class_ndim_norm ( MultiChannelImage : Regions : Metric, SingleMultiple, Radius, Center : )

Classify pixels using hyper-spheres or hyper-cubes.

class_ndim_norm classifies the pixels of the multi-channel image given in MultiChannelImage. The result is returned in Regions as one region per classification object. The metric used ('euclid' or 'maximum') is determined by Metric. This parameter must be set to the same value used in learn_ndim_norm. The parameter SingleMultiple determines whether one region ('single') or multiples regions ('multiple') are generated for each cluster. Radius determines the radii or half edge length of the clusters, respectively. Center determines their centers.


Parameters

MultiChannelImage (input_object)
multichannel-image(-array) -> object : byte
Multi channel input image.

Regions (output_object)
region-array -> object
Classification result.

Metric (input_control)
string -> string
Metric to be used.
Default value: 'euclid'
List of values: 'euclid', 'maximum'

SingleMultiple (input_control)
string -> string
Return one region or one region for each cluster.
Default value: 'single'
List of values: 'single', 'multiple'

Radius (input_control)
number(-array) -> real / integer
Cluster radii or half edge lengths (returned by learn_ndim_norm).

Center (input_control)
number(-array) -> real / integer
Coordinates of the cluster centers (returned by learn_ndim_norm).


Example (Syntax: C++)
#include "HIOStream.h"
#if !defined(USE_IOSTREAM_H)
using namespace std;
#endif
#include "HalconCpp.h"

int main ()
{
  HImage   image ("meer"),
           t1, t2, t3,
           m1, m2, m3, m;

  HWindow  w;

  w.SetColor ("green");
  image.Display (w);

  cout << "Draw your region of interest " << endl;

  HRegion testreg = w.DrawRegion ();

  t1 = image.TextureLaws ("el", 2, 5);     m1 = t1.MeanImage (21, 21);
  t2 = image.TextureLaws ("es", 2, 5);     m2 = t2.MeanImage (21, 21);
  t3 = image.TextureLaws ("le", 2, 5);     m3 = t3.MeanImage (21, 21);

  m  = m1.Compose3 (m2, m3);

  Tuple Metric = "euclid";
  Tuple Radius = 20.0;
  Tuple MinNum = 5;
  Tuple NbrCha = 3;

  HRegion empty;
  Tuple cen, t;

  Radius = testreg.LearnNdimNorm (empty, m, Metric, Radius,
                                  MinNum, NbrCha, &cen, &t);
  Tuple RegMod = "multiple";

  HRegionArray reg = m.ClassNdimNorm (Metric, RegMod, Radius, cen, NbrCha);

  w.SetColored (12);
  reg.Display (w);
  cout << "Result of classification" << endl;
  return (0);
}

Complexity

Let N be the number of clusters and A be the area of the input region. Then the runtime complexity is O(N,A).


Result

class_ndim_norm returns 2 (H_MSG_TRUE) if all parameters are correct. The behavior with respect to the input images and output regions can be determined by setting the values of the flags 'no_object_result', 'empty_region_result', and 'store_empty_region' with set_system. If necessary, an exception is raised.


Parallelization Information

class_ndim_norm is reentrant and automatically parallelized (on tuple level).


Possible Predecessors

learn_ndim_norm, compose2, compose3, compose4, compose5, compose6, compose7


Possible Successors

connection, select_shape, reduce_domain, select_gray


Alternatives

class_ndim_box, class_2dim_sup, class_2dim_unsup


Module

Foundation



Copyright © 1996-2008 MVTec Software GmbH