Construct clusters for ::class_ndim_norm.
::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 tuple).
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 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 Euclidian 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.
|
Foreground (input_object) |
region(-array) -> Hobject: HRegion(Array) |
| Foreground pixels to be trained. | |
|
Background (input_object) |
region(-array) -> Hobject: HRegion(Array) |
| Background pixels to be trained (rejection class). | |
|
Image (input_object) |
image(-array) -> Hobject: HImage(Array) ( byte / int1 / int2 / int4 / real ) |
| Multi-channel training image. | |
|
Metric (input_control) |
string -> HTuple.char * |
| Metric to be used. | |
| Default value: 'euclid' | |
| List of values: 'euclid', 'maximum' | |
|
Distance (input_control) |
number -> HTuple.double / long |
| Maximum cluster radius. | |
| Default value: 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 | |
| Typical range of values: 0.0 <= Distance <= 511.0 (lin) | |
| Minimum increment: 0.01 | |
|
Recommended increment: 1.0 | |
| Restriction: Radius > 0.0 | |
|
MinNumberPercent (input_control) |
number -> HTuple.double / long |
| 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 value: 0.01 | |
| Suggested values: 0.001, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0 | |
| Typical range of values: 0.0 <= MinNumberPercent <= 100.0 (lin) | |
| Minimum increment: 0.01 | |
|
Recommended increment: 0.1 | |
| Restriction: (0 <= MinNumberPercent) && (MinNumberPercent <= 100) | |
|
Radius (output_control) |
real-array -> HTuple.double * |
| Cluster radii of half edge lengths. | |
|
Center (output_control) |
real-array -> HTuple.double * |
| Coordinates of all cluster centers. | |
|
Quality (output_control) |
real -> HTuple.double * |
| Overlap of the rejection class with the classified objects (1: no overlapping). | |
| Assertion: (0 <= Quality) && (Quality <= 1) | |
::learn_ndim_norm returns 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.
::min_max_gray, ::sobel_amp, ::gauss_image, ::reduce_domain, ::diff_of_gauss
::class_ndim_norm, ::connection, ::dilation1, ::erosion1, ::opening, ::closing, ::rank_region, ::shape_trans, ::skeleton
::class_ndim_norm, ::class_ndim_box, ::histo_2dim
P. Haberäcker, "Digitale Bildverarbeitung"; Hanser-Studienbücher, München, Wien, 1987
Region processing
