Classifies the image sample located in the given ROI.
Region of interest specifying the location of the sample in the image.
The ROI must be one or more closed contours. If ROI descriptor is empty or not connected, the entire image is considered to be the region. For best performance, use only one rectangle or one rotated rectangle per sample.
Coordinates of the bounding rectangle.
Individual shapes that define an ROI.
Object specifying if contour is the external or internal edge of an ROI.
Shape type of the contour.
Relative position of the contour.
Reference to the classifier session on which the node operates.
Reference to the source image.
Error conditions that occur before this node runs.
The node responds to this input according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Default: No error
Reference to the classifier session the node creates.
Reference to the source image.
Class into which the classifier session categorizes the input sample.
Array with one element for every class in the classifier session.
One of the classes in classifier session in.
Distance from the closest sample in class to the input sample when performing nearest neighbor and k-nearest neighbor classification.
distance is the distance between the input sample and the center of each class when performing minimum mean distance classification.
Error information.
The node produces this output according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Estimations of how well the classifier session classified the input.
The score can vary from 0 to 1000, where 1000 represents the best possible score.
Score that indicates how much better the assigned class represents the input sample than other classes represent the input.
Score that indicates the similarity of the input and the assigned class.
Use identification score only when you cannot reach a decision about the class of a sample using classification score alone.
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported
Web Server: Not supported in VIs that run in a web application