The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
Abstract
Abstract The wide-baseline stereo problem, i.e. the problem of establishing correspondences between a pair of images taken from different viewpoints is studied. A new set of image elements that are put into correspondence, the so called extremal regions , is introduced. Extremal regions possess highly desirable properties: the set is closed under (1) continuous (and thus projective) transformation of image coordinates and (2) monotonic transformation of image intensities. An efficient (near linear complexity) and practically fast detection algorithm (near frame rate) is presented for an affinely invariant stable subset of extremal regions, the maximally stable extremal regions (MSER). A new robust similarity measure for establishing tentative correspondences is proposed. The robustness ensures that invariants from multiple measurement regions (regions obtained by invariant constructions from extremal regions), some that are significantly larger (and hence discriminative) than the MSERs, may be used to establish tentative correspondences. The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes. Significant change of scale (3.5×), illumination conditions, out-of-plane rotation, occlusion, locally anisotropic scale change and 3D translation of the viewpoint are all present in the test problems. Good estimates of epipolar geometry (average distance from corresponding points to the epipolar line below 0.09 of the inter-pixel distance) are obtained.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
Watersheds in digital spaces: an efficient algorithm based on immersion simulations
Vincent, L. M., & Soille, P. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.87344 · Cited by 5,663
Local 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
Indexing based on scale invariant interest points
Mikolajczyk, K., & Schmid, C. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937561 · Cited by 1,101
In defence of the 8-point algorithm
Hartley, R. I. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466816 · Cited by 648
Reliable feature matching across widely separated views
Baumberg, A. (2002) · 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.2000.855899 · Cited by 629
Pritchett, P., & Zisserman, A. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1998.710802 · Cited by 279
Hummel, J. E. (1998) · Cited by 198
Viewpoint invariant texture matching and wide baseline stereo
Schaffalitzky, F., & Zisserman, A. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937686 · Cited by 193
Flagged first, then by citations
Cited by
Sorted by citations
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Bag-of-visual-words and spatial extensions for land-use classification
Yang, Y., & Newsam, S. D. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869829 · Cited by 3,183
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
Self-citationVedaldi, A., & Fulkerson, B. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874249 · Cited by 2,691
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901
LIFT: Learned Invariant Feature Transform
Yi, K. M., Trulls, E., Lepetit, V., & Fua, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_28 · Cited by 1,306
Local Invariant Feature Detectors: A Survey
Tuytelaars, T., & Mikolajczyk, K. (2007) · now publishers, Inc. eBooks · doi:10.1561/0600000017 · Cited by 1,278
Reading text in the wild with convolutional neural networks
Jaderberg, M., Simonyan, K., Vedaldi, A., & Zisserman, A. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0823-z · Cited by 1,191
A sparse texture representation using local affine regions
Lazebnik, S., Schmid, C., & Ponce, J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.151 · Cited by 1,138
Image‐based 3D Modelling: A Review
Fabio Remondino, A., & El-Hakim, S. F. (2006) · The Photogrammetric Record · doi:10.1111/j.1477-9730.2006.00383.x · Cited by 1,045
Robust Object Detection with Interleaved Categorization and Segmentation
Leibe, B., Leonardis, A., & Schiele, B. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0095-3 · Cited by 947
Visual simultaneous localization and mapping: a survey
Fuentes-Pacheco, J., Ruiz-Ascencio, J., & Rendón-Mancha, J. M. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9365-8 · Cited by 883
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140381 · Cited by 879
Chum, O., Matas, J., & Kittler, J. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45243-0_31 · Cited by 866
Self-citationDiscriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Dosovitskiy, A., Fischer, P., Springenberg, J. T., Riedmiller, M., & Brox, T. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2496141 · Cited by 762
Improving Bag-of-Features for Large Scale Image Search
Jeǵou, H., Douze, M., & Schmid, C. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0285-2 · Cited by 736
Keypoint recognition using randomized trees
Lepetit, V., & Fua, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.188 · Cited by 694
Deep Features for Text Spotting
Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10593-2_34 · Cited by 653
Robust Text Detection in Natural Scene Images
Yin, X.-C., Yin, X., Huang, K., & Hao, H. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.182 · Cited by 635
Efficient Visual Search of Videos Cast as Text Retrieval
Sivic, J., & Zisserman, A. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.111 · Cited by 499
Reweighted Random Walks for Graph Matching
Cho, M., Lee, J., & Lee, K. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15555-0_36 · Cited by 477
Near Duplicate Image Detection: min-Hash and tf-idf Weighting
Chum, O., Philbin, J. F., & Zisserman, A. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.50 · Cited by 467
Self-citationA quasi-dense approach to surface reconstruction from uncalibrated images
Lhuillier, M., & Quan, L. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.44 · Cited by 432
An Affine Invariant Salient Region Detector
Kadir, T., Zisserman, A., & Brady, M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24670-1_18 · Cited by 423
Evaluation of Features Detectors and Descriptors based on 3D Objects
Moreels, P., & Perona, P. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-9967-1 · Cited by 420
Rothganger, F., Lazebnik, S., Schmid, C., & Ponce, J. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-005-3674-1 · Cited by 413
Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking
Gauglitz, S., Höllerer, T., & Turk, M. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0431-5 · Cited by 469
Evaluation of GIST descriptors for web-scale image search
Douze, M., Jeǵou, H., Sandhawalia, H., Amsaleg, L., & Schmid, C. (2009) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1646396.1646421 · Cited by 394
Negative Evidences and Co-occurences in Image Retrieval: The Benefit of PCA and Whitening
Jeǵou, H., & Chum, O. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_55 · Cited by 391
Self-citationRobust Scene Text Detection with Convolution Neural Network Induced MSER Trees
Huang, W., Qiao, Y., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_33 · Cited by 384
Image Based Localization in Urban Environments
Zhang, W., & Košecká, J. (2006) · International Symposium on 3D Data Processing, Visualization and Transmission · doi:10.1109/3dpvt.2006.80 · Cited by 377
Adaptive Road Crack Detection System by Pavement Classification
Gavilán, M., Balcones, D., Marcos, O., Llorca, D. F., Sotelo, M. Á., Parra, I., Ocaña, M., Aliseda, P., Yarza, P., & Amírola, A. (2011) · Sensors · doi:10.3390/s111009628 · Cited by 370
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
Riba, E., Mishkin, D., Ponsa, D., Rublee, E., & Bradski, G. R. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093363 · Cited by 383
50 Years of object recognition: Directions forward
Andreopoulos, A., & Tsotsos, J. K. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.04.005 · Cited by 347
Sikuli: using GUI screenshots for search and automation
Yeh, T., Chang, T.-H., & Miller, R. C. (2009) · Proceedings of the 22nd annual ACM symposium on User interface software and technology · doi:10.1145/1622176.1622213 · Cited by 346
A Unified Framework for Multioriented Text Detection and Recognition
Yao, C., Bai, X., & Liu, W. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2353813 · Cited by 339
Kernelized Locality-Sensitive Hashing
Kulis, B., & Grauman, K. L. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.219 · Cited by 331
Multi-view traffic sign detection, recognition, and 3D localisation
Timofte, R., Zimmermann, K., & Gool, L. V. (2011) · Machine Vision and Applications · doi:10.1007/s00138-011-0391-3 · Cited by 317
Linear Time Maximally Stable Extremal Regions
Nistér, D., & Stewénius, H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_14 · Cited by 309
Universal Correspondence NetworkPreprint
Choy, C., Gwak, J., Savarese, S., & Chandraker, M. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03558 · Cited by 300
Performance evaluation of local colour invariants
Burghouts, G. J., & Geusebroek, J. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2008.07.003 · Cited by 297
MSLD: A robust descriptor for line matching
Wang, Z., Wu, F., & Hu, Z. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.08.035 · Cited by 296
Symmetry-driven accumulation of local features for human characterization and re-identification
Bazzani, L., Cristani, M., & Murino, V. (2012) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.10.008 · Cited by 295
Viewpoint Invariant Texture Description Using Fractal Analysis
Xu, Y., Ji, H., & Fermuller, C. M. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0220-6 · Cited by 302
Girod, B., Chandrasekhar, V., Chen, D. M., Cheung, N. M., Grzeszczuk, R., Reznik, Y. A., Gabriel Takacs, A., Tsai, S. S., & Vedantham, R. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.940881 · Cited by 290
On the use of depth camera for 3D phenotyping of entire plants
Chéné, Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P., Belin, É., & Chapeau‐Blondeau, F. (2012) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2011.12.007 · Cited by 290
A comprehensive review of current local features for computer vision
Li, J., & Allinson, N. M. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.11.032 · Cited by 282
Deseilligny, M. P., & Cléry, I. (2012) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xxxviii-5-w16-269-2011 · Cited by 269
World-scale mining of objects and events from community photo collections
Quack, T., Leibe, B., & Gool, L. V. (2008) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1386352.1386363 · Cited by 259
Fast Similarity Search for Learned Metrics
Kulis, B., Jain, P., & Grauman, K. L. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.151 · Cited by 258
Learning Shape-Aware Embedding for Scene Text Detection
Tian, Z., Shu, M., Lyu, P., Li, R., Zhou, C., Shen, X., & Jia, J. (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.2019.00436 · Cited by 251
Lu, Z., Carneiro, G., & Bradley, A. P. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2389619 · Cited by 247
Moment invariants for recognition under changing viewpoint and illumination
Mindru, F., Tuytelaars, T., Gool, L. V., & Moons, T. (2003) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2003.10.011 · Cited by 246
Graph-Based Compression of Dynamic 3D Point Cloud Sequences
Thanou, D., Chou, P. A., & Pascal Frossard, B. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2529506 · Cited by 244
Action Recognition in Realistic Sports Videos
Soomro, K., & Zamir, A. (2014) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-09396-3_9 · Cited by 243
Spatial coding for large scale partial-duplicate web image search
Zhou, W., Lu, Y., Li, H., Song, Y., & Tian, Q. C. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874019 · Cited by 243
Real-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks
Silva, S. M., & Jung, C. R. (2017) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2017.14 · Cited by 241
Towards Multi-View Object Class Detection
Thomas, A., Ferrar, V., Leibe, B., Tuytelaars, T., Schiel, B., & Gool, L. V. (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.2006.311 · Cited by 228
Image Features Detection, Description and Matching
Hassaballah, M., Abdelmgeid, A., & Alshazly, H. A. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-28854-3_2 · Cited by 225
Learning to Track and Identify Players from Broadcast Sports Videos
Lu, W.-L., Ting, J. A. S., Little, J. J., & Murphy, K. P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.242 · Cited by 224
Semi-Local Affine Parts for Object Recognition
Lazebnik, S., Schmid, C., & Ponce, J. (2004) · British Machine Vision Conference (BMVC) · doi:10.5244/c.18.98 · Cited by 223
Detecting Loop Closure with Scene Sequences
Ho, K. L., & Newman, P. M. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0020-1 · Cited by 211
Video Google: Efficient Visual Search of Videos
Šivic, J., & Zisserman, A. (2006) · Lecture notes in computer science · doi:10.1007/11957959_7 · Cited by 209
Grabner, M., Grabner, H., & Bischof, H. (2006) · Lecture Notes in Computer Science · doi:10.1007/11612032_92 · Cited by 208
Image Matching Using Generalized Scale-Space Interest Points
Lindeberg, T. (2013) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-014-0541-0 · Cited by 222
Fernández-Hernandez, J., González‐Aguilera, D., Rodríguez‐Gonzálvez, P., & Mancera-Taboada, J. (2014) · Archaeometry · doi:10.1111/arcm.12078 · Cited by 207
Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition
Larsson, F., & Felsberg, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21227-7_23 · Cited by 204
Shape Descriptors for Maximally Stable Extremal Regions
Forssén, P.-E., & Lowe, D. G. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409025 · Cited by 202
Topological mapping, localization and navigation using image collections
Fraundorfer, F., Engels, C., & Nistér, D. (2007) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2007.4399123 · Cited by 201
Shape recognition with edge-based features
Mikolajczyk, K., Zisserman, A., & Schmid, C. (2003) · British Machine Vision Conference (BMVC) · doi:10.5244/c.17.79 · Cited by 197
Omnidirectional Vision Based Topological Navigation
Goedemé, T., Nuttin, M., Tuytelaars, T., & Gool, L. V. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0025-9 · Cited by 197
Text Detection, Tracking and Recognition in Video: A Comprehensive Survey
Yin, X.-C., Zuo, Z.-Y., Tian, S., & Liu, C. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2554321 · Cited by 197
Kniaz, V. V., Knyaz, V. A., Hladůvka, J., Kropatsch, W. G., & Mizginov, V. A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11024-6_46 · Cited by 191
Discovering Texture Regularity as a Higher-Order Correspondence Problem
Hays, J. H., Leordeanu, M., Efros, A. A., & Liu, Y. (2006) · Lecture notes in computer science · doi:10.1007/11744047_40 · Cited by 184
Hyper-graph matching via reweighted random walks
Lee, J., Cho, M., & Lee, K. M. (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.2011.5995387 · Cited by 183
Domain-size pooling in local descriptors: DSP-SIFT
Dong, J., & Soatto, S. (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.2015.7299145 · Cited by 187
Geotagging in multimedia and computer vision—a survey
Luo, J., Joshi, D., Yu, J., & Gallagher, A. (2010) · Multimedia Tools and Applications · doi:10.1007/s11042-010-0623-y · Cited by 182
Efficient View-Based SLAM Using Visual Loop Closures
Mahon, I., Williams, S. B., Pizarro, O. R., & Johnson‐Roberson, M. (2008) · IEEE Transactions on Robotics · doi:10.1109/tro.2008.2004888 · Cited by 182
A comparative evaluation of interest point detectors and local descriptors for visual SLAM
Gil, A., Mozos, Ó. M., Ballesta, M., & Reinoso, Ó. (2009) · Machine Vision and Applications · doi:10.1007/s00138-009-0195-x · Cited by 178
Vision-based topological mapping and localization methods: A survey
Garcia-Fidalgo, E., & Ortiz, A. (2015) · Robotics Auton. Syst. · doi:10.1016/j.robot.2014.11.009 · Cited by 185
3D model matching with Viewpoint-Invariant Patches (VIP)
Wu, C., Clipp, B., Li, X., Frahm, J., & Pollefeys, M. (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.2008.4587501 · Cited by 176
Scale Invariant Feature Transform on the Sphere: Theory and Applications
Cruz, J., Bogdanova, I., Paquier, B., Bierlaire, M., & Thiran, J. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0505-4 · Cited by 176
Feature-Based Sequence-to-Sequence Matching
Caspi, Y., Simakov, D., & Irani, M. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-005-4842-z · Cited by 173
High-level event recognition in unconstrained videos
Jiang, Y., Bhattacharya, S., Chang, S., & Shah, M. A. (2012) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-012-0024-2 · Cited by 174
Rotationally Invariant Descriptors Using Intensity Order Pooling
Fan, B., Wu, F., & Hu, Z. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.277 · Cited by 170
Structure from motion with wide circular field of view cameras
Micusik, B., & Pajdla, T. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.151 · Cited by 170
Self-citationCurious George: An attentive semantic robot
Meger, D., Forssén, P.-E., Lai, K., Helmer, S., McCann, S., Southey, T., Baumann, M., Little, J. J., & Lowe, D. G. (2008) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.03.008 · Cited by 166
Real-time license plate detection and recognition using deep convolutional neural networks
Silva, S. M., & Jung, C. R. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102773 · Cited by 166
SIFT and SURF Performance Evaluation against Various Image Deformations on Benchmark Dataset
Khan, N. Y., Brendan Mccane, A., & Wyvill, G. (2011) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2011.90 · Cited by 165
Keyframe-based monocular SLAM: design, survey, and future directions
Younes, G., Asmar, D. C., Shammas, E. A., & Zelek, J. (2016) · Robotics Auton. Syst. · doi:10.1016/j.robot.2017.09.010 · Cited by 168
Describing Visual Scenes Using Transformed Objects and Parts
Sudderth, E. B., Torralba, A. B., Freeman, W. T., & Willsky, A. S. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0069-5 · Cited by 165
MODS: Fast and robust method for two-view matching
Mishkin, D., Matas, J., & Perďoch, M. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.08.005 · Cited by 159
Self-citationSoft-Tissue Motion Tracking and Structure Estimation for Robotic Assisted MIS Procedures
Stoyanov, D. V., Mylonas, G. P., Deligianni, F., Darzi, A. W., & Yang, G. Z. (2005) · Lecture notes in computer science · doi:10.1007/11566489_18 · Cited by 153
Extinction Profiles for the Classification of Remote Sensing Data
Ghamisi, P., de Souza, R. M., Benediktsson, J. A., Zhu, X. X., Rittner, L., & Lotufo, R. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2561842 · Cited by 150
Local velocity-adapted motion events for spatio-temporal recognition
Laptev, I., Barbara Caputo, B., Schüldt, C., & Lindeberg, T. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.11.023 · Cited by 148
A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles
Jiao, Z., Jia, G., & Cai, Y. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.11.008 · Cited by 149
Metadata
- Authors
- Matas, J., Chum, O., Urban, M., & Pajdla, T.
- Affiliations
- University of Surrey (GB)
- Venue
- Procedings of the British Machine Vision Conference 2002
- Publisher
- British Machine Vision Association
- Published
- 2002
- Pages
- 36.1-36.10
- OpenAlex
- W2147237076
- Cited by
- 1,350
- References
- 15
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 11 · 2023: 6 · 2022: 17 · 2021: 23 · 2020: 34 · 2019: 56 · 2018: 61 · 2017: 74 · 2016: 118 · 2015: 125 · 2014: 96 · 2013: 89 · 2012: 82
- FWCI
- 8.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- British Machine Vision Conference (BMVC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- 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
- Keywords
- wide-baseline stereo, MSER, feature detection, correspondence estimation, affine invariance, projective invariance, epipolar geometry estimation, scale variation, illumination variation, 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
