Histogram of Oriented Gradient (HOG) [1], Scale Invariant Feature Transform (SIFT) [2], and Local Binary Pattern (LBP) [3].”
Merten, A.-L., Schöler, U., Guo, Y., Linsenmeier, F., Martinac, B., Friedrich, O., & Schürmann, S. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05159-6 · Cited by 5
“If an xy-drift appeared during different phases of the time-lapse recording, it was corrected for by using the ImageJ plugin “Linear Stack Alignment with SIFT” [29].”
MethodLocal sharpness distribution–based feature points matching algorithm
Zhao, Y., & Su, J. (2014) · Journal of Electronic Imaging · doi:10.1117/1.jei.23.1.013011 · Cited by 5
A study of multi-sensor satellite image indexing
Dumitru, C. O., Cui, S., & Datcu, M. P. (2015) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse.2015.7120458 · Cited by 5
Latte, N., Gaucher, P., Bolyn, C., et al. (2020) · Remote Sensing · doi:10.3390/rs12081265 · Cited by 5
Dimensionality reduction for histogram features: A distance-adaptive approach
Cui, M., Cui, J., & Li, H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.123 · Cited by 5
3D Rotation-Invariant Description from Tensor Operation on Spherical HOG Field
Liu, K., Skibbe, H., Schmidt, T., et al. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.33 · Cited by 5
Markerless reconstruction of dynamic facial expressions
Sibbing, D., Habbecke, M., & Kobbelt, L. P. (2009) · doi:10.1109/iccvw.2009.5457498 · Cited by 5
Bhattacharya, A. (2021) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.090307 · Cited by 5
Probabilistic framework for multi-target tracking using multi-camera: applied to fall detection
Rudakova, V. (2010) · NORA - Norwegian Open Research Archives · Cited by 5
Adaptive Euclidean maps for histograms: generalized Aitchison embeddings
Le, T., & Cuturi, M. (2014) · Machine Learning · doi:10.1007/s10994-014-5446-z · Cited by 5
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
