pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TermCriteria Struct Reference

This structure is used for determining the end of the k-means clustering process. More...

#include <pcl/recognition/implicit_shape_model.h>

Public Types
enum	{ COUNT = 1 , EPS = 2 }

Public Member Functions
	TermCriteria (int type, int max_count, float epsilon)
	Termination criteria constructor. More...

Public Attributes
int	type_
	Flag that determines when the k-means clustering must be stopped. More...
int	max_count_
	Defines maximum number of iterations for k-means clustering. More...
float	epsilon_
	Defines the accuracy for k-means clustering. More...

Detailed Description

template<int FeatureSize, typename PointT, typename NormalT = pcl::Normal>
struct pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TermCriteria

This structure is used for determining the end of the k-means clustering process.

Definition at line 281 of file implicit_shape_model.h.

Member Enumeration Documentation

anonymous enum

template<int FeatureSize, typename PointT , typename NormalT = pcl::Normal>

anonymous enum

Enumerator
COUNT
EPS

Definition at line 283 of file implicit_shape_model.h.

Constructor & Destructor Documentation

TermCriteria()

template<int FeatureSize, typename PointT , typename NormalT = pcl::Normal>

pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TermCriteria::TermCriteria ( int  type, int  max_count, float  epsilon  )

inline

Termination criteria constructor.

Parameters

[in]	type	defines the condition of termination(max iter., desired accuracy)
[in]	max_count	defines the max number of iterations
[in]	epsilon	defines the desired accuracy

Definition at line 294 of file implicit_shape_model.h.

Member Data Documentation

epsilon_

template<int FeatureSize, typename PointT , typename NormalT = pcl::Normal>

float pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TermCriteria::epsilon_

Defines the accuracy for k-means clustering.

Definition at line 311 of file implicit_shape_model.h.

Referenced by pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::computeKMeansClustering().

max_count_

template<int FeatureSize, typename PointT , typename NormalT = pcl::Normal>

int pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TermCriteria::max_count_

Defines maximum number of iterations for k-means clustering.

Definition at line 308 of file implicit_shape_model.h.

Referenced by pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::computeKMeansClustering().

type_

template<int FeatureSize, typename PointT , typename NormalT = pcl::Normal>

int pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TermCriteria::type_

Flag that determines when the k-means clustering must be stopped.

If type_ equals COUNT then it must be stopped when the max number of iterations will be reached. If type_ eaquals EPS then it must be stopped when the desired accuracy will be reached. These flags can be used together, in that case the clustering will be finished when one of these conditions will be reached.

Definition at line 305 of file implicit_shape_model.h.

Referenced by pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::computeKMeansClustering().

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The documentation for this struct was generated from the following file:

• pcl/recognition/implicit_shape_model.h