learn_ndim_normđź”—
Short descriptionđź”—
learn_ndim_norm — Construct classes for class_ndim_norm.
Signatuređź”—
learn_ndim_norm( region Foreground, region Background, image Image, string Metric, number Distance, number MinNumberPercent, out real Radius, out real Center, out real Quality )
Descriptionđź”—
learn_ndim_norm generates classification clusters from the
region Foreground and the corresponding gray values in
the multi-channel image Image, which can be used in
class_ndim_norm. Background determines a class
of pixels not to be found in class_ndim_norm. This
parameter may be empty (empty object).
The parameter Distance determines the maximum distance
Radius of the clusters. It describes the minimum distance
between two cluster centers. If the parameter Distance
is small the (small) hyper-cubes or hyper-spheres can approximate
the feature space well. Simultaneously the runtime during
classification increases.
The ratio of the number of pixels in a cluster to the total number
of pixels (in percent) must be larger than the value of
MinNumberPercent, otherwise the cluster is not returned.
MinNumberPercent serves to eliminate outliers in the
training set. If it is chosen too large many clusters are
suppressed.
Two different clustering procedures can be selected: The minimum
Euclidean distance algorithm (n-dimensional hyper-spheres) and the maximum
algorithm (n-dimensional hyper-cubes) for describing the pixels of
the image to classify in the n-dimensional histogram (parameter
Metric). The Euclidean metric usually yields the better
results, but takes longer to compute. The parameter
Quality returns the quality of the clustering. It is a
measure of overlap between the rejection class and the classificator
classes. Values larger than 0 denote the corresponding ratio of
overlap. If no rejection region is given, its value is set to 1.
The regions in Background do not influence on the
clustering. They are merely used to check the results that can be
expected.
Execution informationđź”—
Execution information
-
Multithreading type: exclusive (runs in parallel only with independent operators).
-
Multithreading scope: global (may be called from any thread).
-
Processed without parallelization.
Parametersđź”—
Foreground (input_object) region(-array) → object
Foreground pixels to be trained.
Background (input_object) region(-array) → object
Background pixels to be trained (rejection class).
Image (input_object) (multichannel-)image(-array) → object (byte)
Multi-channel training image.
Metric (input_control) string → (string)
Metric to be used.
Default: 'euclid'
List of values: 'euclid', 'maximum'
Distance (input_control) number → (real / integer)
Maximum cluster radius.
Default: 10.0
Suggested values: 1.0, 2.0, 3.0, 4.0, 6.0, 8.0, 10.0, 13.0, 17.0, 24.0, 30.0, 40.0
Value range: 1.0 ≤ Distance (lin)
Minimum increment: 0.01
Recommended increment: 1.0
MinNumberPercent (input_control) number → (real / integer)
The ratio of the number of pixels in a cluster to the total number of pixels (in percent) must be larger than MinNumberPercent (otherwise the cluster is not output).
Default: 0.01
Suggested values: 0.001, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0
Value range: 0.0 ≤ MinNumberPercent ≤ 100.0 (lin)
Minimum increment: 0.01
Recommended increment: 0.1
Radius (output_control) real-array → (real)
Cluster radii or half edge lengths.
Center (output_control) real-array → (real)
Coordinates of all cluster centers.
Quality (output_control) real → (real)
Overlap of the rejection class with the classified objects (1: no overlap).
Assertion: 0 <= Quality && Quality <= 1
Resultđź”—
learn_ndim_norm returns 2 (H_MSG_TRUE) if all parameters are
correct. 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.
Combinations with other operatorsđź”—
Combinations
Possible predecessors
min_max_gray, sobel_amp, binomial_filter, gauss_filter, reduce_domain, diff_of_gauss
Possible successors
class_ndim_norm, connection, dilation1, erosion1, opening, closing, rank_region, shape_trans, skeleton
See also
Referencesđź”—
P. Haberäcker, “Digitale Bildverarbeitung”; Hanser-Studienbücher, München, Wien, 1987
Moduleđź”—
Foundation