CGAL::Feature_graph::Optimization_parameters_on_image< NormalEstimator > Class Template Reference

#include <CGAL/Feature_graph/Optimization_parameters.h>

Inherits from

CGAL::Feature_graph::Optimization_parameters< NormalEstimator >.

Definition

template<typename NormalEstimator = Feature_graph::AmbrosioTortorelli_on_image::Normal_functor>
class CGAL::Feature_graph::Optimization_parameters_on_image< NormalEstimator >

The class Optimization_parameters_on_image describes the parameters for the optimization step with default values adapted for image inputs.

Template Parameters

Normal_estimator

a model of NormalEstimator.

See also

CGAL::Feature_graph::Optimization_parameters_on_surface

CGAL::Feature_graph::Detect_sharp_features_on_labeled_image

CGAL::Feature_graph::Detect_sharp_features_on_surface

Types
typedef Base::Size	Size
	Natural number type.
typedef Base::FT	FT
	Numerical type.

Constructor
template<typename NamedParameters = CGAL::parameters::Default_named_parameters>
	Optimization_parameters_on_image (const NamedParameters &np=parameters::default_values())
	constructs the parameters used to optimize the feature graph placement on the surface of an image.

Additional Inherited Members
Public Types inherited from CGAL::Feature_graph::Optimization_parameters< NormalEstimator >
typedef std::size_t	Size
	Natural number type.
typedef double	FT
	Numerical type.
typedef NormalEstimator	Normal_estimator
	Type of the functor that evaluates normals.
Public Member Functions inherited from CGAL::Feature_graph::Optimization_parameters< NormalEstimator >
Size	maximum_number_of_iteration () const
	returns the maximum number of iteration of the gradient descent.
FT	start_step_size () const
	returns the step size at the first iteration of the gradient descent.
FT	end_step_size () const
	returns the step size at the last iteration of the gradient descent.
FT	mininmum_energy_delta () const
	returns the minimum energy change to stop the gradient descent iterations.
FT	collapse_distance () const
	returns the distance to collapse adjacent points in a line during the gradient descent.
FT	smoothing_factor () const
	returns the smoothing factor of the energy.
FT	refine_normal_distance () const
	returns the distance to refine the normals of elements near the sharp features.
FT	plane_detection_distance () const
	returns the distance to collect elements near the sharp features to determine the adjacent planes.
Normal_estimator	normal_estimator () const
	returns a functor that estimates the normal on an element.

Constructor & Destructor Documentation

Optimization_parameters_on_image()

template<typename NormalEstimator = Feature_graph::AmbrosioTortorelli_on_image::Normal_functor>

template<typename NamedParameters = CGAL::parameters::Default_named_parameters>

CGAL::Feature_graph::Optimization_parameters_on_image< NormalEstimator >::Optimization_parameters_on_image

(

const NamedParameters &

np = parameters::default_values()

)

constructs the parameters used to optimize the feature graph placement on the surface of an image.

Template Parameters

NamedParameters

a sequence of Named Parameters

Parameters

np	an optional sequence of Named Parameters among the ones listed below. The distances are expressed in terms of the longest voxel edge length.

Optional Named Parameters

maximum_iteration the maximum number of iteration of the gradient descent. Default: Size(20)
start_step_size the step size at the first iteration of the gradient descent. Default: FT(1.0)
end_step_size the step size at the last iteration of the gradient descent. Default: FT(0.125)
min_energy_delta the minimum energy change to stop the gradient descent iterations. Default: FT(1.e-3)
collapse_distance the distance to collapse adjacent points in a line during the gradient descent. Default: FT(0.5)
smoothing_factor the smoothing factor of the energy. 0 means no smoothing, 1 means that the energy will consider smoothing with the same weight as the displacement toward the sharp features of the surface. Default: FT(1.0)
refine_normal_distance the distance to refine the normals of elements near the sharp features. Default: FT(4.0)
plane_detection_distance the distance to collect elements near the sharp features to determine the adjacent planes. Default: FT(4.0)
normal_estimator a functor to evaluate the normals on elements. Default: The normal_functor of the CGAL::Feature_graph::AmbrosioTortorelli_on_image that will be initialized with the image