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 2,030
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 7,001–7,100 of 56,203
Keyframe extraction in endoscopic video
Schoeffmann, K., Fabro, M. D., Szkaliczki, T., Böszörményi, L., & Keckstein, J. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2224-7 · Cited by 47
Robust visual-based localization and mapping for underwater vehicles: A survey
Ding, S.-S., Zhang, T.-D., Lei, M., Chai, H., & Jia, F. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.119274 · Cited by 44
LCBM: A Multi-View Probabilistic Model for Multi-Label Classification
Sun, S., & Zong, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2974203 · Cited by 47
Robust Spatial Consistency Graph Model for Partial Duplicate Image Retrieval
Chu, L., Jiang, S., Wang, S., et al. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2270455 · Cited by 47
Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer
Lu, J., Richter, F., & Yip, M. C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3151981 · Cited by 47
Hierarchical Space-Time Model Enabling Efficient Search for Human Actions
Ning, H., Han, T. X., Walther, D. B., Liu, M., & Huang, T. S. (2009) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2009.2017399 · Cited by 46
Energy-Latency Computation Offloading and Approximate Computing in Mobile-Edge Computing Networks
Younis, A., Maheshwari, S., & Pompili, D. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3360850 · Cited by 48
Image Classification to Support Emergency Situation Awareness
Lagerstrom, R., Arzhaeva, Y., Szul, P., Obst, O., Power, R. A., Robinson, B., & Bednarz, T. P. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2016.00054 · Cited by 46
de Souza, L. A., Jr., Júnior, L. A. P., Mendel, R., Ebigbo, A., Probst, A., Messmann, H., Palm, C., & Papa, J. P. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104029 · Cited by 47
Fey, C., Rutzinger, M., Wichmann, V., et al. (2015) · GIScience & Remote Sensing · doi:10.1080/15481603.2015.1045278 · Cited by 47
Automatic landmark point detection and tracking for human facial expressions
Tie, Y., & Guan, L. (2013) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2013-8 · Cited by 47
Efficient Stixel-based object recognition
Enzweiler, M., Hummel, M., Pfeiffer, D., & Franke, U. (2012) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2012.6232137 · Cited by 47
SketchHelper: Real-Time Stroke Guidance for Freehand Sketch Retrieval
Choi, J., Cho, H., Song, J., et al. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2892301 · Cited by 47
Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning
Bhowmick, B., Patra, S., Chatterjee, A., Govindu, V. M., & Banerjee, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16808-1_19 · Cited by 48
Entropic image thresholding based on GLGM histogram
Xiao, Y., Cao, Z., & Yuan, J. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.12.017 · Cited by 47
Airport detection in remote sensing images: a method based on saliency map
Wang, X., Lv, Q., Wang, B., & Zhang, L. (2012) · Cognitive Neurodynamics · doi:10.1007/s11571-012-9223-z · Cited by 48
Building Collapse Assessment in Urban Areas Using Texture Information From Postevent SAR Data
Sun, W., Shi, L., Yang, J., & Li, P. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2580610 · Cited by 44
An improved Bag-of-Words framework for remote sensing image retrieval in large-scale image databases
Yang, J., Liu, J., & Dai, Q. (2014) · International Journal of Digital Earth · doi:10.1080/17538947.2014.882420 · Cited by 45
Supervised Earth Mover’s Distance Learning and Its Computer Vision Applications
Wang, F., & Guibas, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_32 · Cited by 47
Boschmann, A., Neuhaus, D., Vogt, S., et al. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00822-6 · Cited by 47
Road traffic density estimation using microscopic and macroscopic parameters
Ouessai, A., Keche, M., & Ouamri, A. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2013.09.006 · Cited by 47
Scalable Similarity Search With Topology Preserving Hashing
Zhang, L., Zhang, Y., Gu, X., Tang, J., & Tian, Q. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2326010 · Cited by 47
3D visual phrases for landmark recognition
Hao, Q., Cai, R., Li, Z., Zhang, L., Pang, Y., & Wu, F. (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.2012.6248104 · Cited by 44
Stereo Matching with the Distinctive Similarity Measure
Yoon, K., & Kweon, I. S. (2007) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409002 · Cited by 47
A patch analysis method to detect seam carved images
Wei, J.-D., Lin, Y., & Wu, Y.-J. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.09.026 · Cited by 47
Quality-Specific Hand Vein Recognition System
Wang, J., & Wang, G. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2713340 · Cited by 47
Discriminative affine sparse codes for image classification
Kulkarni, N., & Li, B. (2011) · doi:10.1109/cvpr.2011.5995701 · Cited by 48
Skinner, K. A., Zhang, J., Olson, E. A., et al. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794272 · Cited by 47
Houwen, B. B. S. L., Hazewinkel, Y., Giotis, I., Vleugels, J. L. A., Mostafavi, N. S., van Putten, P., Fockens, P., Dekker, E., & POLAR Study Group (2023) · Endoscopy · doi:10.1055/a-2009-3990 · Cited by 48
Cohen, A. R., Bjornsson, C. S., Temple, S., Banker, G. A., & Roysam, B. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.162 · Cited by 44
Particle Filter Based Object Tracking with Sift and Color Feature
Fazli, S., Pour, H. M., & Bouzari, H. (2009) · International Conference on Machine Vision · doi:10.1109/icmv.2009.47 · Cited by 48
Ozturk, O., Yamasaki, T., & Aizawa, K. (2009) · doi:10.1109/iccvw.2009.5457590 · Cited by 47
Learning Deep Binary Descriptor with Multi-Quantization
Duan, Y., Lu, J., Wang, Z., Feng, J., & Zhou, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858760 · Cited by 47
Local Polar DCT Features for Image Description
Song, T., & Li, H. (2012) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2229273 · Cited by 46
Urban, S., & Martin Weinmann, A. (2015) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-3-w5-121-2015 · Cited by 43
Aguzzi, J., Costa, C., Robert, K., Matabos, M., Antonucci, F., Juniper, S. K., & Menesatti, P. (2011) · Sensors · doi:10.3390/s111110534 · Cited by 47
Mobile robot monocular vision navigation based on road region and boundary estimation
Chang, C.-K., Siagian, C., & Itti, L. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6385703 · Cited by 46
A multi-resolution area-based technique for automatic multi-modal image registration
Bunting, P., Labrosse, F., & Lucas, R. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.12.005 · Cited by 46
Wearables, smartphones, and artificial intelligence for digital phenotyping and health
Perez-Pozuelo, I., Spathis, D., Clifton, E. A. D., & Mascolo, C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820077-3.00003-1 · Cited by 53
Beyond Bits: Reconstructing Images from Local Binary Descriptors
d'Angelo, E., Alahi, A., & Vandergheynst, P. (2012) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 40
Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network
Amrani, A., Sohel, F. A., Diepeveen, D., Murray, D., & Jones, M. G. K. (2022) · Australian Journal of Agricultural Research · doi:10.1071/cp21710 · Cited by 50
Filtering of Brand-Related Microblogs Using Social-Smooth Multiview Embedding
Gao, Y., Zhen, Y., Li, H., & Chua, T. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2581483 · Cited by 46
Visual Model Feature Tracking For UAV Control
Mondragon, I. F., Campoy, P., Correa, J. F., & Mejías, L. (2007) · IEEE International Symposium on Intelligent Signal Processing, · doi:10.1109/wisp.2007.4447629 · Cited by 46
Computer vision–based automatic rod-insulator defect detection in high-speed railway catenary system
Han, Y., Liu, Z., Lee, D.-J., et al. (2018) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881418773943 · Cited by 46
A Novel Depth and Color Feature Fusion Framework for 6D Object Pose Estimation
Zhou, G., Yan, Y., Wang, D., & Chen, Q. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3001533 · Cited by 46
Familiarity based unified visual attention model for fast and robust object recognition
Lee, S., Kim, K., Kim, J.-Y., et al. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.07.014 · Cited by 46
3D Trajectory Reconstruction under Perspective Projection
Park, H. S., Shiratori, T., Matthews, I. A., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0804-2 · Cited by 46
3D hand tracking for human computer interaction
Prisacariu, V. A., & Reid, I. D. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.01.003 · Cited by 46
Task-Driven Feature Pooling for Image Classification
Xie, G.-S., Zhang, X.-Y., Shu, X., Yan, S., & Liu, C. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.140 · Cited by 46
Classification of UAV point clouds by random forest machine learning algorithm
Zeybek, M. (2020) · Turkish Journal of Engineering · doi:10.31127/tuje.669566 · Cited by 50
Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor
Lee, D., & Myung, H. (2014) · Sensors · doi:10.3390/s140712467 · Cited by 46
Image Retrieval based on Bag-of-Words modelPreprint
Liu, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.5168 · Cited by 47
Theoretical Foundations of Gaussian Convolution by Extended Box Filtering
Gwosdek, P., Grewenig, S., Bruhn, A., & Weickert, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-24785-9_38 · Cited by 48
A Vegetable Category Recognition System Using Deep Neural Network
Sakai, Y., Oda, T., Ikeda, M., & Barolli, L. (2016) · International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) · doi:10.1109/imis.2016.84 · Cited by 48
Object detection with sliding window in images including multiple similar objects
Lee, J., Bang, J., & Yang, S.-I. (2017) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2017.8190786 · Cited by 52
Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
Fidalgo, E., Alegre, E., Fernández-Robles, L., et al. (2019) · Digital Investigation · doi:10.1016/j.diin.2019.05.004 · Cited by 46
SEMANTIC SEGMENTATION OF AERIAL IMAGES IN URBAN AREAS WITH CLASS-SPECIFIC HIGHER-ORDER CLIQUES
Montoya‐Zegarra, J. A., Wegner, J. D., Ladický, Ľ., et al. (2015) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-3-w4-127-2015 · Cited by 46
Full-field X-ray reflection microscopy of epitaxial thin-films
Laanait, N., Zhang, Z., Schlepütz, C. M., et al. (2014) · Journal of Synchrotron Radiation · doi:10.1107/s1600577514016555 · Cited by 46
Selective visual odometry for accurate AUV localization
Bellavia, F., Fanfani, M., & Colombo, C. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9541-1 · Cited by 44
Finding region of interest in the infrared image of electrical installation
Jadin, M. S., Taib, S., & Ghazali, K. H. (2015) · Infrared Physics & Technology · doi:10.1016/j.infrared.2015.05.010 · Cited by 46
Automatic Signature Stability Analysis and Verification Using Local Features
Malik, M. I., Liwicki, M., Dengel, A. R., et al. (2014) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2014.109 · Cited by 46
Multiscale gigapixel video: A cross resolution image matching and warping approach
Yuan, X., Fang, L., Dai, Q., Brady, D. J., & Liu, Y. (2017) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccphot.2017.7951481 · Cited by 44
Deep Listwise Triplet Hashing for Fine-Grained Image Retrieval
Liang, Y., Pan, Y., Lai, H., Liu, W., & Yin, J. P. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3137653 · Cited by 47
How Deep Features Have Improved Event Recognition in Multimedia
Ahmad, K., & Conci, N. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3306240 · Cited by 48
Object Class Recognition with Many Local Features
Helmer, S., & Lowe, D. G. (2005) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.409 · Cited by 46
Self-citationObject Matching Using a Locally Affine Invariant and Linear Programming Techniques
Li, H., Huang, X., & He, L. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.99 · Cited by 46
Shi, Q., Gao, Z., Jia, G., Li, C., Huang, Q., Ishii, H., Takanishi, A., & Fukuda, T. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3033705 · Cited by 47
Aerial video surveillance system for small-scale UAV environment monitoring
Avola, D., Foresti, G. L., Martinel, N., et al. (2017) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078523 · Cited by 46
Reflection Symmetry Detection Using Locally Affine Invariant Edge Correspondence
Wang, Z., Tang, Z., & Zhang, X. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2393060 · Cited by 46
X-Ray Scattering Image Classification Using Deep Learning
Wang, B., Yager, K. G., Yu, D., & Hoai, M. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.83 · Cited by 47
Synchronizing Object Detection: Applications, Advancements and Existing Challenges
Hosain, M. T., Zaman, A., Abir, M. R., Akter, S., Mursalin, S., & Khan, S. S. (2024) · IEEE Access · doi:10.1109/access.2024.3388889 · Cited by 53
Development of Fashion Product Retrieval and Recommendations Model Based on Deep Learning
Jo, J., Lee, S., Lee, C., Lee, D., & Lim, H. (2020) · Electronics · doi:10.3390/electronics9030508 · Cited by 53
Hegazi, A., Taha, A., & Selim, M. M. (2019) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2019.07.007 · Cited by 46
Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation
Campbell, D., Petersson, L., Kneip, L., & Li, H. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2848650 · Cited by 48
Ahn, J., Yasukawa, S., Sonoda, T., et al. (2017) · Journal of Marine Science and Technology · doi:10.1007/s00773-017-0442-1 · Cited by 46
Quaternionic Weber Local Descriptor of Color Images
Lan, R., Zhou, Y., & Tang, Y. Y. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2492839 · Cited by 46
Visual control through the trifocal tensor for nonholonomic robots
Nicolás, G. L., Guerrero, J. J., & Sagüés, C. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2009.09.005 · Cited by 46
Okawa, M. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.05.019 · Cited by 45
DeepCar 5.0: Vehicle Make and Model Recognition Under Challenging Conditions
Amirkhani, A., & Barshooi, A. H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3212921 · Cited by 46
Teixeira, L. F., & Côrte-Real, L. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.04.001 · Cited by 46
Detecting tomato leaf diseases by image processing through deep convolutional neural networks
Hossain, M. I., Jahan, S., Asif, M. R. A., et al. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100301 · Cited by 46
Hand-Crafted Features or Machine Learnt Features? Together They Improve RGB-D Object Recognition
Jin, L., Gao, S., Li, Z., & Tang, J. (2014) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2014.56 · Cited by 45
Action recognition based on overcomplete independent components analysis
Zhang, S., Yao, H., Sun, X., Wang, K., Zhang, J., Lu, X., & Zhang, Y. (2014) · Information Sciences · doi:10.1016/j.ins.2013.12.052 · Cited by 45
Compression of image patches for local feature extraction
Makar, M. A., Chang, C.-L., Chen, D. M., Tsai, S. S., & Girod, B. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4959710 · Cited by 44
Distinctive image features from illumination and scale invariant keypoints
Tang, G., Liu, Z., & Xiong, J. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7566-8 · Cited by 45
Small Defect Detection Using Convolutional Neural Network Features and Random Forests
Dong, X., Taylor, C., & Cootes, T. F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11018-5_35 · Cited by 49
Liang, G., Chen, K., Li, H., Lu, Y., & Wang, L. (2024) · 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/cvpr52733.2024.00011 · Cited by 48
“However, it struggles with SIFT keypoint computation and matching [26] in the low light.”
BackgroundAn enhanced method for human action recognition
Moussa, M. M., Hamayed, E., Fayek, M. B., et al. (2013) · Journal of Advanced Research · doi:10.1016/j.jare.2013.11.007 · Cited by 46
Valenti, F., Giaquinto, D., Musto, L., Zinelli, A., Bertozzi, M., & Broggi, A. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569695 · Cited by 49
Jiang, J., Liu, J., Cheng, C., et al. (2019) · Remote Sensing · doi:10.3390/rs11050587 · Cited by 46
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
Kim, S., Min, D., Ham, B., Lin, S. C.-F., & Sohn, K. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2803169 · Cited by 44
Optimized hierarchical block matching for fast and accurate image registration
Je, C., & Park, H. (2013) · Signal Processing Image Communication · doi:10.1016/j.image.2013.04.002 · Cited by 46
HOG and LBP: Towards a robust face recognition system
Ghorbani, M., Targhi, A. T., & Dehshibi, M. M. (2015) · International Conference on Digital Information Management · doi:10.1109/icdim.2015.7381860 · Cited by 46
Tree-Based Morse Regions: A Topological Approach to Local Feature Detection
Xu, Y., Monasse, P., Géraud, T., & Najman, L. A. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2364127 · Cited by 47
Visual indoor positioning with a single camera using PnP
Deretey, E., Ahmed, M. T., Marshall, J. A., et al. (2015) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2015.7346756 · Cited by 46
XGBoost: 2D-Object Recognition Using Shape Descriptors and Extreme Gradient Boosting Classifier
Monika, M., Kumar, M., & Kumar, M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-6876-3_16 · Cited by 46
A Review of Object Detection Techniques
Li, K., & Cao, L. (2020) · International Conference on Electromechanical Control Technology and Transportation (ICECTT) · doi:10.1109/icectt50890.2020.00091 · Cited by 55
Geometry-Aware Similarity Learning on SPD Manifolds for Visual Recognition
Huang, Z., Wang, R., Li, X., et al. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2729660 · Cited by 46
Scene Classification Based on the Fully Sparse Semantic Topic Model
Zhu, Q., Zhong, Y., Zhang, L., et al. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2709802 · Cited by 46
Text2Loc: 3D Point Cloud Localization from Natural Language
Xia, Y., Shi, L., Ding, Z., Henriques, J. F., & Cremers, D. (2024) · 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/cvpr52733.2024.01417 · Cited by 61
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
