Collaboration diagram for Optimizers:
|
Classes | |
| class | itk::AmoebaOptimizer |
| Wrap of the vnl_amoeba algorithm. More... | |
| class | itk::ConjugateGradientOptimizer |
| Wrap of the vnl_conjugate_gradient. More... | |
| class | itk::CostFunction |
| Base class for cost functions intended to be used with Optimizers. More... | |
| class | itk::CumulativeGaussianOptimizer |
| This is an optimizer specific to estimating the parameters of Cumulative Gaussian sampled data. More... | |
| class | itk::ExhaustiveOptimizer |
| Optimizer that fully samples a grid on the parametric space. More... | |
| class | itk::FRPROptimizer |
| Implements Fletch-Reeves & Polak-Ribiere optimization using dBrent line search. More... | |
| class | itk::GradientDescentOptimizer |
| Implement a gradient descent optimizer. More... | |
| class | itk::LBFGSBOptimizer |
| Limited memory Broyden Fletcher Goldfarb Shannon minimization with simple bounds. More... | |
| class | itk::LBFGSOptimizer |
| Wrap of the vnl_lbfgs algorithm. More... | |
| class | itk::LevenbergMarquardtOptimizer |
| Wrap of the vnl_levenberg_marquardt algorithm. More... | |
| class | itk::MultipleValuedCostFunction |
| This class is a base for the CostFunctions returning a multiple values. More... | |
| class | itk::MultipleValuedNonLinearOptimizer |
| This class is a base for the Optimization methods that optimize a multiple valued function. More... | |
| class | itk::MultipleValuedNonLinearVnlOptimizer |
| This class is a base for the Optimization methods that optimize a multi-valued function. More... | |
| class | itk::MultipleValuedVnlCostFunctionAdaptor |
| This class is an Adaptor that allows to pass itk::MultipleValuedCostFunctions to vnl_optimizers expecting a vnl_cost_function. More... | |
| class | itk::NonLinearOptimizer |
| Wrap of the vnl_nonlinear_minimizer to be adapted. More... | |
| class | itk::OnePlusOneEvolutionaryOptimizer |
| 1+1 evolutionary strategy optimizer More... | |
| class | itk::Optimizer |
| Generic representation for an optimization method. More... | |
| class | itk::PowellOptimizer |
| Implements Powell optimization using Brent line search. More... | |
| class | itk::QuaternionRigidTransformGradientDescentOptimizer |
| Implement a gradient descent optimizer. More... | |
| class | itk::RegularStepGradientDescentBaseOptimizer |
| Implement a gradient descent optimizer. More... | |
| class | itk::RegularStepGradientDescentOptimizer |
| Implement a gradient descent optimizer. More... | |
| class | itk::ShapePriorMAPCostFunction< TFeatureImage, TOutputPixel > |
| Represents the maximum aprior (MAP) cost function used ShapePriorSegmentationLevelSetImageFilter to estimate the shape paramaeters. More... | |
| class | itk::ShapePriorMAPCostFunctionBase< TFeatureImage, TOutputPixel > |
| Represents the base class of maximum aprior (MAP) cost function used ShapePriorSegmentationLevelSetImageFilter to estimate the shape paramaeters. More... | |
| class | itk::SingleValuedCostFunction |
| This class is a base for the CostFunctions returning a single value. More... | |
| class | itk::SingleValuedNonLinearOptimizer |
| This class is a base for the Optimization methods that optimize a single valued function. More... | |
| class | itk::SingleValuedNonLinearVnlOptimizer |
| This class is a base for the Optimization methods that optimize a single valued function. More... | |
| class | itk::SingleValuedVnlCostFunctionAdaptor |
| This class is an Adaptor that allows to pass itk::SingleValuedCostFunctions to vnl_optimizers expecting a vnl_cost_function. More... | |
| class | itk::SPSAOptimizer |
| An optimizer based on simultaneous perturbation... More... | |
| class | itk::VersorRigid3DTransformOptimizer |
| Implement a gradient descent optimizer for the VersorRigid3DTransform parameter space. More... | |
| class | itk::VersorTransformOptimizer |
| Implement a gradient descent optimizer. More... | |
Detailed Description
- See also:
- Components of Registration Methods
Set of Optimization methods. Some of these methods are adaptors for classes already defined in the VNL library. These methods have been particularly tailored to be used as modular components in the registration framework.
