Optical flow estimation with uncertainties through dynamic MRFs
Abstract
In this paper, we propose a novel dynamic discrete framework to address image morphing with application to optical flow estimation. We reformulate the problem using a number of discrete displacements, and therefore the estimation of the morphing parameters becomes a tractable matching criteria independent combinatorial problem which is solved through the FastPD algorithm. In order to overcome the main limitation of discrete approaches (low dimensionality of the label space is unable to capture the continuous nature of the expected solution), we introduce a dynamic behavior in the model where the plausible discrete deformations (displacements) are varying in space (across the domain) and time (different states of the process - successive morphing states) according to the local uncertainty of the obtained solution.
Integrity signals
Registry
Cites
Statistics
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?
Dynamic MRF's performance degradation is only apparent on sequences with strong discontinuities (Grove, Urban, and Teddy).
“quantitatively, the performance degradation is only apparent on the sequences with strong discontinuities ( Grove , Urban , and Teddy ).”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Glocker et al. 2008
Only a few algorithms (e.g., DPOF, Fusion, and Dynamic MRF) perform well in this region.
“Only a few algorithms (e.g., DPOF-Lei and Yang 2009, Fusion-Lempitsky et al. 2008, and Dynamic MRF-Glocker et al. 2008) perform well in this region.”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Glocker et al. 2008
The flow fields for Dynamic MRF (Glocker et al. 2008) all appear to be over-smoothed.
“The flow fields for Dynamic MRF (Glocker et al. 2008) all appear to be over-smoothed”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Glocker et al. 2008
Cites
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949 · see citing works
Horn, B. K. P., & Schunck, B. G. (1981) · Artificial Intelligence · doi:10.1016/0004-3702(81)90024-2 · Cited by 10,312 · see citing works
Horn, B. K. P., & Schunck, B. G. (1981) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.965761 · Cited by 7,443 · see citing works
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988 · see citing works
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265 · see citing works
What energy functions can be minimized via graph cuts?
Kolmogorov, V., & Zabih, R. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262177 · Cited by 3,127 · see citing works
High Accuracy Optical Flow Estimation Based on a Theory for Warping
Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24673-2_3 · Cited by 2,634 · see citing works
The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields
Black, M. J., & Anandan, P. (1996) · Computer Vision and Image Understanding · doi:10.1006/cviu.1996.0006 · Cited by 1,571 · see citing works
Efficient Belief Propagation for Early Vision
Felzenszwalb, P. F., & Huttenlocher, D. P. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7899-4 · Cited by 1,338 · see citing works
Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods
Bruhn, A., Weickert, J., & Schnörr, C. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000045324.43199.43 · Cited by 1,160 · see citing works
Convergent Tree-Reweighted Message Passing for Energy Minimization
Kolmogorov, V. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.200 · Cited by 1,100 · see citing works
Computation of component image velocity from local phase information
Fleet, D. J., & Jepson, A. D. (1990) · International Journal of Computer Vision · doi:10.1007/bf00056772 · Cited by 1,085 · see citing works
Lucas/Kanade meets Horn/Schunck : combining local and global optic flow methods
Weickert, J., Bruhn, A., & Schnörr, C. (2003) · doi:10.22028/d291-26234 · Cited by 963 · see citing works
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618 · see citing works
Handbook of Mathematical Models in Computer Vision
(2006) · doi:10.1007/0-387-28831-7 · Cited by 574 · see citing works
Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters
Tappen, & Freeman (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238444 · Cited by 453 · see citing works
The dual-bootstrap iterative closest point algorithm with application to retinal image registration
Stewart, C. V., Tsai, C.-L., & Roysam, B. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819276 · Cited by 347 · see citing works
Multimodal estimation of discontinuous optical flow using Markov random fields
Heitz, F., & Bouthémy, P. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.250841 · Cited by 261 · see citing works
Efficient graph-based energy minimization methods in computer vision
Zabih, R., & Veksler, O. (1999) · Cited by 220 · see citing works
Ill-Posed Problems in Natural Sciences
Tikhonov, A. N. (1992) · De Gruyter eBooks · doi:10.1515/9783112313930 · Cited by 193 · see citing works
Fast, Approximately Optimal Solutions for Single and Dynamic MRFs
Nikos Komodakis, A., Tziritas, G., & Nikos Paragios, 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.2007.383095 · Cited by 181 · see citing works
Self-citationEfficiently solving dynamic Markov random fields using graph cuts
Kohli, P., & Torr, P. H. S. (2005) · doi:10.1109/iccv.2005.81 · Cited by 173 · see citing works
A Framework for Uncertainty and Validation of 3-D Registration Methods Based on Points and Frames
Pennec, X., & Thirion, J. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007976002485 · Cited by 158 · see citing works
Over-Parameterized Variational Optical Flow
Nir, T., Bruckstein, A. M., & Kimmel, R. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0051-2 · Cited by 105 · see citing works
Bayesian multi-scale differential optical flow
Simoncelli, E. P. (1999) · Cited by 69 · see citing works
Rohr, K., Fornefett, M., & Stiehl, H. S. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48714-x_19 · Cited by 59 · see citing works
Glocker, B., Nikos Komodakis, A., Nikos Paragios, A., Tziritas, G., & Navab, N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73273-0_34 · Cited by 55 · see citing works
Self-citationKohli, P., & Torr, P. H. S. (2006) · Lecture notes in computer science · doi:10.1007/11744047_3 · Cited by 38 · see citing works
Taron, M., Nikos Paragios, A., & Jolly, M. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.153 · Cited by 10 · see citing works
Flagged first, then by citations
1 of the 30 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205 · see citing works
“12 F-TV-L1 [79] 8 Second-order prior [75] 14 Fusion [40] 2,666 Dynamic MRF [27] 366 Algorithm Runtime”
BackgroundInfluentialOptical flow modeling and computation: A survey
Fortun, D., Bouthemy, P., & Kervrann, C. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.02.008 · Cited by 457 · see citing works
Dense image registration through MRFs and efficient linear programming
Glocker, B., Nikos Komodakis, A., Tziritas, G., Navab, N., & Nikos Paragios, A. (2008) · Medical Image Analysis · doi:10.1016/j.media.2008.03.006 · Cited by 428 · see citing works
Self-citationNikos Komodakis, A., & Pesquet, J.-C. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2377273 · Cited by 420 · see citing works
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618 · see citing works
Sun, D., Roth, S., Lewis, J. P., & Black, M. J. (2008) · European Conference on Computer Vision · doi:10.1007/978-3-540-88690-7_7 · Cited by 303 · see citing works
Motion Coherent Tracking Using Multi-label MRF Optimization
Tsai, D., Flagg, M., Nakazawa, A., & Rehg, J. M. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-011-0512-5 · Cited by 246 · see citing works
Wang, C., Nikos Komodakis, A., & Nikos Paragios, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.07.004 · Cited by 210 · see citing works
Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods
Glocker, B., Sotiras, A., Nikos Komodakis, A., & Nikos Paragios, A. (2011) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071910-124649 · Cited by 189 · see citing works
Self-citationNikos Komodakis, A., Tziritas, G., & Nikos Paragios, A. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2008.06.007 · Cited by 181 · see citing works
Self-citationFull Flow: Optical Flow Estimation By Global Optimization over Regular Grids
Chen, Q., & Koltun, V. (2016) · 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.2016.509 · Cited by 166 · see citing works
Robust Monocular Epipolar Flow Estimation
Yamaguchi, K., McAllester, D. A., & Urtasun, R. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.243 · Cited by 136 · see citing works
Deformable image registration by combining uncertainty estimates from supervoxel belief propagation
Heinrich, M., Simpson, I. J. A., Papież, B., Brady, M., & Schnabel, J. (2016) · Medical Image Anal. · doi:10.1016/j.media.2015.09.005 · Cited by 72 · see citing works
Optical flow estimation on coarse-to-fine region-trees using discrete optimization
Lei, C., & Yang, Y.-H. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459253 · Cited by 67 · see citing works
Measuring uncertainty in graph cut solutions
Kohli, P., & Torr, P. H. S. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2008.07.002 · Cited by 62 · see citing works
Recurrent Spatial Pyramid CNN for Optical Flow Estimation
Hu, P., Wang, G., & Tan, Y. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2815784 · Cited by 57 · see citing works
ProbFlow: Joint Optical Flow and Uncertainty Estimation
Wannenwetsch, A. S., Keuper, M., & Roth, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.133 · Cited by 55 · see citing works
Constrained Optical Flow Estimation as a Matching Problem
Mozerov, M. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2244221 · Cited by 49 · see citing works
Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs
Parisot, S., Wells, W. M., Chemouny, S., Duffau, H., & Nikos Paragios, A. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.02.006 · Cited by 46 · see citing works
Quantitative error prediction of medical image registration using regression forests
Sokooti, H., Saygılı, G., Glocker, B., Lelieveldt, B. P. F., & Staring, M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.05.005 · Cited by 39 · see citing works
Self-citationTriangleFlow: Optical Flow with Triangulation-Based Higher-Order Likelihoods
Glocker, B., Heibel, H., Navab, N., Kohli, P., & Rother, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15558-1_20 · Cited by 35 · see citing works
Self-citationEstimating medical image registration error and confidence: A taxonomy and scoping review
Bierbrier, J. L., Gueziri, H.-E., & Collins, D. L. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102531 · Cited by 33 · see citing works
Optical flow estimation with adaptive convolution kernel prior on discrete framework
Lee, K. J., Kwon, D., Yun, I., & Lee, S.-U. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539953 · Cited by 32 · see citing works
Sokooti, H., Yousefi, S., Elmahdy, M. S., Lelieveldt, B. P. F., & Staring, M. (2021) · IEEE Access · doi:10.1109/access.2021.3074124 · Cited by 22 · see citing works
Graph Based Spatial Position Mapping of Low-Grade Gliomas
Parisot, S., Duffau, H., Chemouny, S., & Nikos Paragios, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23629-7_62 · Cited by 22 · see citing works
Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty
Sedghi, A., Kapur, T., Luo, J., Mousavi, P., & Wells, W. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32689-0_2 · Cited by 22 · see citing works
Tight convex relaxations for vector-valued labeling problems
Strekalovskiy, E., Goldlüecke, B., & Cremers, D. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126514 · Cited by 21 · see citing works
(Hyper)-graphical models in biomedical image analysis
Nikos Paragios, A., Ferrante, E., Glocker, B., Nikos Komodakis, A., Parisot, S., & Zacharaki, E. I. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.028 · Cited by 20 · see citing works
Self-citationSparse Camera Network for Visual Surveillance -- A Comprehensive Survey
Song, M.-L., Tao, D., & Maybank, S. (2013) · arXiv.org · doi:10.48550/arxiv.1302.0446 · Cited by 33 · see citing works
A Convex Discrete-Continuous Approach for Markov Random Fields
Zach, C., & Kohli, P. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_28 · Cited by 20 · see citing works
Discrete-Continuous Optimization for Optical Flow Estimation
Roth, S., Lempitsky, V., & Rother, C. (2009) · Statistical and Geometrical Approaches to Visual Motion Analysis · doi:10.1007/978-3-642-03061-1_1 · Cited by 27 · see citing works
Schäfer, S., Nylund, K., Sævik, F., Engjom, T., Mézl, M., Jiřík, R., Dimcevski, G., Gilja, O. H., & Tönnies, K. D. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.09.014 · Cited by 18 · see citing works
Folgoc, L. L., Delingette, H., Criminisi, A., & Ayache, N. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.09.008 · Cited by 16 · see citing works
Are Registration Uncertainty and Error Monotonically Associated?
Luo, J., Frisken, S. F., Wang, D., Golby, A. J., Sugiyama, M., & Wells, W. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59716-0_26 · Cited by 16 · see citing works
Tang, L. Y. W., & Hamarneh, G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_6 · Cited by 14 · see citing works
Uncertainty Driven Multi-scale Optimization
Kohli, P., Lempitsky, V., & Rother, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15986-2_25 · Cited by 13 · see citing works
Becker, F., Petra, S., & Schnörr, C. (2015) · Springer eBooks · doi:10.1007/978-1-4939-0790-8_38 · Cited by 12 · see citing works
Minimum Delay Moving Object Detection
Lao, D., & Sundaramoorthi, G. (2017) · 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.2017.511 · Cited by 9 · see citing works
Nikos Komodakis, A., Kumar, M., & Nikos Paragios, A. (2016) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000066 · Cited by 7 · see citing works
Parisot, S., Wells, W. M., Chemouny, S., Duffau, H., & Nikos Paragios, A. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.85 · Cited by 5 · see citing works
Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies
Petersen, J., Arias, A., Selvan, R., Bos, D., van der Lugt, A., Pedersen, J. H., Nielsen, M., & de Bruijne, M. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2890386 · Cited by 5 · see citing works
Polyconvexification of the multi-label optical flow problem
Papadakis, N., Baeza, A., Gargallo, P., & Caselles, V. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5652107 · Cited by 5 · see citing works
Adaptive large window correlation for optical flow estimation with discrete optimization
Lee, K. J., Yun, I., & Lee, S.-U. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2013.06.009 · Cited by 5 · see citing works
Markov Random Fields in Vision Perception: A Survey
Wang, C., & Nikos Paragios, A. (2012) · HAL · Cited by 4 · see citing works
Registration of Temporal Ultrasonic Image Sequences Using Markov Random Fields
Schäfer, S., & Toennies, K. (2012) · International Symposium on Vision, Modeling, and Visualization · doi:10.2312/pe/vmv/vmv12/151-158 · Cited by 3 · see citing works
Wang, Y., & Zhang, H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22180-9_37 · Cited by 3 · see citing works
Nikos Paragios, A., & Nikos Komodakis, A. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.13 · Cited by 3 · see citing works
Optical Flow: Traditional Approaches
Brox, T. (2020) · Computer Vision · doi:10.1007/978-3-030-03243-2_600-1 · Cited by 3 · see citing works
3D motion analysis of keratin filaments in living cells
Herberich, G., Windoffer, R., Leube, R. E., & Aach, T. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.844148 · Cited by 2 · see citing works
A nonparametric regression model for virtual humans generation
Chou, Y.-F., & Shih, Z.-C. (2010) · Multimedia tools and applications · doi:10.1007/s11042-009-0412-7 · Cited by 3 · see citing works
Object Class Recognition (Categorization)
Lazebnik, S. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_337 · Cited by 2 · see citing works
Biomedical image analysis using markov random fields & efficient linear programing
Nikos Komodakis, A., Besbes, A., Glocker, B., & Nikos Paragios, A. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5332535 · Cited by 2 · see citing works
Self-citationImproved Discrete Optical Flow Estimation With Triple Image Matching Cost
Yang, F., Cheng, Y., van de Weijer, J., & Mozerov, M. G. (2020) · IEEE Access · doi:10.1109/access.2020.2968180 · Cited by 2 · see citing works
Improving Motion Estimation Using Image-Driven Functions and Hybrid Scheme
Nguyen, D., & Jeon, J. (2011) · Pacific-Rim Symposium on Image and Video Technology · doi:10.1007/978-3-642-25367-6_7 · Cited by 3 · see citing works
Enhancing accuracy and sharpness of motion field with adaptive scheme and occlusion-aware filter
Nguyen, D., & Jeon, J. (2013) · IET Image Processing · doi:10.1049/iet-ipr.2012.0563 · Cited by 1 · see citing works
Occlusion detecting window matching scheme for optical flow estimation with discrete optimization
Lee, K. J., & Yun, I. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.02.009 · Cited by 1 · see citing works
Becker, F., Petrab, S., & Schnörr, C. (2014) · Springer eBooks · doi:10.1007/978-3-642-27795-5_38-3 · Cited by 1 · see citing works
Time analysis of regional structure of large-scale particle using an interactive visual system
Zhang, Y., Li, G., & Shan, G. (2022) · Visual Informatics · doi:10.1016/j.visinf.2022.03.004 · Cited by 1 · see citing works
Brox, T. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_600 · Cited by 1 · see citing works
Optical Flow: Traditional Approaches
Brox, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-63416-2_600 · Cited by 1 · see citing works
Uncertainty Driven Multi-scale Energy Minimization
Kohli, P., Lempitsky, V., & Rother, C. (2010) · Cited by 1 · see citing works
Measuring deformations and illumination changes in images with applications to face recognition
Jacobs, D. W., & Jorstad, A. (2012) · University Libraries (University of Maryland) · Cited by 1 · see citing works
Graph based semi-supervised learning in computer vision
Huang, N. (2009) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t39g5n2c
Ezaki, S., Shimizu, H., Yamamoto, S., Nakaguchi, T., & Tsumura, N. (2013) · Artificial Life and Robotics · doi:10.1007/s10015-013-0110-4
Dense Optical Flow Estimation using Diffusion Distances
Wartak, S. (2010) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)
A REGION TREE BASED IMAGE DISCRETE LABELING FRAMEWORK
Lei, C., & Yang, H. (2009) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789814273398_0007
Deep learning for registration of high-resolution 3D brain images
Nazib, A. (2020) · Queensland University of Technology · doi:10.5204/thesis.eprints.204293
Efficient and Flexible Sublabel-Accurate Energy Minimization
Nurlanov, Z., Cremers, D., & Bernard, F. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956324
- Authors
- Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., & Navab, N.
- Affiliations
- Technical University of Munich (DE)
- University of Crete (GR)
- Venue
- 2008 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2163621828
- Cited by
- 69
- References
- 30
- Citations by year
- 2022: 3 · 2021: 2 · 2020: 4 · 2019: 3 · 2018: 1 · 2017: 3 · 2016: 4 · 2015: 5 · 2014: 7 · 2013: 9 · 2012: 4
- FWCI
- 6.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- HAL (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- optical flow, image morphing, Markov random fields, combinatorial optimization, uncertainty modeling
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