EasyColor::ImproveClassCenters
Redefines the class centers by computing the average color value of the pixels assigned to each class in the source image.
Namespace: Euresys::Open_eVision
[C++]
void ImproveClassCenters(
EROIC24* sourceImage,
EC24Vector* classCenters
)
Parameters
sourceImage
Pointer to the source image/ROI.
classCenters
Pointer to the vector of the class centers.
Remarks
This implements a step of the K-means method for unsupervised clustering.
Color image segmentation allows you to decompose a color image in different regions by assigning a "class" (integer index) to every pixel. The nearest neighbor method is used, i.e. for each class a representative center is specified, and a given pixel is associated to the class with the closest center.
Color image segmentation can be used in conjunction with EasyObject to perform blob analysis on the segmented regions.
EasyColor.ImproveClassCenters
Redefines the class centers by computing the average color value of the pixels assigned to each class in the source image.
Namespace: Euresys.Open_eVision
[C#]
void ImproveClassCenters(
Euresys.Open_eVision.EROIC24 sourceImage,
Euresys.Open_eVision.EC24Vector classCenters
)
Parameters
sourceImage
Pointer to the source image/ROI.
classCenters
Pointer to the vector of the class centers.
Remarks
This implements a step of the K-means method for unsupervised clustering.
Color image segmentation allows you to decompose a color image in different regions by assigning a "class" (integer index) to every pixel. The nearest neighbor method is used, i.e. for each class a representative center is specified, and a given pixel is associated to the class with the closest center.
Color image segmentation can be used in conjunction with EasyObject to perform blob analysis on the segmented regions.