Point Cloud Library (PCL)  1.9.1-dev
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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 285 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 287 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]typedefines the condition of termination(max iter., desired accuracy)
[in]max_countdefines the max number of iterations
[in]epsilondefines the desired accuracy

Definition at line 298 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 315 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 312 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 301 of file implicit_shape_model.h.

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


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