The DOI matches its Crossref record; 44 of the 45 references it cites were found in a registry or an index.
Scale & Affine Invariant Interest Point Detectors
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
“Using the determinant of the Hessian matrix rather than its trace (the Laplacian) seems advantageous, as it fires less on elongated, ill-localised structures.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [15]
In the citing paper
“By studying the existing detectors and from published comparisons [15, 8], we can conclude that (1) Hessian-based detectors are more stable and repeatable than their Harris-based counterparts.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [15]
In the citing paper
“Also, (2) approximations like the DoG can bring speed at a low cost in terms of lost accuracy.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [15]
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
A 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
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
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
The design and use of steerable filters
Freeman, W. T., & Adelson, E. H. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.93808 · Cited by 2,937
Feature detection with automatic scale selection
Lindeberg, T. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008045108935 · Cited by 2,541
Evaluation of Interest Point Detectors
Schmid, C., Mohr, R., & Bauckhage, C. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008199403446 · Cited by 1,484
Self-citationLocal grayvalue invariants for image retrieval
Schmid, C., & Mohr, R. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.589215 · Cited by 1,430
Self-citationAn Affine Invariant Interest Point Detector
Mikolajczyk, K., & Schmid, C. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47969-4_9 · Cited by 1,325
Self-citation
Research works printed but not linked: 5
- Förstner,W. and Gülchm, E. 1987. A fast operator for detection and precise location of distinct points, corners and centres of circular features. In Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, pp. 281–305.
- Lazebnik, S., Schmid, C., and Ponce, J. 2003. Sparse texture representation using affine-invariant neighborhoods. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, USA, pp. 319–324.
- Mikolajczyk, K. 2002. Interest point detection invariant to affine transformations. PhD thesis, Institut National Polytechnique de Grenoble.
- Pritchett, P. and Zisserman, A. 1998.Wide baseline stereo matching. In Proceedings of the 6th International Conference on Computer Vision, Bombay, India. IEEE Computer Society Press, pp. 754–760.
- Schaffalitzky, F. and Zisserman, A. 2002. Multi-view matching for unordered image sets. In Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, pp. 414–431.
Cited by
Sorted by citations
Items 301–400 of 1,003
Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features
Toews, M., & Wells, W. (2012) · Medical Image Anal. · doi:10.1016/j.media.2012.11.002 · Cited by 98
Performance Comparisons of Contour-Based Corner Detectors
Awrangjeb, M., Lu, G., & Fraser, C. S. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2200493 · Cited by 84
Improved SIFT-Features Matching for Object Recognition
Alhwarin, F., Wang, C., Ristić–Durrant, D., & Gräser, A. (2008) · Electronic workshops in computing · doi:10.14236/ewic/vocs2008.16 · Cited by 84
Large deformation measurement using digital image correlation: a fully automated approach
Zhou, Y., Pan, B., & Chen, Y. Q. (2012) · Applied Optics · doi:10.1364/ao.51.007674 · Cited by 84
Object Mining Using a Matching Graph on Very Large Image Collections
Philbin, J. F., & Zisserman, A. (2008) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1109/icvgip.2008.103 · Cited by 84
Pinz, A. J. (2005) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000003 · Cited by 84
Positive Semidefinite Metric Learning with BoostingPreprint
Shen, C., Kim, J., Wang, L., & van den Hengel, A. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0910.2279 · Cited by 84
Fast Template Matching With Polynomials
Omachi, S., & Omachi, M. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901243 · Cited by 83
Distributed metric calibration of ad hoc camera networks
Devarajan, D., Radke, R. J., & Chung, H. (2006) · ACM Transactions on Sensor Networks · doi:10.1145/1167935.1167939 · Cited by 83
Feature hierarchies for object classification
Epshtein, B., & Uliman, S. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.98 · Cited by 85
Image webs: Computing and exploiting connectivity in image collections
Heath, K., Gelfand, N., Ovsjanikov, M., Aanjaneya, M., & Guibas, L. J. (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.2010.5539991 · Cited by 82
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters Revisited
Barroso-Laguna, A., & Mikolajczyk, K. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3145820 · Cited by 82
Self-citationA New Digital Image Watermarking Algorithm Resilient to Desynchronization Attacks
Wang, X., Wu, J., & Niu, P. (2007) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2007.908233 · Cited by 82
Image Registration Using Adaptive Polar Transform
Matungka, R., Zheng, Y. F., & Ewing, R. L. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2025010 · Cited by 82
A visual odometry framework robust to motion blur
Pretto, A., Menegatti, E., Bennewitz, M., Burgard, W., & Pagello, E. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152447 · Cited by 82
Yu, Q., Ni, D., Jiang, Y., Yan, Y., An, J., & Sun, T. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.10.019 · Cited by 81
Xie, L., Wang, J., Zhang, B., & Tian, Q. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2408566 · Cited by 81
Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images
Al-khafaji, S. L., Zhou, J. W., Zia, A., & Liew, A. W. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2749145 · Cited by 81
Large-Scale Video Hashing via Structure Learning
Ye, G., Liu, D., Wang, J., & Chang, S. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.282 · Cited by 80
Query Adaptive Similarity for Large Scale Object Retrieval
Qin, D., Wengert, C., & Gool, L. V. (2013) · 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.2013.211 · Cited by 79
Corner detection based on gradient correlation matrices of planar curves
Zhang, X., Wang, H., Smith, A. W. B., Xu, L., Lovell, B. C., & Yang, D. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.10.017 · Cited by 79
Adapted Anisotropic Gaussian SIFT Matching Strategy for SAR Registration
Wang, F., You, H., & Fu, X. (2014) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2330593 · Cited by 79
Real-time video stabilization without phantom movements for micro aerial vehicles
Aguilar, W. G., & Ángulo, C. (2014) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2014-46 · Cited by 78
Hand-Dorsa Vein Recognition by Matching Local Features of Multisource Keypoints
Huang, D., Tang, Y., Wang, Y., Chen, L., & Wang, Y. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2360894 · Cited by 78
Lin, B., Sun, Y., Qian, X., Goldgof, D., Gitlin, R., & You, Y. (2016) · The international journal of medical robotics + computer assisted surgery : MRCAS · doi:10.1002/rcs.1661 · Cited by 87
Near-duplicate keyframe retrieval with visual keywords and semantic context
Wu, X. Y., Zhao, W., & Ngo, C. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282309 · Cited by 78
Evaluation of low-complexity visual feature detectors and descriptors
Canclini, A., Cesana, M., Redondi, A. E. C., Tagliasacchi, M., Ascenso, J., & Cilla, R. (2013) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2013.6622757 · Cited by 77
Applying Color Names to Image Description
van de Weijer, J., & Schmid, C. (2007) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2007.4379354 · Cited by 76
Self-citationD2-Net: A Trainable CNN for Joint Detection and Description of Local FeaturesPreprint
Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Šivic, J., Torii, A., & Sattler, T. (2019) · arXiv (Cornell University) · Cited by 76
POP: Privacy-Preserving Outsourced Photo Sharing and Searching for Mobile Devices
Zhang, L., Jung, T., Liu, C., Ding, X. X., Li, X., & Liu, Y. (2015) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2015.39 · Cited by 75
SIFER: Scale-Invariant Feature Detector with Error Resilience
Mainali, P., Lafruit, G., Yang, Q., Geelen, B., Gool, L. V., & Lauwereins, R. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0622-3 · Cited by 75
Towards Codebook-Free: Scalable Cascaded Hashing for Mobile Image Search
Zhou, W., Yang, M., Li, H., Wang, X., Lin, Y., & Tian, Q. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2301979 · Cited by 75
Local Features and Visual Words Emerge in Activations
Siméoni, O., Avrithis, Y., & Chum, O. (2019) · 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.2019.01192 · Cited by 75
Tsai, S. S., Chen, D. M., Chandrasekhar, V., Gabriel Takacs, A., Cheung, N. M., Vedantham, R., Grzeszczuk, R., & Girod, B. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874293 · Cited by 75
Generalized Fourier Descriptors with Applications to Objects Recognition in SVM Context
Smach, F., Lemaître, C., Gauthier, J.-P., Mitéran, J., & Atri, M. (2007) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-007-0036-3 · Cited by 75
Learning to Detect Features in Texture Images
Zhang, L., & Rusinkiewicz, S. M. (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.2018.00662 · Cited by 75
BOLD - Binary online learned descriptor for efficient image matching
Balntas, V., Tang, L., & Mikolajczyk, K. (2015) · 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.2015.7298850 · Cited by 74
Self-citationMultimodal object categorization by a robot
NAKAMURA, T., Nagai, T., & Iwahashi, N. (2007) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2007.4399634 · Cited by 74
Positive Semidefinite Metric Learning Using Boosting-like AlgorithmsPreprint
Shen, C., Kim, J., Wang, L., & van den Hengel, A. (2011) · arXiv (Cornell University) · doi:10.5555/2188385.2343679 · Cited by 74
Locality in Generic Instance Search from One Example
Ran, T., Gavves, E., Snoek, C. G. M., & Smeulders, A. W. M. (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.2014.269 · Cited by 73
Local histogram based geometric invariant image watermarking
Deng, C. D., Gao, X., Li, X., & Tao, D. (2010) · Signal Processing · doi:10.1016/j.sigpro.2010.05.032 · Cited by 73
A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification
Zhao, L., Tang, P. T. P., & Huo, L. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.890762 · Cited by 73
Fast copy-move forgery detection using local bidirectional coherency error refinement
Bi, X., & Pun, C. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.028 · Cited by 73
Interest Point Detector and Feature Descriptor Survey
Krig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_6 · Cited by 74
Real-Time Model-Based Video Stabilization for Microaerial Vehicles
Aguilar, W. G., & Ángulo, C. (2015) · Neural Processing Letters · doi:10.1007/s11063-015-9439-0 · Cited by 72
Brain state decoding for rapid image retrieval
Wang, J., Pohlmeyer, E. A., Hanna, B., Jiang, Y., Sajda, P., & Chang, S. (2009) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1631272.1631463 · Cited by 72
Improving Harris corner selection strategy
Bellavia, F., Tegolo, D., & Valenti, C. (2011) · IET Computer Vision · doi:10.1049/iet-cvi.2009.0127 · Cited by 72
Rate-efficient visual correspondences using random projections
Yeo, C., Ahammad, P., & Ramchandran, K. (2008) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2008.4711730 · Cited by 72
Iris Recognition Development Techniques: A Comprehensive Review
Malgheet, J. R., Manshor, N. B., & Affendey, L. S. (2021) · Complexity · doi:10.1155/2021/6641247 · Cited by 71
Learning non-redundant codebooks for classifying complex objects
Zhang, W., Surve, A. S., Fern, X. Z., & Dietterich, T. G. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553533 · Cited by 71
Video Mining with Frequent Itemset Configurations
Quack, T., Ferrari, V., & Gool, L. V. (2006) · Lecture notes in computer science · doi:10.1007/11788034_37 · Cited by 71
Image Retagging Using Collaborative Tag Propagation
Liu, D., Yan, S., Hua, X., & Zhang, H.-J. (2011) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2134078 · Cited by 72
Detectors and Descriptors for Photogrammetric Applications
Fabio Remondino, A. (2006) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000158063 · Cited by 71
Small Blob Identification in Medical Images Using Regional Features From Optimum Scale
Zhang, M., Wu, T., & Bennett, K. M. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2360154 · Cited by 70
Calibrating Distributed Camera Networks
Devarajan, D., Cheng, Z., & Radke, R. J. (2008) · Proceedings of the IEEE · doi:10.1109/jproc.2008.928759 · Cited by 70
Robust Affine Invariant Feature Extraction for Image Matching
Cheng, L., Gong, J., Yang, X., Fan, C., & Han, P. (2008) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2008.915599 · Cited by 70
The structure-from-motion reconstruction pipeline – a survey with focus on short image sequences
Häming, K., & Peters, G. (2010) · Czech digital mathematics library · Cited by 70
Super Fast Event Recognition in Internet Videos
Jiang, Y., Dai, Q., Mei, T., Rui, Y., & Chang, S. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2436813 · Cited by 69
Enhanced Biologically Inspired Model for Object Recognition
Huang, Y., Huang, K., Tao, D., Tan, T., & Li, X. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2158418 · Cited by 70
Morel, J., & Yu, G. (2011) · Inverse Problems and Imaging · doi:10.3934/ipi.2011.5.115 · Cited by 69
Decoupling Makes Weakly Supervised Local Feature Better
Li, K., Wang, L., Liu, L., Ran, Q., Xu, K., & Guo, Y. (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/cvpr52688.2022.01538 · Cited by 69
Dennis, J. W., Tran, H. D., & Chng, E. S. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.02.015 · Cited by 69
Exploiting affine invariant regions and leaf edge shapes for weed detection
Kazmi, W., García-Ruíz, F., Nielsen, J., Rasmussen, J., & Andersen, H. J. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.08.023 · Cited by 69
Chang, S., He, J., Jiang, Y., Khoury, E., Ngo, C., Yanagawa, A., & Zavesky, E. (2008) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 69
Detection and matching of rectilinear structures
Mičušík, B., Wildenauer, H., & Košecká, J. (2008) · 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.2008.4587488 · Cited by 69
Improving the SIFT descriptor with smooth derivative filters
Moreno, P., Bernardino, A. J. M., & Santos-Victor, J. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.08.012 · Cited by 68
Frame Fusion for Video Copy Detection
Wei, S., Zhao, Y., Zhu, C., Xu, C., & Zhu, Z. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2105554 · Cited by 68
Illumination-Robust remote sensing image matching based on oriented self-similarity
Sedaghat, A., & Mohammadi, N. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.018 · Cited by 67
S2DNet: Learning Image Features for Accurate Sparse-to-Dense Matching
Germain, H., Bourmaud, G., & Lepetit, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_37 · Cited by 67
DietCam: Regular Shape Food Recognition with a Camera Phone
Kong, F., & Tan, J. (2011) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2011.19 · Cited by 69
Improving Large-Scale Image Retrieval Through Robust Aggregation of Local Descriptors
Husain, S. S., & Bober, M. Z. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2613873 · Cited by 67
Neven, H., Rose, G., & Macready, W. G. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0804.4457 · Cited by 67
Content-based image retrieval using visually significant point features
Banerjee, M., Kundu, M. K., & Maji, P. (2009) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2009.02.024 · Cited by 66
Jain, M., Jeǵou, H., & Gros, P. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072035 · Cited by 66
Geometric Latent Dirichlet Allocation on a Matching Graph for Large-scale Image Datasets
Philbin, J. F., Šivic, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0363-5 · Cited by 69
Stanciu, S. G., Xu, S., Peng, Q., Yan, J., Stanciu, G. A., Welsch, R. E., So, P. T. C., Csúcs, G., & Yu, H. (2014) · Scientific Reports · doi:10.1038/srep04636 · Cited by 66
Fan, W., & Bouguila, N. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.03.026 · Cited by 66
Conformal Prediction with Neural Networks
Papadopoulos, H., Vovk, V., & Gammerman, A. (2007) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2007.47 · Cited by 66
Affinity learning on a tensor product graph with applications to shape and image retrieval
Yang, X., & Latecki, L. J. (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.2011.5995325 · Cited by 66
Depth from Familiar Objects: A Hierarchical Model for 3D Scenes
Sudderth, E. B., Torralba, A. B., Freeman, W. T., & Willsky, A. S. (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.2006.97 · Cited by 66
Logo Spotting by a Bag-of-words Approach for Document Categorization
Rusiñol, M., & Lladós, J. (2009) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2009.103 · Cited by 65
Shoeprint Image Retrieval Based on Local Image Features
Su, D. C. H., Bouridane, A., & Gueham, M. (2007) · International Conference on Information Assurance and Security (IAS) · doi:10.1109/ias.2007.18 · Cited by 65
Toward a Robust Analysis of Dermoscopy Images Acquired under Different Conditions
Celebi, M. E., Mendonca, T., & Marques, J. S. (2015) · CRC Press eBooks · doi:10.1201/b19107-5 · Cited by 65
LDFT-Based Watermarking Resilient to Local Desynchronization Attacks
Tian, H., Zhao, Y., Ni, R., Qin, L., & Li, X. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2245415 · Cited by 65
Zhang, W., & Shui, P. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.021 · Cited by 65
Wu, B., Zhang, Y., & Zhu, Q. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.12.005 · Cited by 65
A mapping and localization framework for scalable appearance-based navigation
Šegvić, S., Remazeilles, A., Diosi, A., & Chaumette, F. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2008.08.005 · Cited by 65
Randomized structure from motion based on atomic 3D models from camera triplets
Havlena, M., Torii, A., Knopp, J., & Pajdla, T. (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.2009.5206677 · Cited by 65
Evaluation of local features for person re-identification in image sequences
Bäuml, M., & Stiefelhagen, R. (2011) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2011.6027339 · Cited by 64
Oriented diffusion filtering for enhancing low-quality fingerprint images
Gottschlich, C., & Schönlieb, C. (2012) · IET Biometrics · doi:10.1049/iet-bmt.2012.0003 · Cited by 64
Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval
Feng, L., & Bhanu, B. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2469281 · Cited by 64
Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review
Jing, J., Gao, T. S., Zhang, W., Gao, Y., & Sun, C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3201185 · Cited by 66
Covariance-Based Descriptors for Efficient 3D Shape Matching, Retrieval, and Classification
Tabia, H., & Laga, H. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2457676 · Cited by 64
Improved Video Registration using Non-Distinctive Local Image Features
Heß, R., & Fern, A. (2007) · 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.2007.382989 · Cited by 64
Learning the Compositional Nature of Visual Object Categories for Recognition
Ommer, B., & Buhmann, J. M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.22 · Cited by 64
Boundary preserving dense local regions
Kim, J., & Grauman, K. L. (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.2011.5995526 · Cited by 63
Scalable Large-Margin Mahalanobis Distance Metric Learning
Shen, C., Kim, J., & Wang, L. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2052630 · Cited by 63
Image registration based on corner detection and affine transformation
Lin, H., Du, P., Zhao, W., Zhang, L., & Sun, H. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5647722 · Cited by 63
Visual detection of knives in security applications using Active Appearance Models
Głowacz, A., Kmieć, M., & Dziech, A. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1537-2 · Cited by 63
Zhai, K., Jiang, B., & Chan, W. K. (2012) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2012.40 · Cited by 63
Metadata
- Authors
- Mikolajczyk, K., & Schmid, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- International Journal of Computer Vision
- Published
- 2004-10
- Volume
- 60
- Issue
- 1
- Pages
- 63-86
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2172188317
- Cited by
- 3,436
- References
- 45
- Citations by year
- 2026: 17 · 2025: 35 · 2024: 47 · 2023: 64 · 2022: 66 · 2021: 94 · 2020: 93 · 2019: 124 · 2018: 103 · 2017: 160 · 2016: 186 · 2015: 277 · 2014: 251 · 2013: 279 · 2012: 270
- FWCI
- 116.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- HAL (submittedVersion)
- 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 › Image and Object Detection Techniques
- Keywords
- keypoint detection, scale invariance, affine invariance, Harris corner detector, structure tensor, multi-scale representation, image matching
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
