gen_initial_components — Extract the initial components of a component model.
gen_initial_components(ModelImage : InitialComponents : ContrastLow, ContrastHigh, MinSize, Mode, GenericName, GenericValue : )
In general, there are two possibilities to use
gen_initial_components. The first possibility should be
chosen if the components of the component model are not known. Then
gen_initial_components automatically extracts the initial
components of a component model from a model image. The second
possibility can be chosen if the components of the component model
are approximately known. Then gen_initial_components can be
used to find suitable parameter values for the model feature
extraction in train_model_components and
create_component_model. Hence, the second possibility is
comparable to the function of inspect_shape_model within the
shape-based matching.
When using the first possibility, gen_initial_components
extracts the initial components of a component model from a model
image ModelImage. As already mentioned, this is especially
useful if the components of the component model are not known. In
this case, the resulting initial components can be used to
automatically train the component model with
train_model_components, which extracts the (final) model
components and the relations between them.
gen_initial_components returns the initial components in a
region object tuple InitialComponents that contains a
representation for each initial component in form of contour
regions.
For the automatic determination of the initial components, the
domain of the model image ModelImage must contain the
entire compound object including all components. Mode
specifies the method used for the automatic computation. Currently,
only the mode 'connection' is available. In this mode the
automatic computation is performed in two steps: In the first step,
features are extracted using the parameters ContrastLow,
ContrastHigh, and MinSize. These three parameters
define the contours of which the initial components should consist
and should be chosen such that only the significant features of the
model image are contained in the initial components.
ContrastLow and ContrastHigh specify the gray
value contrast of the points that should be contained in the initial
components. The contrast is a measure for local gray value
differences between the object and the background and between
different parts of the object. The model image is segmented using a
method similar to the hysteresis threshold method used in
edges_image. Here, ContrastLow determines the lower
threshold, while ContrastHigh determines the upper
threshold. If the same value is passed for ContrastLow and
ContrastHigh a simple thresholding operation is
performed. For more information about the hysteresis threshold
method, see hysteresis_threshold. MinSize can be
used to select only significant features for the initial components
based on the size of the connected contour regions, i.e., connected
contour regions with fewer than MinSize points are
suppressed.
The resulting connected contour regions are iteratively merged in
the second step. For this, two contour regions are merged if the
distance between both regions is smaller than a certain threshold
(see below). Finally, the merged regions are returned in
InitialComponents and can be used to train the component
model by passing them to train_model_components.
To control the internal image processing, the parameters
GenericName and GenericValue are used. This is
done by passing the names of the control parameters to be changed in
GenericName as a list of strings. In GenericValue
the values are passed at the corresponding index positions.
Normally, none of the values needs to be changed. A change should only be applied in case of unsatisfying results of the automatic determination of the initial components. The two parameters that can be changed are 'merge_distance' and 'merge_fraction'; both are used during the iterative merging of contour regions (see above). First, the fraction of contour pixels of one contour region that at most have a distance of 'merge_distance' from another contour region is computed. If this fraction exceeds the value that is passed in 'merge_fraction' the two contour regions are merged. Consequently, the higher 'merge_distance' and the lower 'merge_fraction' is chosen the more contour regions are merged. The default value of 'merge_distance' is 5 and the default value of 'merge_fraction' is 0.5 (corresponds to 50 percent).
When using the second possibility, i.e., the components of the
component model are approximately known, the training by using
train_model_components can be performed without previously
executing gen_initial_components. If this is desired, the
initial components can be specified by the user and directly passed
to train_model_components. Furthermore, if the components as
well as the relative movements (relations) of the components are
known, gen_initial_components as well as
train_model_components need not be executed. In fact, by
immediately passing the components as well as the relations to
create_component_model, the component model can be created
without any training. In both cases, however,
gen_initial_components can be used to evaluate the effect of
the feature extraction parameters ContrastLow,
ContrastHigh, and MinSize of
train_model_components and create_component_model,
and hence to find suitable parameter values for a certain
application.
For this, the image regions for the (initial) components must be
explicitly given, i.e., for each (initial) component a separate
image from which the (initial) component should be created is
passed. In this case, ModelImage contains multiple image
objects. The domain of each image object is used as the region of
interest for calculating the corresponding (initial) component. The
image matrix of all image objects in the tuple must be identical,
i.e., ModelImage cannot be constructed in an arbitrary
manner using concat_obj, but must be created from the same
image using add_channels or equivalent calls. If this is not
the case, an error message is returned. If the parameters
ContrastLow, ContrastHigh, or MinSize
only contain one element, this value is applied to the creation of
all (initial) components. In contrast, if different values for
different (initial) components should be used, tuples of values can
be passed for these three parameters. In this case, the tuples must
have a length that corresponds to the number of (initial)
components, i.e., the number of image objects in
ModelImage. The contour regions of the (initial) components
are returned in InitialComponents.
Thus, the second possibility is equivalent to the function of
inspect_shape_model within the shape-based
matching. However, in contrast to inspect_shape_model,
gen_initial_components does not return the contour regions on
multiple image pyramid levels. Therefore, if the number of pyramid
levels to be used should be chosen manually, preferably
inspect_shape_model should be called individually for each
(initial) component.
For both described possibilities the parameters
ContrastLow, ContrastHigh, and MinSize
can be automatically determined. If both hysteresis threshold should
be automatically determined, both ContrastLow and
ContrastHigh must be set to 'auto'. In contrast,
if only one threshold value should be determined,
ContrastLow must be set to 'auto' while
ContrastHigh must be set to an arbitrary value different
from 'auto'.
If the input image ModelImage has one channel the
representation of the model is created with the method that is used
in create_component_model or
create_trained_component_model for the metrics
'use_polarity', 'ignore_global_polarity', and
'ignore_local_polarity'. If the input image has more than
one channel the representation is created with the method that is
used for the metric 'ignore_color_polarity'.
ModelImage (input_object) (multichannel-)image(-array) → object (byte / uint2)
Input image from which the initial components should be extracted.
InitialComponents (output_object) region-array → object
Contour regions of initial components.
ContrastLow (input_control) integer(-array) → (integer / string)
Lower hysteresis threshold for the contrast of the initial components in the image.
Default value: 'auto'
Suggested values: 'auto', 10, 20, 30, 40, 60, 80, 100, 120, 140, 160
Restriction: ContrastLow > 0
ContrastHigh (input_control) integer(-array) → (integer / string)
Upper hysteresis threshold for the contrast of the initial components in the image.
Default value: 'auto'
Suggested values: 'auto', 10, 20, 30, 40, 60, 80, 100, 120, 140, 160
Restriction: ContrastHigh > 0 && ContrastHigh >= ContrastLow
MinSize (input_control) integer(-array) → (integer / string)
Minimum size of the initial components.
Default value: 'auto'
Suggested values: 'auto', 0, 5, 10, 20, 30, 40
Restriction: MinSize >= 0
Mode (input_control) string → (string)
Type of automatic segmentation.
Default value: 'connection'
List of values: 'connection'
GenericName (input_control) string(-array) → (string)
Names of optional control parameters.
Default value: []
List of values: 'merge_distance', 'merge_fraction'
GenericValue (input_control) number(-array) → (real / integer)
Values of optional control parameters.
Default value: []
* First example that shows the use of gen_initial_components to automatically
* extract the initial components from a model image.
* Get the model image.
read_image (Image, 'model_image.tif')
* Define the entire model region.
gen_rectangle1 (ModelRegion, 119, 106, 330, 537)
reduce_domain (Image, ModelRegion, ModelImage)
* Automatically generate the initial components.
gen_initial_components (ModelImage, InitialComponents, 40, 40, 20, \
'connection', [], [])
* Extract the model components and train the relations.
train_model_components (ModelImage, InitialComponents, TrainingImages, \
ModelComponents, 40, 40, 20, 0.85, 0, 0, rad(15), \
'speed', 'rigidity', 0.2, 0.5, ComponentTrainingID)
* Create the component model based on the training result.
create_trained_component_model (ComponentTrainingID, -rad(30), rad(60), 10, \
0.8, 'auto', 'auto', 'none', \
'use_polarity', 'false', ComponentModelID, \
RootRanking)
* Second example that shows the use of gen_initial_components to evaluate
* the effect of the feature extraction parameters.
* Get the model image.
read_image (ModelImage, 'model_image.tif')
* Define the regions for the components.
gen_rectangle2 (ComponentRegions, 318, 109, -1.62, 34, 19)
gen_rectangle2 (Rectangle2, 342, 238, -1.63, 32, 17)
gen_rectangle2 (Rectangle3, 355, 505, 1.41, 25, 17)
concat_obj (ComponentRegions, Rectangle2, ComponentRegions)
concat_obj (ComponentRegions, Rectangle3, ComponentRegions)
add_channels (ComponentRegions, ModelImage, ModelImageReduced)
gen_initial_components (ModelImageReduced, InitialComponents, 15, 40, 15, \
'connection', [], [])
* Create the component model by explicitly specifying the relations.
create_component_model (ModelImage, ComponentRegions, 20, 20, rad(25), 0, \
rad(360), 15, 40, 15, 10, 0.8, 'auto', 'auto', \
'none', 'use_polarity', 'false', ComponentModelID, \
RootRanking)
If the parameter values are correct, the operator
gen_initial_components returns the value 2 (H_MSG_TRUE). If the input
is empty (no input images are available) the behavior can be set via
set_system('no_object_result',<Result>). If necessary, an
exception is raised.
draw_region,
add_channels,
reduce_domain
Matching