IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Variational methods in image segmentation
Variational methods in image segmentation
A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C/C++
ACM Transactions on Mathematical Software (TOMS)
Regularization, Scale-Space, and Edge Detection Filters
Journal of Mathematical Imaging and Vision
Recipes for adjoint code construction
ACM Transactions on Mathematical Software (TOMS)
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
A Variational Model for Image Classification and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Registration, Optical Flow and Local Rigidity
Journal of Mathematical Imaging and Vision
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Adifor 2.0: Automatic Differentiation of Fortran 77 Programs
IEEE Computational Science & Engineering
Variational Restoration and Edge Detection for Color Images
Journal of Mathematical Imaging and Vision
A Level-Set Based Approach to Image Registration
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Journal of Mathematical Imaging and Vision
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
Future Generation Computer Systems
Γ-Convergence approximation to piecewise constant mumford-shah segmentation
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Automatic sparsity detection implemented as a source-to-source transformation
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Solving PDEs in C++: Numerical Methods in a Unified Object-Oriented Approach
Solving PDEs in C++: Numerical Methods in a Unified Object-Oriented Approach
Total variation blind deconvolution
IEEE Transactions on Image Processing
Variational approach for edge-preserving regularization using coupled PDEs
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Nonlinear image recovery with half-quadratic regularization
IEEE Transactions on Image Processing
Fast Marker Based C-Arm Pose Estimation
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
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Many vision problems can be formulated as minimization of appropriate energy functionals. These energy functionals are usually minimized, based on the calculus of variations (Euler-Lagrange equation). Once the Euler-Lagrange equation has been determined, it needs to be discretized in order to implement it on a digital computer. This is not a trivial task and, is moreover, error-prone. In this paper, we propose a flexible alternative. We discretize the energy functional and, subsequently, apply the mathematical concept of algorithmic differentiation to directly derive algorithms that implement the energy functional's derivatives. This approach has several advantages: First, the computed derivatives are exact with respect to the implementation of the energy functional. Second, it is basically straightforward to compute second-order derivatives and, thus, the Hessian matrix of the energy functional. Third, algorithmic differentiation is a process which can be automated. We demonstrate this novel approach on three representative vision problems (namely, denoising, segmentation, and stereo) and show that state-of-the-art results are obtained with little effort.