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 6,301–6,400 of 56,204
Localization and Mapping for Autonomous Navigation in Outdoor Terrains : A Stereo Vision Approach
Agrawal, M., Konolige, K., & Bolles, R. (2007) · Proceedings · doi:10.1109/wacv.2007.40 · Cited by 52
Efficient Airport Detection Using Line Segment Detector and Fisher Vector Representation
Budak, Ü., Halıcı, U., Şengür, A., Karabatak, M., & Xiao, Y. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2565706 · Cited by 53
Field phenotyping of grapevine growth using dense stereo reconstruction
Klodt, M., Herzog, K., Töpfer, R., et al. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0560-x · Cited by 52
Wang, G., Chen, H., Chen, L., Zhuang, Y., Zhang, S., Zhang, T., Dong, H., & Gao, P. (2023) · Remote Sensing · doi:10.3390/rs15071773 · Cited by 55
Feature-Guided SAR-to-Optical Image Translation
Zhang, J., Zhou, J., & Lu, X. (2020) · IEEE Access · doi:10.1109/access.2020.2987105 · Cited by 53
Fisheye-Based Method for GPS Localization Improvement in Unknown Semi-Obstructed Areas
Moreau, J., Ambellouis, S., & Ruichek, Y. (2017) · Sensors · doi:10.3390/s17010119 · Cited by 51
Discriminative Bayesian Dictionary Learning for Classification
Akhtar, N., Faisal Shafait, B., & Mian, A. S. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2527652 · Cited by 52
Monocular vision based 3D vibration displacement measurement for civil engineering structures
Shao, Y., Li, L., Li, J., Li, Q., An, S., & Hao, H. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.116661 · Cited by 58
Pedestrian Counting with Occlusion Handling Using Stereo Thermal Cameras
Kristoffersen, M. S., Dueholm, J., Gade, R., & Moeslund, T. (2016) · Italian National Conference on Sensors · doi:10.3390/s16010062 · Cited by 50
Robust image matching via local graph structure consensus
Jiang, X., Xia, Y., Zhang, X.-P. S., & Ma, J. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108588 · Cited by 55
Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning
Xue, N., Wu, T., Bai, S., Wang, F., Xia, G.-S., Zhang, L., & Torr, P. H. S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3312749 · Cited by 64
Automatic SAR Image Registration via Tsallis Entropy and Iterative Search Process
Kang, M., & Kim, K. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2981398 · Cited by 54
MagicHand: Interact with IoT Devices in Augmented Reality Environment
Sun, Y., Armengol-Urpi, A., Kantareddy, S. N. R., Siegel, J. E., & Sarma, S. E. (2019) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr.2019.8798053 · Cited by 54
Orientation-selective building detection in aerial images
Manno-Kovács, A., & Szirányi, T. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.06.007 · Cited by 52
Haines, T. S. F., Aodha, O. M., & Brostow, G. (2016) · ACM Transactions on Graphics · doi:10.1145/2886099 · Cited by 55
On Model-Based Analysis of Ear Biometrics
Arbab‐Zavar, B., Nixon, M. S., & Hurley, D. J. (2007) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2007.4401937 · Cited by 52
Enabling warping on stereoscopic images
Niu, Y., Feng, W., & Liu, F. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366202 · Cited by 52
Multi‐scale attention encoder for street‐to‐aerial image geo‐localization
Li, S., Tu, Z., Chen, Y., & Yu, T. (2022) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12077 · Cited by 53
Scalable face image retrieval with identity-based quantization and multi-reference re-ranking
Wu, Z., Ke, Q., Sun, J. Z., & Shum, H. (2010) · 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.2010.5539976 · Cited by 52
A Survey on Human Action Recognition Using Depth Sensors
Liang, B., & Zheng, L. (2015) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2015.7371223 · Cited by 54
Anytime Recognition of Objects and Scenes
Karayev, S., Fritz, M., & Darrell, T. (2014) · 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.2014.80 · Cited by 50
Endoscopic Image Classification and Retrieval using Clustered Convolutional Features
Ahmad, J., Muhammad, K., Lee, M. Y., et al. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0836-y · Cited by 52
Learning weights for codebook in image classification and retrieval
Cai, H., Yan, F., & Mikolajczyk, K. (2010) · 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.2010.5539918 · Cited by 52
Barnesi, N., Loy, G., Shaw, D., et al. (2005) · doi:10.1109/iccv.2005.207 · Cited by 52
Visual terrain classification for selecting energy efficient gaits of a hexapod robot
Zenker, S., Aksoy, E. E., Goldschmidt, D., Wörgötter, F., & Manoonpong, P. (2013) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim.2013.6584154 · Cited by 52
Recent progresses on object detection: a brief review
Zhang, H., & Hong, X. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07898-2 · Cited by 52
Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification
Yu, D., Guo, H., Xu, Q., Lu, J., Zhao, C., & Lin, Y. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3030257 · Cited by 52
Rotational copy-move forgery detection using SIFT and region growing strategies
Chen, C.-C., Lu, W.-Y., & Chou, C.-H. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7165-8 · Cited by 52
Three-dimensional models of natural environments and the mapping of navigational information
Stürzl, W., Grixa, I., Mair, E., Narendra, A., & Zeil, J. (2015) · Journal of Comparative Physiology A · doi:10.1007/s00359-015-1002-y · Cited by 52
High-Resolution Face Verification Using Pore-Scale Facial Features
Li, D., Zhou, H., & Lam, K. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2412374 · Cited by 52
City scale geo-spatial trajectory estimation of a moving camera
Vaca-Castano, G., Zamir, A., & Shah, M. A. (2012) · 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.2012.6247800 · Cited by 53
Graph-Based Place Recognition in Image Sequences with CNN Features
Zhang, X., Wang, L., Zhao, Y. C., & Su, Y. L. (2018) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-018-0917-2 · Cited by 54
Local Tiled Deep Networks for Recognition of Vehicle Make and Model
Gao, Y., & Lee, H. J. (2016) · Sensors · doi:10.3390/s16020226 · Cited by 54
Scalable Feature Matching by Dual Cascaded Scalar Quantization for Image Retrieval
Zhou, W., Yang, M., Wang, X., Li, H., Lin, Y., & Tian, Q. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2430329 · Cited by 50
Integration of Model-based and Model-free Cues for Visual Object Tracking in 3D
Kyrki, V., & Kragić, D. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570335 · Cited by 52
Reference Face Graph for Face Recognition
Kafai, M., An, L., & Bhanu, B. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2359548 · Cited by 52
A visual compass based on SLAM
Montiel, J. M. M., & Davison, A. J. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2006.1641986 · Cited by 52
Adapting content-based image retrieval techniques for the semantic annotation of medical images
Kumar, A., Dyer, S., Kim, J., Li, C., Leong, P. H. W., FULHAM, M. J., & Feng, D. D. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.01.001 · Cited by 52
A novel image retrieval method based on multi-trend structure descriptor
Zhao, M. Y., Zhang, H., & Sun, J. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.02.016 · Cited by 52
Visual animal biometrics: survey
Kumar, S., & Singh, S. K. (2016) · IET Biometrics · doi:10.1049/iet-bmt.2016.0017 · Cited by 52
Pothole 3D Reconstruction With a Novel Imaging System and Structure From Motion Techniques
Ahmed, A., Ashfaque, M., Haq, M. U. U., Mathavan, S., Kamal, K., & Rahman, M. M. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054026 · Cited by 52
Image based skin disease detection using hybrid neural network coupled bag-of-features
Chakraborty, S., Mali, K., Chatterjee, S., et al. (2017) · doi:10.1109/uemcon.2017.8249038 · Cited by 52
Rafiei, M., Raitoharju, J., & Iosifidis, A. (2023) · IEEE Access · doi:10.1109/access.2023.3234187 · Cited by 57
Interest Points for Hyperspectral Image Data
Mukherjee, A., Vélez-Reyes, M., & Roysam, B. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.2011280 · Cited by 51
Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features
Roy, S. D., Das, S., Kar, D., et al. (2021) · Sensors · doi:10.3390/s21113628 · Cited by 52
Coding Visual Features Extracted From Video Sequences
Baroffio, L., Cesana, M., Redondi, A. E. C., Tagliasacchi, M., & Tubaro, S. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2312617 · Cited by 51
Indexing in large scale image collections: Scaling properties and benchmark
Aly, M., Munich, M. E., & Perona, P. (2011) · Proceedings · doi:10.1109/wacv.2011.5711534 · Cited by 51
User Intent in Multimedia Search
Kofler, C., Larson, M., & Hanjalić, A. (2016) · ACM Computing Surveys · doi:10.1145/2954930 · Cited by 52
Liu, W. C., & Wu, B. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.017 · Cited by 54
YOLOv8-ACU: improved YOLOv8-pose for facial acupoint detection
Yuan, Z., Shao, P., Li, J., Wang, Y., Zhu, Z., Qiu, W., Chen, B., Tang, Y. D., & Han, A. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1355857 · Cited by 51
“Berretti et al. (2011) conducted experiments on the BU-3DFE dataset to extract the local features of the face using the SIFT (Lowe, 2004) model for keypoint detection and used a multi-class SVM (Platt, 1998) for classification, and the experimental results showed that the method has a good…”
MethodAudio and face video emotion recognition in the wild using deep neural networks and small datasets
Ding, W., Xu, M., Huang, D., Lin, W., Dong, M., Yu, X., & Li, H. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997637 · Cited by 51
SVM-based discriminative accumulation scheme for place recognition
Pronobis, A., Mozos, Ó. M., & Caputo, B. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543260 · Cited by 52
A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network
Yan, Y., Yao, X., Wang, S., & Zhang, Y. (2021) · Biology · doi:10.3390/biology10111084 · Cited by 53
Video-Based Outdoor Human Reconstruction
Zhu, H., Liu, Y.-B., Fan, J.-T., Dai, Q.-H., & Cao, X. (2017) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2016.2596118 · Cited by 45
Improvement in the performance of camera based vehicle detector for parking lot
Ichihashi, H., Katada, T., Fujiyoshi, M., Notsu, A., & Honda, K. (2010) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzzy.2010.5584554 · Cited by 52
A Hybrid convolutional neural network for sketch recognition
Zhang, X., Huang, Y., Zou, Q., Pei, Y., Zhang, R., & Wang, S. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.01.006 · Cited by 53
Unsupervised Part-Based Weighting Aggregation of Deep Convolutional Features for Image Retrieval
Xu, J., Shi, C., Qi, C., Wang, C., & Xiao, B. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12231 · Cited by 60
A novel measurement model based on abBRIEF for global localization of a UAV over satellite images
Mantelli, M., Pittol, D., Neuland, R., Ribacki, A., Maffei, R. D. Q., Jorge, V. A. M., Prestes, E., & Kolberg, M. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.12.006 · Cited by 52
3D object understanding with 3D Convolutional Neural Networks
Leng, B., Liu, Y., Yu, K., et al. (2015) · Information Sciences · doi:10.1016/j.ins.2015.08.007 · Cited by 52
Multi-Modal Distance Metric Learning
Xie, P., & Xing, E. P. (2018) · Figshare · doi:10.1184/r1/6475904 · Cited by 50
Model Latent Views With Multi-Center Metric Learning for Vehicle Re-Identification
Jin, Y., Li, C., Li, Y., Peng, P., & Giannopoulos, G. A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3042558 · Cited by 51
Robust Registration Algorithm for Optical and SAR Images Based on Adjacent Self-Similarity Feature
Xiong, X., Jin, G., Xu, Q., et al. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3197357 · Cited by 52
A Minimal Solution to the Generalized Pose-and-Scale Problem
Ventura, J., Arth, C., Reitmayr, G., & Schmalstieg, D. (2014) · 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.2014.61 · Cited by 51
Porikli, F., & Yılmaz, A. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-28598-1_1 · Cited by 53
DeepFood: Automatic Multi-Class Classification of Food Ingredients Using Deep Learning
Pan, L., Pouyanfar, S., Chen, H., Qin, J., & Chen, S. (2017) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2017.00033 · Cited by 53
Preliminary results from active landslide monitoring using multidisciplinary surveys
Nikolakopoulos, K. G., Kavoura, K., Depountis, N., Kyriou, A., Argyropoulos, N., Koukouvelas, I., & Sabatakakis, N. (2017) · European Journal of Remote Sensing · doi:10.1080/22797254.2017.1324741 · Cited by 52
Survey of Spatio-Temporal Interest Point Detection Algorithms in Video
Li, Y., Xia, R., Huang, Q., Xie, W., & Li, X. (2017) · IEEE Access · doi:10.1109/access.2017.2712789 · Cited by 52
Comparative object similarity for improved recognition with few or no examples
Wang, G., Forsyth, D. A., & Hoiem, D. (2010) · 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.2010.5539955 · Cited by 51
Motion recognition using local auto-correlation of space–time gradients
Kobayashi, T., & Otsu, N. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.01.007 · Cited by 51
Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approach
Köse, N., Köpüklü, O., Unnervik, A., & Rigoll, G. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917460 · Cited by 52
Parallel, real-time visual SLAM
Clipp, B., Lim, J., Frahm, J., & Pollefeys, M. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5653696 · Cited by 52
Training a Convolutional Neural Network for Appearance-Invariant Place RecognitionPreprint
Gomez-Ojeda, R., López-Antequera, M., Petkov, N., & González-Jiménez, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.07428 · Cited by 56
A new thermal infrared and visible spectrum images-based pedestrian detection system
Lahmyed, R., Ansari, M. E., & Ellahyani, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6974-5 · Cited by 51
Mei, S., Misiakiewicz, T., & Andrea Montanari, A. (2021) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2021.12.003 · Cited by 83
Søgaard, A., Vulić, I., Ruder, S., & Faruqui, M. (2019) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-02171-8 · Cited by 50
Automatic alignment of paintings and photographs depicting a 3D scene
Russell, B., Šivic, J., Ponce, J., et al. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130291 · Cited by 51
Interannual surface evolution of an Antarctic blue-ice moraine using multi-temporal DEMs
Westoby, M., Dunning, S. A., Woodward, J., et al. (2016) · Earth Surface Dynamics · doi:10.5194/esurf-4-515-2016 · Cited by 51
Accelerated Hypothesis Generation for Multi-structure Robust Fitting
Chin, T.-J., Yu, J., & Suter, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_39 · Cited by 51
OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features
Osokin, A., Sumin, D., & Lomakin, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_38 · Cited by 52
Visually bootstrapped generalized ICP
Pandey, G., Savarese, S., McBride, J. R., et al. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980322 · Cited by 51
Liu, Y., Kiss, A. M., Larsson, D. H., Yang, F., & Pianetta, P. (2016) · Spectrochimica Acta Part B Atomic Spectroscopy · doi:10.1016/j.sab.2016.01.002 · Cited by 51
Minimal Solvers for Relative Pose Estimation of Multi-Camera Systems using Affine Correspondences
Guan, B., Zhao, J. Y., Baráth, D., & Fraundorfer, F. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01690-w · Cited by 52
Fast Line Description for Line-based SLAM
Hirose, K., & Saitô, H. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.83 · Cited by 52
SAR Image Registration Based on Multifeature Detection and Arborescence Network Matching
Zhu, H., Ma, W., Hou, B., et al. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2539207 · Cited by 51
Towards a more effective method for analyzing mobile eye-tracking data
Brône, G., Oben, B., & Goedemé, T. (2011) · doi:10.1145/2029956.2029971 · Cited by 51
Surpassing Human-Level Face Verification Performance on LFW with GaussianFacePreprint
Lu, C., & Tang, X. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.3840 · Cited by 51
A Deep Step Pattern Representation for Multimodal Retinal Image Registration
Lee, J., Liu, P., Cheng, J., & Fu, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00518 · Cited by 56
Detection and Tracking in Thermal Infrared Imagery
Berg, A. (2016) · Linköping University Electronic Press eBooks · doi:10.3384/lic.diva-126955 · Cited by 51
Visual Loop Closing using Gist Descriptors in Manhattan World
Singh, G. B. (2010) · Cited by 52
Creating Panoramic Images Using ORB Feature Detection and RANSAC-based Image Alignment
Wu, K. (2023) · Advances in Computer and Communication · doi:10.26855/acc.2023.08.002 · Cited by 51
Bilinear models of natural images
Olshausen, B. A., Cadieu, C. F., Culpepper, J., & Warland, D. K. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.715515 · Cited by 50
Zender, H., Jensfelt, P., Mozos, Ó. M., et al. (2007) · Lincoln Repository (University of Lincoln) · Cited by 51
REMAP: Multi-Layer Entropy-Guided Pooling of Dense CNN Features for Image Retrieval
Husain, S. S., & Bober, M. Z. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2917234 · Cited by 51
Matching Pixels Using Co-occurrence Statistics
Kat, R., Jevnisek, R. J., & Avidan, S. (2018) · 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.2018.00188 · Cited by 50
Self-Similarity Features for Multimodal Remote Sensing Image Matching
Xiong, X., Jin, G., Xu, Q., & Zhang, H. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3131489 · Cited by 52
Robust zero-watermarking algorithm for medical images based on SIFT and Bandelet-DCT
Fang, Y., Liu, J., Li, J., et al. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12592-x · Cited by 51
A survey of optical flow techniques for UAV navigation applications
Chao, H., Gu, Y., & Napolitano, M. (2013) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2013.6564752 · Cited by 58
An improvement to the SIFT descriptor for image representation and matching
Liao, K., Liu, G., & Hui, Y. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.03.021 · Cited by 52
What is the spatial extent of an object?
Uijlings, J., Smeulders, A. W. M., & Scha, R. J. H. (2009) · 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.2009.5206663 · Cited by 52
Pepe, M., & Costantino, D. D. (2020) · Periodica Polytechnica Civil Engineering · doi:10.3311/ppci.16398 · Cited by 51
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
