SoAutoIntensityClassificationProcessing classifies all pixels/voxels of an image using the k-means method. More...
#include <ImageViz/Engines/ImageSegmentation/Classification/SoAutoIntensityClassificationProcessing.h>
Public Member Functions | |
SoAutoIntensityClassificationProcessing () | |
Public Attributes | |
SoSFEnum | computeMode |
SoSFImageDataAdapter | inImage |
SoSFInt32 | classNumber |
SoSFInt32 | dataPercentage |
SoImageVizEngineOutput < SoSFImageDataAdapter, SoImageDataAdapter * > | outLabelImage |
Given a set of observations (x1, x2, ..., xn), where each observation is a d-dimensional real vector, k-means clustering aims to partition the n observations into k (= n) sets S = {S1, S2, ..., Sk} so as to minimize the within-cluster sum of squares (WCSS) (sum of distance functions of each point in the cluster to the K center).
computeMode | MODE_AUTO |
inImage | NULL |
classNumber | 2 |
dataPercentage | 100 |
SoAutoIntensityClassificationProcessing::SoAutoIntensityClassificationProcessing | ( | ) |
Constructor.
The number of classes to detect (i.e.
the label number of the output). Default value is 2.
Select the compute Mode (2D or 3D or AUTO) Use enum ComputeMode.
Default is MODE_AUTO
The data percentage used for pre-computing the classification.
Default value is 100.
The input image.
Default value is NULL. Supported types include: grayscale binary label color image.
SoImageVizEngineOutput<SoSFImageDataAdapter,SoImageDataAdapter*> SoAutoIntensityClassificationProcessing::outLabelImage |
The output label image where a one label represents one class.
Default value is NULL. Supported types include: label color image.