暂无分享,去创建一个
Enric Meinhardt | Gloria Haro | Coloma Ballester | Roberto P. Palomares | C. Ballester | G. Haro | E. Meinhardt
[1] Daniel Cremers,et al. Large displacement optical flow computation withoutwarping , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[2] Long Quan,et al. Match Propagation for Image-Based Modeling and Rendering , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[3] Ramin Zabih,et al. Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.
[4] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[5] Luc Van Gool,et al. Sparse Flow: Sparse Matching for Small to Large Displacement Optical Flow , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.
[6] Stefano Soatto,et al. Sparse Occlusion Detection with Optical Flow , 2012, International Journal of Computer Vision.
[7] Thomas Brox,et al. High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.
[8] Michael J. Black,et al. A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them , 2013, International Journal of Computer Vision.
[9] In-So Kweon,et al. Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..
[10] Cristian Sminchisescu,et al. Efficient Closed-Form Solution to Generalized Boundary Detection , 2012, ECCV.
[11] KweonIn So,et al. Adaptive Support-Weight Approach for Correspondence Search , 2006 .
[12] Michael J. Black,et al. A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.
[13] Yasuyuki Matsushita,et al. Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[14] Rachid Deriche,et al. Symmetrical Dense Optical Flow Estimation with Occlusions Detection , 2002, ECCV.
[15] Horst Bischof,et al. Motion estimation with non-local total variation regularization , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[16] Gloria Haro,et al. A Rotation-Invariant Regularization Term for Optical Flow Related Problems , 2014, ACCV.
[17] C. Lawrence Zitnick,et al. Structured Forests for Fast Edge Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[18] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[19] Michael J. Black,et al. Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[20] Michael J. Black,et al. The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..
[21] Yasuyuki Matsushita,et al. Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[22] Christian Heipke,et al. Discrete Optimization for Optical Flow , 2015, GCPR.
[23] S. Osher,et al. A new median formula with applications to PDE based denoising , 2009 .
[24] Daniel Cremers,et al. Convex Relaxation of Vectorial Problems with Coupled Regularization , 2014, SIAM J. Imaging Sci..
[25] Rudolf Mester,et al. Illumination-Robust Dense Optical Flow Using Census Signatures , 2011, DAGM-Symposium.
[26] Vladlen Koltun,et al. Efficient Nonlocal Regularization for Optical Flow , 2012, ECCV.
[27] Jitendra Malik,et al. Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Daniel Cremers,et al. Advanced Data Terms for Variational Optic Flow Estimation , 2009, VMV.
[29] R. Tibshirani,et al. PATHWISE COORDINATE OPTIMIZATION , 2007, 0708.1485.
[30] Hans-Peter Seidel,et al. Complementary Optic Flow , 2009, EMMCVPR.
[31] Cristian Sminchisescu,et al. Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation , 2013, 2013 IEEE International Conference on Computer Vision.
[32] Daniel Cremers,et al. Anisotropic Huber-L1 Optical Flow , 2009, BMVC.
[33] Yurii Nesterov,et al. Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems , 2012, SIAM J. Optim..
[34] Camillo J. Taylor,et al. Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames , 2015, EMMCVPR.
[35] Horst Bischof,et al. A Duality Based Approach for Realtime TV-L1 Optical Flow , 2007, DAGM-Symposium.
[36] Richard Szeliski,et al. A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[37] Hailin Jin,et al. Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow , 2014, CVPR.
[38] Jiaolong Yang,et al. Dense, accurate optical flow estimation with piecewise parametric model , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Cordelia Schmid,et al. EpicFlow: Edge-preserving interpolation of correspondences for optical flow , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Fridtjof Stein,et al. Efficient Computation of Optical Flow Using the Census Transform , 2004, DAGM-Symposium.
[41] Joachim Weickert,et al. Why Is the Census Transform Good for Robust Optic Flow Computation? , 2013, SSVM.
[42] Ives Rey-Otero,et al. Anatomy of the SIFT Method , 2014, Image Process. Line.
[43] Guy Gilboa,et al. Nonlocal Operators with Applications to Image Processing , 2008, Multiscale Model. Simul..
[44] Juho Kannala,et al. Quasi-Dense Wide Baseline Matching Using Match Propagation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[45] M. Nikolova. An Algorithm for Total Variation Minimization and Applications , 2004 .
[46] Patrick Bouthemy,et al. Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow , 2014, Comput. Vis. Image Underst..
[47] Antoni Buades,et al. Reliable multi-scale and multi-window stereo matching , 2015 .
[48] Hans-Hellmut Nagel,et al. An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] Antonin Chambolle,et al. A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.
[50] Didier Stricker,et al. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[51] Cordelia Schmid,et al. DeepFlow: Large Displacement Optical Flow with Deep Matching , 2013, 2013 IEEE International Conference on Computer Vision.
[52] Ying Wu,et al. Large Displacement Optical Flow from Nearest Neighbor Fields , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[53] Joachim Weickert,et al. Reliable Estimation of Dense Optical Flow Fields with Large Displacements , 2000, International Journal of Computer Vision.
[54] Berthold K. P. Horn,et al. Determining Optical Flow , 1981, Other Conferences.
[55] Konrad Schindler,et al. An Evaluation of Data Costs for Optical Flow , 2013, GCPR.
[56] Agustín Salgado de la Nuez,et al. Preserving accurate motion contours with reliable parameter selection , 2014, ICIP.
[57] Joachim Weickert,et al. Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Optic Flow in Harmony Optic Flow in Harmony Optic Flow in Harmony , 2022 .
[58] Thomas Brox,et al. Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Highly Accurate Optic Flow Computation with Theoretically Justified Warping Highly Accurate Optic Flow Computation with Theoretically Justified Warping , 2022 .
[59] Vanel A. Lazcano,et al. A TV-L1 Optical Flow Method with Occlusion Detection , 2012, DAGM/OAGM Symposium.
[60] Thomas Pock,et al. Non-local Total Generalized Variation for Optical Flow Estimation , 2014, ECCV.