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 9,101–9,200 of 56,203
Local Feature Based Person Reidentification in Infrared Image Sequences
Jüngling, K., & Arens, M. (2010) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2010.75 · Cited by 35
A Unified Framework for Object Retrieval and Mining
Anjulan, A., & Canagarajah, N. C. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.2005801 · Cited by 35
Line Matching in Wide-Baseline Stereo: A Top-Down Approach
Alshahri, M. A. S., & Yılmaz, A. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2331147 · Cited by 35
Mining Compositional Features From GPS and Visual Cues for Event Recognition in Photo Collections
Yuan, J., Luo, J., & Wu, Y. N. (2010) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2010.2051868 · Cited by 35
Picard, D., Gosselin, P.-H., & Gaspard, M.-C. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2409557 · Cited by 34
Maÿe, A., & Engel, A. K. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979919 · Cited by 36
Sun, X., Zhu, Q., & Qin, Q. (2021) · IEEE Access · doi:10.1109/access.2021.3052977 · Cited by 35
A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning
Bai, C., Bai, X., & Wu, K. (2023) · Electronics · doi:10.3390/electronics12244902 · Cited by 39
An efficient foreign objects detection network for power substation
Xu, L., Song, Y., Zhang, W., An, Y., Wang, Y., & Ning, H. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104159 · Cited by 35
View-invariant person re-identification with an Implicit Shape Model
Jüngling, K., & Arens, M. (2011) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2011.6027319 · Cited by 35
An Integrated System for Moving Object Classification in Surveillance Videos
Chen, L., Feris, R., Zhai, Y., et al. (2008) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2008.24 · Cited by 35
Fisher Linear Discriminant Analysis for text-image combination in multimedia information retrieval
Moulin, C., Largeron, C., Ducottet, C., Géry, M., & Barat, C. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.06.003 · Cited by 35
Recursive Implementation of the Gaussian Filter Using Truncated Cosine Functions
Charalampidis, D. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2549985 · Cited by 37
G-Michael, T., Marchand, B., Tucker, J. D., et al. (2015) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2015.2465631 · Cited by 35
Weighting scheme for image retrieval based on bag‐of‐visual‐words
Zhu, L., Jin, H., Zheng, R., & Feng, X. (2014) · IET Image Processing · doi:10.1049/iet-ipr.2013.0375 · Cited by 34
Jiang, J., Zhang, Q., Wang, W., et al. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3158644 · Cited by 35
Neumann, C., Behling, R. P., Schindhelm, A., et al. (2019) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.121 · Cited by 35
MSFD: Multi-Scale Segmentation-Based Feature Detection for Wide-Baseline Scene Reconstruction
Mustafa, A., Kim, H., & Hilton, A. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2872906 · Cited by 30
Democratic Diffusion Aggregation for Image Retrieval
Gao, Z., Xue, J., Zhou, W., Pang, S., & Tian, Q. C. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2568748 · Cited by 32
Repeated Pattern Detection Using CNN Activations
Lettry, L., Perďoch, M., Vanhoey, K., et al. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.13 · Cited by 35
AIR-PolSAR-Seg: A Large-Scale Data Set for Terrain Segmentation in Complex-Scene PolSAR Images
Wang, Z., Zeng, X., Yan, Z., Kang, J. S., & Sun, X. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3170326 · Cited by 39
Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval
Pickup, D., Sun, X., Rosin, P. L., et al. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.02.021 · Cited by 35
SMD: A Locally Stable Monotonic Change Invariant Feature Descriptor
Gupta, R. K., & Mittal, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_20 · Cited by 35
Yoshihashi, R., Kawakami, R., Iida, M., & Naemura, T. (2017) · Wind Energy · doi:10.1002/we.2135 · Cited by 39
Cross-Modal Integration for Performance Improving in Multimedia: A Review
Maragos, P. A., Gros, P., Katsamanis, A., & Papandreou, G. (2008) · Springer eBooks · doi:10.1007/978-0-387-76316-3_1 · Cited by 36
Uniting Keypoints: Local Visual Information Fusion for Large-Scale Image Search
Liu, Z., Li, H., Zhou, W., et al. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2399851 · Cited by 35
Pain detection from facial characteristics using supervised descent method
Neshov, N. N., & Manolova, A. H. (2015) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2015.7340738 · Cited by 36
Fayaz, S., Shah, S. Z. A., Din, N. M. U., Gul, N., & Assad, A. (2024) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558286875231215054324 · Cited by 40
EAGLE: A novel descriptor for identifying plant species using leaf lamina vascular features
Charters, J., Wang, Z., Chi, Z., et al. (2014) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2014.6890557 · Cited by 35
Monocular visual odometry for robot localization in LNG pipes
Hansen, P., Alismail, H., Rander, P. W., et al. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5979681 · Cited by 35
Contextual Similarity Distillation for Asymmetric Image Retrieval
Wu, H., Wang, M., Zhou, W., Li, H., & Tian, Q. C. (2022) · 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/cvpr52688.2022.00927 · Cited by 39
Increasing the capturing angle in print-cam robust watermarking
Pramila, A., Keskinarkaus, A., & Seppänen, T. (2017) · Journal of Systems and Software · doi:10.1016/j.jss.2017.10.029 · Cited by 34
Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene
Akkad, N. E., Merras, M., Saaidi, A., & Satori, K. (2013) · The Visual Computer · doi:10.1007/s00371-013-0877-2 · Cited by 35
On fusing the latent deep CNN feature for image classification
Liu, X., Zhang, R., Meng, Z., Hong, R., & Liu, G. (2018) · World Wide Web · doi:10.1007/s11280-018-0600-3 · Cited by 35
Multiview Clustering by Consensus Spectral Rotation Fusion
Chen, J., Mao, H., Peng, D., et al. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3310339 · Cited by 35
Stereo-Vision Based 3D Modeling and Localization for Unmanned Vehicles
Se, S., & Jasiobedzki, P. (2008) · Cited by 35
Specific Comic Character Detection Using Local Feature Matching
Sun, W., Burie, J.-C., Ogier, J.-M., et al. (2013) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2013.62 · Cited by 35
Efficient hybrid image denoising scheme based on SVM classification
Routray, S., Ray, A. K., Mishra, C., & Palai, G. (2017) · Optik · doi:10.1016/j.ijleo.2017.11.116 · Cited by 36
Xue, K., Liu, Y., Ogunmakin, G., Chen, J., & Zhang, J. (2012) · Machine Vision and Applications · doi:10.1007/s00138-012-0426-4 · Cited by 35
Zhang, W., Deng, Z., Zhang, T., Choi, K., Wang, J., & Wang, S. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3099696 · Cited by 33
Efficient image mosaicing for multi-robot visual underwater mapping
Elibol, A., Kim, J., Gracias, N., & García, R. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.04.020 · Cited by 34
Maeda, H., Sekimoto, Y., & Seto, T. (2016) · International Workshop on Mobile Geographic Information Systems · doi:10.1145/3004725.3004729 · Cited by 35
Recursive interferometric representations
Mollai, S. (2010) · Cited by 36
When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)
Villena-Martínez, V., Oprea, S., Saval-Calvo, M., Azorín-López, J., Fuster-Guilló, A., & Fisher, R. B. (2020) · Applied Sciences · doi:10.3390/app10217524 · Cited by 40
Deep Learning for Underwater Visual Odometry Estimation
Teixeira, B., Silva, H. M., Matos, A. C., & Silva, E. (2020) · IEEE Access · doi:10.1109/access.2020.2978406 · Cited by 37
A SLAM Overview from a User’s Perspective
Frese, U., Wagner, R., & Röfer, T. (2010) · Künstliche Intell. · doi:10.1007/s13218-010-0040-4 · Cited by 35
Learning Sparse Representation for Objective Image Retargeting Quality Assessment
Jiang, Q., Shao, F., Lin, W., & Jiang, G. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2690452 · Cited by 35
Makeup-robust face verification
Hu, J., Ge, Y., Lu, J., et al. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638073 · Cited by 35
Probabilistic Deformation Models for Challenging Periocular Image Verification
Smereka, J. M., Boddeti, V. N., & Kumar, B. V. K. V. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2434271 · Cited by 35
Approximate similarity search for online multimedia services on distributed CPU–GPU platforms
Teodoro, G., Junior, E. A. D. V., Mariano, N., Torres, R. D. S., Meira, W., & Saltz, J. H. (2013) · The VLDB Journal · doi:10.1007/s00778-013-0329-7 · Cited by 34
Robust object pose estimation via statistical manifold modeling
Mei, L., Liu, J., Hero, A. O., et al. (2011) · International Conference on Computer Vision · doi:10.1109/iccv.2011.6126340 · Cited by 35
VSD2014: A dataset for violent scenes detection in hollywood movies and web videos
Schedi, M., Sjöberg, M., Mironică, I., Ionescu, B. E., Quang, V. L., Jiang, Y., & Demarty, C.-H. (2015) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2015.7153604 · Cited by 35
Globally Optimal Inlier Set Maximization with Unknown Rotation and Focal Length
Bazin, J., Seo, Y., Hartley, R. I., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_52 · Cited by 35
Development of 3D rock fragmentation measurement system using photogrammetry
Jang, H., Kitahara, I., Kawamura, Y., et al. (2019) · International Journal of Mining Reclamation and Environment · doi:10.1080/17480930.2019.1585597 · Cited by 35
Liu, W., Cao, J., Yang, L., Xu, L., Qiu, X., & Li, J. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2624733 · Cited by 32
Automatic co-registration of 3D multi-sensor point clouds
Persad, R., & Armenakis, C. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.05.014 · Cited by 29
RGB-D sensor-based visual SLAM for localization and navigation of indoor mobile robot
-, W. P. L. J. Z. Y., Li, Z., & Su, C. (2016) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm.2016.7606899 · Cited by 36
Baguley, D., & Norman, M. (2001) · PubMed · Cited by 34
Geometric Graph Matching Using Monte Carlo Tree Search
Pinheiro, M. A., Kybic, J., & Fua, P. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2636200 · Cited by 33
Xu, J., Chen, H.-J., Qian, K., Dong, E., Sun, M., Wu, C., Zhang, L., & Yang, Z. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351272 · Cited by 34
On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC
Tran, N.-T., Tan, D.-K. L., Doan, A.-D., Do, T.-T., Bui, T.-A., Tan, M., & Cheung, N. M. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2881829 · Cited by 36
Improving Underwater Obstacle Detection using Semantic Image Segmentation
Arain, B., McCool, C., Rigby, P., et al. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793588 · Cited by 34
A brain-inspired SLAM system based on ORB features
Zhou, S.-C., Yan, R., Li, J., Chen, Y., & Tang, H. (2017) · Machine Intelligence Research · doi:10.1007/s11633-017-1090-y · Cited by 35
Total Cluster: A person agnostic clustering method for broadcast videos
Tapaswi, M., Parkhi, O. M., Rahtu, E., Sommerlade, E., Stiefelhagen, R., & Zisserman, A. (2014) · Indian Conference on Computer Vision, Graphics & Image Processing · doi:10.1145/2683483.2683490 · Cited by 41
Iwamura, M., Sato, T., & Kise, K. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.439 · Cited by 34
Automated detection of diabetic retinopathy in fundus images using fused features
Bibi, I., Mir, J., & Raja, G. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00929-5 · Cited by 34
FPGA-based detection of SIFT interest keypoints
Chang, L., Hernández-Palancar, J., Sucar, L. E., & Arias-Estrada, M. (2012) · Machine Vision and Applications · doi:10.1007/s00138-012-0430-8 · Cited by 34
Video Registration Using Dynamic Textures
Ravichandran, A., & Vidal, R. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.61 · Cited by 34
Simultaneous Local Binary Feature Learning and Encoding for Face Recognition
Lu, J., Liong, V. E., & Zhou, J. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.424 · Cited by 34
Cylindrical panoramic image stitching method based on multi-cameras
Lin, M., Xu, G., Ren, X., et al. (2015) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber.2015.7288097 · Cited by 34
Kim, J. W., Seitz, S. M., & Agrawala, M. (2004) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1029632.1029650 · Cited by 34
Autonomous science during large‐scale robotic survey
Thompson, D. R., Wettergreen, D. S., & Peralta, F. J. C. (2011) · Journal of Field Robotics · doi:10.1002/rob.20391 · Cited by 34
Point-based rendering enhancement via deep learning
Bui, G. T. T., Le, T., Morago, B., & Duan, Y. (2018) · The Visual Computer · doi:10.1007/s00371-018-1550-6 · Cited by 32
Santos, T. T., Koenigkan, L. V., Barbedo, J. G. A., & Rodrigues, G. C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16220-1_18 · Cited by 34
Aligning ASL for Statistical Translation Using a Discriminative Word Model
Farhadi, A., & Forsyth, D. A. (2006) · 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.2006.51 · Cited by 34
Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application
Karlsson, R., Schön, T. B., Törnqvist, D., Conte, G., & Gustafsson, F. (2008) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2008.4526442 · Cited by 31
Classification of Breast Cancer Histopathological Images Using KAZE Features
Morillo, D., González, J., García‐Rojo, M., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78759-6_26 · Cited by 34
Multiple Object Tracking in Robotic Applications: Trends and Challenges
Gad, A., Basmaji, T., Yaghi, M., Alheeh, H., AlKhedher, M. A., & Ghazal, M. A. (2022) · Applied Sciences · doi:10.3390/app12199408 · Cited by 35
Image Retrieval Based on a Multi-Integration Features Model
Chu, K., & Liu, G. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/1461459 · Cited by 34
Indexing of Satellite Images With Different Resolutions by Wavelet Features
Luo, B., Aujol, J., Gousseau, Y., et al. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.925367 · Cited by 34
Hu, Q., He, S., Wang, S., Liu, Y., Zhang, Z., He, L., Wang, F., Cai, Q., Shi, R., & Yang, Y. (2017) · Sensors · doi:10.3390/s17061305 · Cited by 35
Sampling Minimal Subsets with Large Spans for Robust Estimation
Tran, Q. H., Chin, T.-J., Chojnacki, W., & Suter, D. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0643-y · Cited by 31
Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
Han, K., Wan, W., Yao, H., et al. (2017) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2017.p0632 · Cited by 34
Smoothness-Driven Consensus Based on Compact Representation for Robust Feature Matching
Fan, A., Jiang, X., Ma, Y., Mei, X., & Ma, J. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3118409 · Cited by 34
Cao, J., Wang, M., Li, Y., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215136 · Cited by 34
Exploring relations of visual codes for image classification
Huang, Y., Huang, K., Wang, C., & Tan, T. (2011) · 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.2011.5995655 · Cited by 33
Pugeault, N., Wörgötter, F., & Krüger, N. (2010) · International Journal of Humanoid Robotics · doi:10.1142/s0219843610002209 · Cited by 34
Thermal Face Recognition Under Temporal Variation Conditions
Hermosilla, G., Verdugo, J. L., Farías, G., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2704296 · Cited by 34
Learning Pixel-wise Alignment for Unsupervised Image Stitching
Jia, Q., Feng, X., Liu, Y., et al. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612298 · Cited by 35
Action categorization by structural probabilistic latent semantic analysis
Zhang, J., & Gong, S. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.04.006 · Cited by 34
Wang, S., Zhang, W., Zhao, X., et al. (2024) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/j.ijrmms.2024.105774 · Cited by 34
Real-time mosaicing of fetoscopic videos using SIFT
Daga, P., Chadebecq, F., Shakir, D. I., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2217172 · Cited by 34
A perceptual scheme for fully automatic video shot boundary detection
Birinci, M., & Serkan Kiranyaz, B. (2014) · Signal Processing Image Communication · doi:10.1016/j.image.2013.12.003 · Cited by 34
MAPNet: Multi-modal attentive pooling network for RGB-D indoor scene classification
Li, Y., Zhang, Z., Cheng, Y., Wang, L., & Tan, T. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.02.005 · Cited by 34
Suresha, M., Kuppa, S., & Raghukumar, D. S. (2020) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00190-x · Cited by 40
ChangeChip: A Reference-Based Unsupervised Change Detection for PCB Defect Detection
Fridman, Y., Rusanovsky, M., & Oren, G. (2021) · IEEE Physical Assurance and Inspection of Electronics (PAINE) · doi:10.1109/paine54418.2021.9707699 · Cited by 34
Multiple camera fruit localization using a particle filter
Mehta, S., Ton, C., Asundi, S., & Burks, T. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.08.007 · Cited by 35
OANet: Learning Two-View Correspondences and Geometry Using Order-Aware Network
Zhang, J., Sun, D., Luo, Z., Yao, A., Chen, H., Zhou, L., Shen, T., Chen, Y., Quan, L., & Liao, H. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3048013 · Cited by 37
Ngo, C., Jiang, Y., Wei, X.-Y., et al. (2009) · Cited by 34
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
