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
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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 51,401–51,500 of 56,204
Priya, K., Das, B., Adak, C., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36042-7_9
Extraction of geometrical corners of buildings for infrared imaging sequence
Wang, J., Zhou, F., Xue, B., et al. (2014) · Conference proceedings · doi:10.1109/mwscas.2014.6908505
SpatialVISION – software module for objects identification before robotic gripping
Starețu, I., & Moldovan, C. C. (2017) · Journal of Engineering Sciences and Innovation · doi:10.56958/jesi.2017.2.1.22
A camera self-calibration method based on dual constraints of multi-view images
Liu, F., Han, W., Xu, Q., et al. (2011) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2011.6096839
Genetic Algorithm-Based Multi-Resolution Pyramid Registration for Fingerprint Recognition
Ren, L., Li, D., Zhao, Y., et al. (2025) · doi:10.1109/asma67812.2025.11553092
“The performance was compared across several methods, including SIFT [1], DeepPore [2], Affine Transformation [5], Phase Correlation [4], and the proposed algorithm.”
Deep Neural Network for Product Classification System with Korean Character Image
Sung, J.-K., Park, S.-M., Sin, S.-Y., et al. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i3.33.21008
Feature Points Selection for Rectangle Panorama Stitching
Yan, W., Wang, S., Yue, G., et al. (2019) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-030-23307-5_13
Learning compact representations for large scale image search
Jain, H. (2018) · theses.fr (ABES) · doi:10.70675/b7435784z5c25z445bzadfcz780bb560cf20
Appearance-based Object Pose Estimation and Misestimation Detection by Shape Fitness
NISHIKAWA, R., Noguchi, H., YAMAZAKI, T., et al. (2013) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.79.1050
Combined Use of Multimodal Similarity Measures for Visual to Radar Image Registration
Uss, M., Vozel, B., Лукин, В. В., et al. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8517800
Pose estimation and trajectory derivation from underwater imagery
Sehgal, A., Cernea, D., & Makaveeva, M. (2012) · doi:10.1109/oceans-yeosu.2012.6263625
FLD-SIFT: Class Based Scale Invariant Feature Transform for Accurate Classification of Faces
Shekar, B. H., Kumari, M. S., Mestetskiy, L. M., et al. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-19542-6_3
Schleiss, M., Hölzemann, H., Rouatbi, F., Fiolka, T., Pany, T., Förstner, R., & Cremers, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37261-1_7
Registering Long-Term Image Series
Droege, D., & Paulus, D. W. R. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74272-2_101
Zarei, A., & Moallem, P. (2025) · Signal and Data Processing · doi:10.66224/jsdp.22.3.3
Blur-ASKNet: An Effective Network for Remote Sensing Image Classification Capturing Joint Features
Liu, T., Wang, Q., & Hao, G. (2023) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl58838.2023.10166484
A Fast Target Recognition Algorithm Based on MSA and MSR
Huang, J., Wang, Y., Ji, H., et al. (2012) · International Conference on Industrial Control and Electronics Engineering · doi:10.1109/icicee.2012.173
A Method Based on Prior Information of Feature Points Registration for SAR Images
Li, D., Shi, P., & Tan, X. (2016) · International Conference on Advanced Materials and Computer Science (ICAMCS) · doi:10.2991/icamcs-16.2016.65
A novel feature descriptor based on microscopy image statistics
Bayramoğlu, N., Kannala, J., Åkerfelt, M., Kaakinen, M., Eklund, L., Nees, M., & Heikkilä, J. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351292
Pill-ID: Matching and retrieval of drug pill images q
Lee, Y., Park, U., Jain, A. K., et al. (2012)
Plastic Waste Detection and Removal System in College Campus
Vijay Franklin, J., Thirumoorthy, P., Karthickkumar, N., Absar Ahamed, J., S, A. C., & Kaleeshwaran, K. (2026) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla67915.2026.11522438
Language-Driven Semantic Edge Detection
余, 斌. (2025) · Computer Science and Application · doi:10.12677/csa.2025.152044
Detection and description of scale invariant interest points with shearlets
Duval-Poo, M. A., Noceti, N., Odone, F., et al. (2017) · International Conference on Sampling Theory and Applications · doi:10.1109/sampta.2017.8024340
Morales, M., Morocho, J., López, X., et al. (2024) · Signals and communication technology · doi:10.1007/978-3-031-47100-1_1
Hassan, D., Madani, K., & Sabourin, C. (2015) · IEEE International Conference on Awareness Science and Technology (iCAST) · doi:10.1109/icawst.2015.7314047
Learning to Find Good Correspondences Based on Global and Local Attention Mechanism
Chen, J., Gu, Y., & Luo, L. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9728340
PRIVACY PROTECTED FACE VERIFICATION SYSTEM USING SPARSE CLASSIFIER.
Thasni, A. N., & Deepthi, V. R. (2016) · International Journal of Advanced Research · doi:10.21474/ijar01/1513
Graph model-based feature point matching
Wang, C. (2013) · doi:10.32657/10356/54912
Fast Object Detection Using a Frame Skip Method
Park, J. W., Kim, J.-S., & Lee, H. (2020) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia49877.2020.9276991
Face feature matching based on semantic Information
Guo, J., Chen, Y., Li, J., & Xie, J. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2024/1/012015
Parallax-tolerant unmanned aerial vehicle remote sensing image stitching based on optimal seams
Tang, X., Huo, J., Li, W., et al. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.2.023009
Final Report on Video Log Data Mining Project
Krishnan, A., Lewis, C., & Day, D. D. (2012)
Abdedayem, F. (2018) · Open Access Biostatistics & Bioinformatics · doi:10.31031/oabb.2018.01.000516
Extending Recognition in a Changing Environment
Harari, D., & Ullman, S. (2013) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0004281106320640
Deep Learning-Based Classification of Military Aircraft Types from Satellite Imagery
Varlik, F., & Özdemir, C. (2026) · Turkish Journal of Science and Technology · doi:10.55525/tjst.1928640
Displacement detection method of QR code reference object based on feature points
Yang, Q., Zhang, B., & Ji, X. (2022) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics55264.2022.9873787
Food Packaging Target Location and Recognition Technology Based on Machine Vision
Xiong, Z. (2023) · Academic Journal of Science and Technology · doi:10.54097/ajst.v8i1.14151
Automatic diagnosis of heating in oil-filled terminals of cables
Yao, S., Liu, Z., Zhao, Y. J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-22506-0
Task-Specific Salience for Object Recognition
Revaud, J., Lavoué, G., Ariki, Y., et al. (2011) · Studies in computational intelligence · doi:10.1007/978-3-642-17934-1_4
Computer Vision and Forensic Investigation
Anani-Manyo, N., & Liu, R. (2022) · ASCE Forensic Engineering Congress · doi:10.1061/9780784484548.061
Near-infrared Coded Illumination-based Image Deblurring and Colorization
Lee, J., & Jeon, B. (2021) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic51217.2021.9369762
3D pipeline reconstruction and diameter measurement method based on target segmentation
Wu, G., Zhang, H., Yan, Z., et al. (2024) · doi:10.1117/12.3014403
Salient object detection based on Drosophila vision-inspired model
Gu, Y., Chen, Z., Feng, H., et al. (2021) · International Symposium on Artificial Intelligence and Robotics (ISAIR) · doi:10.1117/12.2602046
PawPrint: Whose Footprints are These? Identifying Animal Individuals by their Footprints
Song, I., Hwang, H., & Lee, J. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084465
Pérez, D., Bromberg, F., & Díaz, C. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.02775
Electron Microscopy Image Registration with Twin Axial Transformer and Progressive Training
Zhou, C., Jin, H., Yin, C., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73480-9_20
A Study on Applying Real Card to Online Trading Card Game
Park, J.-I., & Kim, S. H. (2012) · Journal of Korea Game Society · doi:10.7583/jkgs.2012.12.4.45
Loop Closure Detection Based on Differentiable Manifold
Dong, T., Xue, B., Zhang, Q., Zhao, Y., Li, W., & Li, M. (2022) · Mobile Information Systems · doi:10.1155/2022/4373064
A Plane Segmentation System Based on Affine Homography and Optical Flow
Aires, K. R., & de Medeiros, A. A. D. (2010) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2010.53
Kinect image classification using LLC
Zhao, Y., Jin, L., & Tao, D. (2012) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2382336.2382352
Hide and Seek tracker: Real-time recovery from target lossPreprint
Bay, A., Sidiropoulos, P., Vazquez, E., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07844
An investigation into viewpoint invariant pedestrian recognition for surveillance systems
Stieber, B. M. (2010) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
A Comprehensive Survey of Palmprint based Biometric Verification System
Gupta, M. R., Namdeo, M. V., & Chourey, M. R. (2017) · IJARCCE · doi:10.17148/ijarcce.2017.6133
Li, X., Wang, S., Mao, Z., et al. (2025) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn64981.2025.11228231
Iterative closest points method based on photometric weight for 3D object reconstruction
Shin, D.-W., & Ho, Y. (2016) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2016.7820707
A target recognition matching method with high reliability
Chen, S., Liu, Z., Xia, M., et al. (2011) · doi:10.1109/emeit.2011.6024094
Lu, J., Chen, Q., Pan, M., et al. (2026) · Displays · doi:10.1016/j.displa.2026.103712
Fast Object Classification Using Texture and Color Information for Video Surveillance Applications
Islam, M. K., Jahan, F., Min, J.-H., et al. (2011) · doi:10.12673/jant.2011.15.1.140
UEC at TRECVID 2007 High Level Feature Task.
Liu, O., Tang, Z., & Yanai, . (2007)
Real-Time Motion Detection of Flying Objects With Dynamic Camera
Zhilenkov, A., & Krupinin, O. S. (2025) · International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) · doi:10.1109/icieam65163.2025.11028163
“Hypotheses were tested for using some statistical and structural descriptors [13, 14, 15].”
Clasificador de pigmentación de la ballena azul aplicando el método SIFT en medios no controlados
Ramos-Arredondo, R. I., Carvajal-Gámez, B. E., Gallegos-Funes, F. J., et al. (2015) · Research in Computing Science · doi:10.13053/rcs-91-1-6
Behavior Based Robot Localisation Using Stereo Vision
Sagrebin, M., Pauli, J., & Herwig, J. (2008) · Springer eBooks · doi:10.1007/978-3-540-78157-8_33
Miniello, G., & Salandra, M. L. (2021) · doi:10.1117/12.2593110
Applicability of Self-Organizing Maps in Content-Based Image Classification
Rohit, K., Gorthi, R. K., & Mishra, D. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-2104-6_28
Semi-Automatic Estimation of Body Orientation in Football
Husan, S. U., Merino, M. V., & Nieto, E. (2025) · doi:10.1145/3728423.3759407
Learning and Exploiting Camera Geometry for Computer Vision
Wang, P. (2016) · DukeSpace (Duke University)
Joint Segmentation-Registration Optimization for Single Snapshot Spatial Frequency Domain Imaging
Wu, C., Sheng, K., Zhang, Y., et al. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3719256
“In order to verify the advancement of AR-Net, we introduce the excellent traditional alignment algorithm SIFT [35] for comparison.”
Selective submap joining SLAM for autonomous vehicles
Masó, A., & Josep, M. (2011) · TDX (Tesis Doctorals en Xarxa)
VOCA: Visual Odometry with Codec Awareness
Hilscher, N. A., de Mayo, M., Muhle, D., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37464-6_32
Geolocation of Monitoring Wells Using Small Unmanned Aircraft Systems
DeYoung, J. (2021) · Journal of the Arkansas Academy of Science
Improved Spatio-Temporal Context Target Tracking Algorithm
Shi, Y., Zang, J., & Liu, Y. (2017) · International Conference on Education, Management, Information and Mechanical Engineering (EMIM) · doi:10.2991/emim-17.2017.40
Lingua, A. M., Marenchino, D., & Nex, F. (2009) · PORTO@Iris
Khalif, M. I., Djuana, E., Rambung, R. A., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6216522
Towards Practical Indoor Navigation with Multi-Perspective Image Descriptors
Buduan, C. J. M., Dagdagan, A. A., Bareng, S. J. B., et al. (2025) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict65714.2025.11100667
“Specifically, we used a combination of the Accelerated-KAZE (AKAZE) [21] and Scale-Invariant Feature Transform (SIFT) [22].”
A Statistical Approach To View Synthesis
Berkowitz, P. (2009)
Contour Sparse Representation with SDD Features for Object RecognitionPreprint
Wang, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05704
From Convolution to Attention and Beyond: A Systematic Review of Modern Vision Architectures
Chakrabarty, A., Aziz, A., Tahsin, A., Nawreen, N. I., Alam, M. G. R., & Maidin, S. S. (2026) · IEEE Access · doi:10.1109/access.2026.3715655
Topological localization based on key-frames selection and vocabulary tree for mobile robots
Xiong, D., Lu, H., Zeng, Z., et al. (2013) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2013.6739848
Shin, J., & Shim, C.-S. (2026) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2026.e05893
Image defogging algorithm based on metaheuristic dark channel prior
Li, B., Cao, R., Hu, H., et al. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117512
FACE VERIFICATION ACROSS AGE PROGRESSIONPreprint
Kasar, A. B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5359707
Selecting matching area based on deep learning keypoint features
Tian, D., Tian, T., & Tian, J. (2024) · doi:10.1117/12.2688337
Orientierung großer Bildverbände
Mayer, H., & Michelini, M. (2017) · Springer eBooks · doi:10.1007/978-3-662-47094-7_39
Image Segmentation and Feature Extraction Using Sift-Sad Algorithm for Disparity Map Generation
Kamencay, P., Zachariášová, M., Hudec, R., et al. (2013) · Communications - Scientific letters of the University of Zilina · doi:10.26552/com.c.2013.2a.185-190
Meng, L., Tan, A., & Wunsch, D. C. (2019) · Advanced information and knowledge processing · doi:10.1007/978-3-030-02985-2_1
LV-FeatEx: Large Viewpoint-Image Feature Extraction
Wang, Y., Wang, Y., Wang, Y., et al. (2025) · Mathematics · doi:10.3390/math13071111
FPGA synthesis of an stereo image matching architecture for autonomous mobile robots
Doménech‐Asensi, G., Ruı́z, R., Zapata, J., et al. (2017) · Conference on Design of Circuits and Integrated Systems · doi:10.1109/dcis.2017.8311639
Bolts as Anchors: Precise Mine Localization Using Rock Bolts
Yang, Y., & Stoyanov, T. (2026) · IEEE transactions on field robotics. · doi:10.1109/tfr.2026.3706036
A Data-driven Transfer Learning Method for Radio Map EstimationPreprint
Jaiswal, R. K., Elnourani, M., Deshmukh, S., & Beferull‐Lozano, B. (2023) · doi:10.36227/techrxiv.23274263.v2
Cho, K., Park, J., Kim, K., Park, S., Park, J., Yun, J., Ahn, Y., Seo, J.-B., & Kim, N. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4165833
Hiouile, L. E., Errami, A., Azami, N., et al. (2020) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1145/3419604.3419785
HSK-YOLO: A Strawberry Maturity Detection Algorithm Based on Improved YOLOv8
Cheng, J., Wang, Y., Liu, M., et al. (2024) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait63902.2024.11022197
“Traditional machine learning methods often require the manual extraction of features, using approaches like Scale-Invariant Feature Transform (SIFT)[3] or Histogram of Oriented Gradients (HOG)[4], followed by training classifiers (such as Support Vector Machines, SVM. to recognize objects.”
Shift Error Analysis in Image Based 3D Skull Feature Reconstruction
Chau, T., Bui, T. D., & Dang, T. K. (2012) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2012.38
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
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