classify_image_class_mlp
— Classify an image with a multilayer perceptron.
classify_image_class_mlp(Image : ClassRegions : MLPHandle, RejectionThreshold : )
classify_image_class_mlp
performs a pixel classification
with the multilayer perceptron (MLP) MLPHandle
on the
multichannel image Image
. Before calling
classify_image_class_mlp
the MLP must be trained with
train_class_mlp
. Image
must have NumInput
channels, as specified with create_class_mlp
. On output,
ClassRegions
contains NumOutput
regions as the
result of the classification. Note that the order of the regions that
are returned in ClassRegions
corresponds to the order of the
classes as defined by the training regions in
add_samples_image_class_mlp
. The parameter
RejectionThreshold
can be used to reject pixels that have
an uncertain classification. RejectionThreshold
represents
a threshold on the probability measure returned by the
classification (see classify_class_mlp
and
evaluate_class_mlp
). All pixels having a probability below
RejectionThreshold
are not assigned to any class. Because
an MLP typically assigns pixels that lie outside the convex hull of
the training data in the feature space to one of the classes with
high probability (confidence), it is useful in many cases to
explicitly train a rejection class, even if
RejectionThreshold
is used, by adding samples for the
rejection class with add_samples_image_class_mlp
and by
re-training the MLP with train_class_mlp
.
Image
(input_object) (multichannel-)image →
object (byte / cyclic / direction / int1 / int2 / uint2 / int4 / real)
Input image.
ClassRegions
(output_object) region-array →
object
Segmented classes.
MLPHandle
(input_control) class_mlp →
(handle)
MLP handle.
RejectionThreshold
(input_control) real →
(real)
Threshold for the rejection of the classification.
Default value: 0.5
Suggested values: 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Restriction: RejectionThreshold >= 0.0 && RejectionThreshold <= 1.0
read_image (Image, 'ic') gen_rectangle1 (Board, 80, 320, 110, 350) gen_rectangle1 (Capacitor, 359, 263, 371, 302) gen_rectangle1 (Resistor, 200, 252, 290, 256) gen_rectangle1 (IC, 180, 135, 216, 165) concat_obj (Board, Capacitor, Classes) concat_obj (Classes, Resistor, Classes) concat_obj (Classes, IC, Classes) create_class_mlp (3, 3, 4, 'softmax', 'principal_components', 3, 42, \ MLPHandle) add_samples_image_class_mlp (Image, Classes, MLPHandle) get_sample_num_class_mlp (MLPHandle, NumSamples) train_class_mlp (MLPHandle, 200, 1, 0.01, Error, ErrorLog) classify_image_class_mlp (Image, ClassRegions, MLPHandle, 0.5) dev_display (ClassRegions)
If the parameters are valid, the operator
classify_image_class_mlp
returns the value 2 (H_MSG_TRUE). If
necessary an exception is raised.
train_class_mlp
,
read_class_mlp
classify_image_class_gmm
,
classify_image_class_knn
,
classify_image_class_svm
,
classify_image_class_lut
,
class_ndim_norm
,
class_2dim_sup
add_samples_image_class_mlp
,
create_class_mlp
Foundation