learn_ndim_box — Train a classificator using a multi-channel image.
learn_ndim_box is obsolete and is only provided for
reasons of backward compatibility.
learn_ndim_box(Foreground, Background, MultiChannelImage : : ClassifHandle : )
learn_ndim_box trains the classificator ClassifHandle
with the gray values of MultiChannelImage using the
points in Foreground as training sample. The points in
Background are to be rejected by the classificator. The
classificator trained thus can be used in class_ndim_box to
segment multi-channel images.
Foreground are the points that should be found, Background
contains the points that should not be found.
Each pixel is trained once during the training process. For points
in Foreground the class “0” is used, while for
Background “1” is used. Pixels are trained by
alternating points from Foreground with points from
Background. If one region is smaller than the other,
pixels are taken cyclically from the smaller region until the larger
region is exhausted. learn_ndim_box later accepts only
points that can be classified into class “0”.
From a user's point of view the key difference between
learn_ndim_norm and learn_ndim_box is that in the
latter case the rejection class affects the classification process
itself. Here, a hyper plane is generated that separates
Foreground and Background classes, so that no
points in feature space are classified incorrectly.
As for learn_ndim_norm, however, an overlap between
Foreground and Background class is allowed.
This has its effect on the return value Quality.
The larger the overlap, the smaller this value.
All channels must be of the same type.
This operator modifies the state of the following input parameter:
The value of this parameter may not be shared across multiple threads without external synchronization.
Foreground (input_object) region(-array) → object
Foreground pixels to be trained.
Background (input_object) region(-array) → object
Background pixels to be trained (rejection class).
MultiChannelImage (input_object) (multichannel-)image(-array) → object (byte / direction / cyclic / int1 / int2 / int4 / real)
Multi-channel training image.
ClassifHandle (input_control, state is modified) class_box → (handle)
Handle of the classifier.
Let N be the number of generated hyper-cuboids and A be the area of the larger input region. Then the runtime complexity is O(N * A).
learn_ndim_box returns 2 (H_MSG_TRUE) if all parameters are
correct and there is an active classificator. The behavior with
respect to the input images can be determined by setting the values
of the flags 'no_object_result' and
'empty_region_result' with set_system.
If necessary, an exception is raised.
class_ndim_box,
descript_class_box
learn_class_box,
learn_ndim_norm
Foundation