Algorithmic Differentiation: Application to Variational Problems in Computer Vision
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Horst Bischof | Thomas Pock | Michael Pock | H. Bischof | T. Pock | M. Pock
[1] Michael Hintermüller,et al. An Inexact Newton-CG-Type Active Contour Approach for the Minimization of the Mumford-Shah Functional , 2004, Journal of Mathematical Imaging and Vision.
[2] Nikos Paragios,et al. Handbook of Mathematical Models in Computer Vision , 2005 .
[3] J. Nocedal,et al. A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..
[4] Rachid Deriche,et al. Regularization, Scale-Space, and Edge Detection Filters , 1996, Journal of Mathematical Imaging and Vision.
[5] A. Griewank,et al. Automatic differentiation of algorithms : theory, implementation, and application , 1994 .
[6] 直樹 武川,et al. Regularization , 2019, Encyclopedia of Continuum Mechanics.
[7] Bernhard Kawohl,et al. From Mumford–Shah to Perona–Malik in image processing , 2004 .
[8] R. Stephenson. A and V , 1962, The British journal of ophthalmology.
[9] Christian Bischof,et al. Automatic differentiation, tangent linear models, and (pseudo) adjoints , 1995 .
[10] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[11] Ron Kimmel,et al. Variational Restoration and Edge Detection for Color Images , 2003, Journal of Mathematical Imaging and Vision.
[12] Griewank,et al. On automatic differentiation , 1988 .
[13] Tony F. Chan,et al. Total variation blind deconvolution , 1998, IEEE Trans. Image Process..
[14] Jorge Nocedal,et al. A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..
[15] Christian Bischof,et al. Adifor 2.0: automatic differentiation of Fortran 77 programs , 1996 .
[16] Riccardo March,et al. A variational method for the recovery of smooth boundaries , 1997, Image Vis. Comput..
[17] Shen,et al. Piecewise H − 1 + H 0 + H 1 Images and the Mumford-Shah-Sobolev Model for Segmented Image Decomposition Jianhong ( , 2005 .
[18] Berthold K. P. Horn,et al. Determining Optical Flow , 1981, Other Conferences.
[19] Tony F. Chan,et al. Active contours without edges , 2001, IEEE Trans. Image Process..
[20] Laurent D. Cohen,et al. Image Registration, Optical Flow and Local Rigidity , 2001, Journal of Mathematical Imaging and Vision.
[21] M. Nikolova. An Algorithm for Total Variation Minimization and Applications , 2004 .
[22] D. Mumford,et al. Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .
[23] Mario Bertero,et al. The Stability of Inverse Problems , 1980 .
[24] Jorge Nocedal,et al. Large Scale Unconstrained Optimization , 1997 .
[25] Donald Geman,et al. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .
[26] 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 .
[27] Andreas Griewank,et al. Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C/C++ , 1996, TOMS.
[28] Josiane Zerubia,et al. A Variational Model for Image Classification and Restoration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[29] Luminita A. Vese,et al. Multiphase Object Detection and Image Segmentation , 2003 .
[30] Bart M. ter Haar Romeny,et al. Geometry-Driven Diffusion in Computer Vision , 1994, Computational Imaging and Vision.
[31] Andreas Griewank,et al. Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.
[32] B. Ripley,et al. Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.
[33] Jean-Michel Morel,et al. Variational methods in image segmentation , 1995 .
[34] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] J. Hadamard. Sur les problemes aux derive espartielles et leur signification physique , 1902 .
[36] A. Chambolle. Practical, Unified, Motion and Missing Data Treatment in Degraded Video , 2004, Journal of Mathematical Imaging and Vision.
[37] Guillermo Sapiro,et al. Robust anisotropic diffusion , 1998, IEEE Trans. Image Process..
[38] Rachid Deriche,et al. Regularization, Scale-Space, and Edge Detection Filters , 1996, ECCV.
[39] Thomas Slawig,et al. Generating efficient derivative code with TAF: Adjoint and tangent linear Euler flow around an airfoil , 2005, Future Gener. Comput. Syst..
[40] Michel Barlaud,et al. Variational approach for edge-preserving regularization using coupled PDEs , 1998, IEEE Trans. Image Process..
[41] Rachid Deriche,et al. Computing Optical Flow via Variational Techniques , 1999, SIAM J. Appl. Math..
[42] L. Ambrosio,et al. Approximation of functional depending on jumps by elliptic functional via t-convergence , 1990 .
[43] Andy M. Yip,et al. Recent Developments in Total Variation Image Restoration , 2004 .
[44] Thomas Kaminski,et al. Automatic Sparsity Detection Implemented as a Source-to-Source Transformation , 2006, International Conference on Computational Science.
[45] Jianhong Shen,et al. Gamma-Convergence Approximation to Piecewise Constant Mumford-Shah Segmentation , 2005, ACIVS.
[46] X. Zou,et al. Introduction to Adjoint Techniques and the MM5 Adjoint Modeling System. , 1997 .
[47] Josien P. W. Pluim,et al. Image registration , 2003, IEEE Transactions on Medical Imaging.
[48] Stanley Osher,et al. Image Decomposition and Restoration Using Total Variation Minimization and the H1 , 2003, Multiscale Model. Simul..
[49] Alper Yilmaz,et al. Level Set Methods , 2007, Wiley Encyclopedia of Computer Science and Engineering.
[50] Thomas Kaminski,et al. Recipes for adjoint code construction , 1998, TOMS.
[51] Jianhong Shen,et al. Digital inpainting based on the Mumford–Shah–Euler image model , 2002, European Journal of Applied Mathematics.
[52] A. Chambolle,et al. Inverse problems in image processing and image segmentation : some mathematical and numerical aspects , 2000 .
[53] Antonin Chambolle,et al. Total Variation Minimization and a Class of Binary MRF Models , 2005, EMMCVPR.
[54] Andrew Blake,et al. Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.
[55] D. Mumford. The Bayesian Rationale for Energy Functionals 1 , 1994 .
[56] Der Fakult,et al. On Variational Problems and Gradient Flows in Image Processing , 2005 .
[57] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[58] X. Yi. On Automatic Differentiation , 2005 .
[59] Donald Geman,et al. Nonlinear image recovery with half-quadratic regularization , 1995, IEEE Trans. Image Process..
[60] Felice Andrea Pellegrino,et al. Self-adaptive regularization , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[61] Jianhong Shen,et al. Piecewise H−1+H0+H1 images and the Mumford-Shah-Sobolevmodel for segmented image decomposition , 2005 .
[62] Jitendra Malik,et al. Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[63] M. J. D. Powell,et al. on The state of the art in numerical analysis , 1987 .
[64] B. Vemuri,et al. A level-set based approach to image registration , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).
[65] Tony F. Chan,et al. Euler's Elastica and Curvature-Based Inpainting , 2003, SIAM J. Appl. Math..
[66] M. Bertero,et al. Ill-posed problems in early vision , 1988, Proc. IEEE.
[67] Andreas Griewank,et al. The chain rule revisited in scientific computing. , 1991 .
[68] Yair Shapira,et al. Solving PDEs in C++: Numerical Methods in a Unified Object-Oriented Approach , 2006 .
[69] 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.