CGAL 5.1.2 - Classification
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Functions that perform classification based on a set of labels and a classifier, with or without regularization.
Functions | |
template<typename ConcurrencyTag , typename ItemRange , typename Classifier , typename LabelIndexRange > | |
void | CGAL::Classification::classify (const ItemRange &input, const Label_set &labels, const Classifier &classifier, LabelIndexRange &output) |
Runs the classification algorithm without any regularization. More... | |
template<typename ConcurrencyTag , typename ItemRange , typename ItemMap , typename NeighborQuery , typename Classifier , typename LabelIndexRange > | |
void | CGAL::Classification::classify_with_local_smoothing (const ItemRange &input, const ItemMap item_map, const Label_set &labels, const Classifier &classifier, const NeighborQuery &neighbor_query, LabelIndexRange &output) |
Runs the classification algorithm with a local smoothing. More... | |
template<typename ConcurrencyTag , typename ItemRange , typename ItemMap , typename NeighborQuery , typename Classifier , typename LabelIndexRange > | |
void | CGAL::Classification::classify_with_graphcut (const ItemRange &input, const ItemMap item_map, const Label_set &labels, const Classifier &classifier, const NeighborQuery &neighbor_query, const float strength, const std::size_t min_number_of_subdivisions, LabelIndexRange &output) |
Runs the classification algorithm with a global regularization based on a graph cut. More... | |
void CGAL::Classification::classify | ( | const ItemRange & | input, |
const Label_set & | labels, | ||
const Classifier & | classifier, | ||
LabelIndexRange & | output | ||
) |
#include <CGAL/Classification/classify.h>
Runs the classification algorithm without any regularization.
There is no relationship between items, the classification energy is only minimized itemwise. This method is quick but produces suboptimal results.
ConcurrencyTag | enables sequential versus parallel algorithm. Possible values are Parallel_if_available_tag , Parallel_tag or Sequential_tag . |
ItemRange | model of ConstRange . Its iterator type is RandomAccessIterator . Its value type depends on the data that is classified (for example, CGAL::Point_3 or CGAL::Triangle_3 ). |
Classifier | model of Classifier . |
Model | of Range with random access iterators whose value type is an integer type. |
input | input range. |
labels | set of input labels. |
classifier | input classifier. |
output | where to store the result. It is stored as a sequence, ordered like the input range, containing for each point the index (in the Label_set ) of the assigned label. |
void CGAL::Classification::classify_with_graphcut | ( | const ItemRange & | input, |
const ItemMap | item_map, | ||
const Label_set & | labels, | ||
const Classifier & | classifier, | ||
const NeighborQuery & | neighbor_query, | ||
const float | strength, | ||
const std::size_t | min_number_of_subdivisions, | ||
LabelIndexRange & | output | ||
) |
#include <CGAL/Classification/classify.h>
Runs the classification algorithm with a global regularization based on a graph cut.
The computed classification energy is globally regularized through an alpha-expansion algorithm. This method is slow but provides the user with good quality results.
To speed up computation, the input domain can be subdivided into smaller subsets such that several smaller graph cuts are applied instead of a big one. The computation of these smaller graph cuts can be done in parallel. Increasing the number of subsets allows for faster computation times but can also reduce the quality of the results.
ConcurrencyTag | enables sequential versus parallel algorithm. Possible values are Parallel_if_available_tag , Parallel_tag or Sequential_tag . |
ItemRange | model of ConstRange . Its iterator type is RandomAccessIterator . |
ItemMap | model of ReadablePropertyMap whose key type is the value type of the iterator of ItemRange and value type is the type of item to classify (for example, CGAL::Point_3 ). |
NeighborQuery | model of NeighborQuery . |
Classifier | model of Classifier . |
Model | of Range with random access iterators whose value type is an integer type. |
input | input range. |
item_map | property map to access the input items. |
labels | set of input labels. |
classifier | input classifier. |
neighbor_query | used to access neighborhoods of items. |
strength | strength of the regularization with respect to the classification energy. Higher values produce more regularized output but may result in a loss of details. |
min_number_of_subdivisions | minimum number of subdivisions (for parallel processing to be efficient, this should be at least the number of cores of the processor). |
output | where to store the result. It is stored as a sequence, ordered like the input range, containing for each point the index (in the Label_set ) of the assigned label. |
void CGAL::Classification::classify_with_local_smoothing | ( | const ItemRange & | input, |
const ItemMap | item_map, | ||
const Label_set & | labels, | ||
const Classifier & | classifier, | ||
const NeighborQuery & | neighbor_query, | ||
LabelIndexRange & | output | ||
) |
#include <CGAL/Classification/classify.h>
Runs the classification algorithm with a local smoothing.
The computed classification energy is smoothed on a user defined local neighborhood of items. This method is a compromise between efficiency and better quality results.
ConcurrencyTag | enables sequential versus parallel algorithm. Possible values are Parallel_if_available_tag , Parallel_tag or Sequential_tag . |
ItemRange | model of ConstRange . Its iterator type is RandomAccessIterator . |
ItemMap | model of ReadablePropertyMap whose key type is the value type of the iterator of ItemRange and value type is the type of item to classify (for example, CGAL::Point_3 ). |
NeighborQuery | model of NeighborQuery . |
Classifier | model of Classifier . |
Model | of Range with random access iterators whose value type is an integer type. |
input | input range. |
item_map | property map to access the input items. |
labels | set of input labels. |
classifier | input classifier. |
neighbor_query | used to access neighborhoods of items. |
output | where to store the result. It is stored as a sequence, ordered like the input range, containing for each point the index (in the Label_set ) of the assigned label. |